Complexity and prediction VI: a model predicts the frequency and severity of interstate wars, ‘a profound mystery for which we have no explanation’

Complexity and prediction VI: a model predicts the frequency and severity of interstate wars, ‘a profound mystery for which we have no explanation’

I spend a lot of time these days reading papers on prediction from different fields looking for connections between methods.

This is an interesting paper: On the frequency and severity of interstate wars, 2019.

Lewis Fry Richardson argued that the frequency and severity of deadly conflicts of all kinds, from homicides to interstate wars and everything in between, followed universal statistical patterns: their frequency followed a simple Poisson arrival process and their severity followed a simple power-law distribution. Although his methods and data in the mid-20th century were neither rigorous nor comprehensive, his insights about violent conflicts have endured. In this chapter, using modern statistical methods and data, we show that Richardson’s original claims appear largely correct, with a few caveats. These facts place important constraints on our understanding of the underlying mechanisms that produce individual wars and periods of peace, and shed light on the persistent debate about trends in conflict…

Fifty years or more of relatively few large wars is thus entirely typical, given the empirical distribution of war sizes, and observing a long period of peace is not necessarily evidence of a changing likelihood for large wars [12, 13]. Even periods comparable to the great violence of the World Wars are not statistically rare under Richardson’s model… Under the model, the 100-year probability of at least one war with 16, 634, 907 or more battle deaths (the size of the Second World War) is 0.43 ± 0.01, implying about one such war per 161 years, on average…

[Simulation to test how unusual the long peace without very big war since 1945 is…] It is not until 100 years into the future [from 2003] that the long peace becomes statistically distinguishable from a large but random fluctuation in an otherwise stationary process… Our modeling effort here cannot rule out the existence of a change in the rules that generate interstate conflicts, but if it occurred, it cannot have been a dramatic shift. The results here are entirely consistent with other evidence of genuine changes in the international system, but they constrain the extent to which such changes could have genuinely impacted the global production of interstate wars…

The agreement between the historical record of inter- state wars and Richardson’s simple model of their frequency and severity is truly remarkable, and it stands as a testament to Richardson’s lasting contribution to the study of violent political conflict…

The lower portion of the distribution is slightly more curved than expected for a simple power law, which suggests potential differences in the processes that generate wars above and below this threshold [7k deaths].

How can it be possible that the frequency and severity of interstate wars are so consistent with a stationary model, despite the enormous changes and obviously non-stationary dynamics in human population, in the number of recognized states, in commerce, communication, public health, and technology, and even in the modes of war itself? The fact that the absolute number and sizes of wars are plausibly stable in the face of these changes is a profound mystery for which we have no explanation.

Our results here indicate that the post-war efforts to reduce the likelihood of large inter- state wars have not yet changed the observed statistics enough to tell if they are working.

The long peace pattern is sometimes described only in terms of peace among largely European powers, who fell into a peaceful configuration after the great violence for well understood reasons. In parallel, however, conflicts in other parts of the world, most notably Africa, the Middle East, and Southeast Asia, have became more common, and these may have statistically balanced the books globally against the decrease in frequency in the West, and may even be causally dependent on the drivers of European war and then peace.’

 

Please leave comments below and links to other work that may throw light on this…

Further reading

Complexity, ‘fog and moonlight’, prediction, and politics I: Introduction (July 2014).

Complexity and prediction II: controlled skids and immune systems (September 2014). Why is the world so hard to predict? Nonlinearity and Bismarck. How to humans adapt? The difference between science and political predictions. Feedback and emergent properties. Decentralised problem-solving in the immune system and ant colonies.

Complexity and prediction III: von Neumann and economics as a science (September 2014). This examines von Neumann’s views on the proper role of mathematics in economics and some history of game theory.

Complexity and prediction IV: The birth of computational thinking (September 2014). Leibniz and computational thinking. The first computers. Punched cards. Optical data networks. Wireless. The state of the field by the time of Turing’s 1936 paper… These sketches may help in trying to understand 1) contemporary discussions about complex systems in general, 2) new tools that are being developed, and 3) contemporary debates concerning scientific, technological, economic, and political issues which depend on computers – from algorithmic high frequency trading to ‘agent based models’, machine intelligence, and military robots.

Complexity and prediction V: The crisis of mathematical paradoxes, Gödel, Turing and the basis of computing (June 2016). The paper concerns a fascinating episode in the history of ideas that saw the most esoteric and unpractical field, mathematical logic, spawn a revolutionary technology, the modern computer. NB. a great lesson to science funders: it’s a great mistake to cut funding on theory and assume that you’ll get more bang for buck from ‘applications’.

Effective action #4a: ‘Expertise’ from fighting and physics to economics, politics and government

‘We learn most when we have the most to lose.’ Michael Nielsen, author of the brilliant book Reinventing Discovery.

‘There isn’t one novel thought in all of how Berkshire [Hathaway] is run. It’s all about … exploiting unrecognized simplicities…Warren [Buffett] and I aren’t prodigies.We can’t play chess blindfolded or be concert pianists. But the results are prodigious, because we have a temperamental advantage that more than compensates for a lack of IQ points.’ Charlie Munger,Warren Buffett’s partner.

I’m going to do a series of blogs on the differences between fields dominated by real expertise (like fighting and physics) and fields dominated by bogus expertise (like macroeconomic forecasting, politics/punditry, active fund management).

Fundamental to real expertise is 1) whether the informational structure of the environment is sufficiently regular that it’s possible to make good predictions and 2) does it allow high quality feedback and therefore error-correction. Physics and fighting: Yes. Predicting recessions, forex trading and politics: not so much. I’ll look at studies comparing expert performance in different fields and the superior performance of relatively very simple models over human experts in many fields.

This is useful background to consider a question I spend a lot of time thinking about: how to integrate a) ancient insights and modern case studies about high performance with b) new technology and tools in order to improve the quality of individual, team, and institutional decision-making in politics and government.

I think that fixing the deepest problems of politics and government requires a more general and abstract approach to principles of effective action than is usually considered in political discussion and such an approach could see solutions to specific problems almost magically appear, just as you see happen in a very small number of organisations — e.g Mueller’s Apollo program (man on the moon), PARC (interactive computing), Berkshire Hathaway (most successful investors in history), all of which have delivered what seems almost magical performance because they embody a few simple, powerful, but largely unrecognised principles. There is no ‘solution’ to the fundamental human problem of decision-making amid extreme complexity and uncertainty but we know a) there are ways to do things much better and b) governments mostly ignore them, so there is extremely valuable low-hanging fruit if, but it’s a big if, we can partially overcome the huge meta-problem that governments tend to resist the institutional changes needed to become a learning system.

This blog presents some basic background ideas and examples…

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Extreme sports: fast feedback = real expertise 

In the 1980s and early 1990s, there was an interesting case study in how useful new knowledge jumped from a tiny isolated group to the general population with big effects on performance in a community. Expertise in Brazilian jiu-jitsu was taken from Brazil to southern California by the Gracie family. There were many sceptics but they vanished rapidly because the Gracies were empiricists. They issued ‘the Gracie challenge’.

All sorts of tough guys, trained in all sorts of ways, were invited to come to their garage/academy in Los Angeles to fight one of the Gracies or their trainees. Very quickly it became obvious that the Gracie training system was revolutionary and they were real experts because they always won. There was very fast and clear feedback on predictions. Gracie jiujitsu quickly jumped from an LA garage to TV. At the televised UFC 1 event in 1993 Royce Gracie defeated everyone and a multi-billion dollar business was born.

People could see how training in this new skill could transform performance. Unarmed combat changed across the world. Disciplines other than jiu jitsu have had to make a choice: either isolate themselves and not compete with jiu jitsu or learn from it. If interested watch the first twenty minutes of this documentary (via professor Steve Hsu, physicist, amateur jiu jitsu practitioner, and predictive genomics expert).

Video: Jiu Jitsu comes to Southern California

Royce Gracie, UFC 1 1993 

Screenshot 2018-05-22 10.41.20

 

Flow, deep in the zone

Another field where there is clear expertise is extreme skiing and snowboarding. One of the leading pioneers, Jeremy Jones, describes how he rides ‘spines’ hurtling down the side of mountains:

‘The snow is so deep you need to use your arms and chest to swim, and your legs to ride. They also collapse underfoot, so you’re riding mini-avalanches and dodging slough slides. Spines have blind rollovers, so you can’t see below. Or to the side. Every time the midline is crossed, it’s a leap into the abyss. Plus, there’s no way to stop and every move is amplified by complicated forces. A tiny hop can easily become a twenty-foot ollie. It’s the absolute edge of chaos. But the easiest way to live in the moment is to put yourself in a situation where there’s no other choice. Spines demand that, they hurl you deep into the zone.’ Emphasis added.

Video: Snowboarder Jeremy Jones

What Jones calls ‘the zone’ is also known as ‘flow‘ — a particular mental state, triggered by environmental cues, that brings greatly enhanced performance. It is the object of study in extreme sports and by the military and intelligence services: for example DARPA is researching whether stimulating the brain can trigger ‘flow’ in snipers.

Flow — or control on ‘the edge of chaos’ where ‘every move is amplified by complicated forces’ — comes from training in which people learn from very rapid feedback between predictions and reality. In ‘flow’, brains very rapidly and accurately process environmental signals and generate hypothetical scenarios/predictions and possible solutions based on experience and training. Jones’s performance is inseparable from developing this fingertip feeling. Similarly, an expert fireman feels the glow of heat on his face in a slightly odd way and runs out of the building just before it collapses without consciously knowing why he did it: his intuition has been trained to learn from feedback and make predictions. Experts operating in ‘flow’ do not follow what is sometimes called the ‘rational model’ of decision-making in which they sequentially interrogate different options — they pattern-match solutions extremely quickly based on experience and intuition.

The video below shows extreme expertise in a state of ‘flow’ with feedback on predictions within milliseconds. This legendary ride is so famous not because of the size of the wave but its odd, and dangerous, nature. If you watch carefully you will see what a true expert in ‘flow’ can do: after committing to the wave Hamilton suddenly realises that unless he reaches back with the opposite hand to normal and drags it against the wall of water behind him, he will get sucked up the wave and might die. (This wave had killed someone a few weeks earlier.) Years of practice and feedback honed the intuition that, when faced with a very dangerous and fast moving problem, almost instantly (few seconds maximum) pattern-matched an innovative solution.

Video: surfer Laird Hamilton in one of the greatest ever rides

 

The faster the feedback cycle, the more likely you are to develop a qualitative improvement in speed that destroys an opponent’s decision-making cycle. If you can reorient yourself faster to the ever-changing environment than your opponent, then you operate inside their ‘OODA loop’ (Observe-Orient-Decide-Act) and the opponent’s performance can quickly degrade and collapse.

This lesson is vital in politics. You can read it in Sun Tzu and see it with Alexander the Great. Everybody can read such lessons and most people will nod along. But it is very hard to apply because most political/government organisations are programmed by their incentives to prioritise seniority, process and prestige over high performance and this slows and degrades decisions. Most organisations don’t do it. Further, political organisations tend to make too slowly those decisions that should be fast and too quickly those decisions that should be slow — they are simultaneously both too sluggish and too impetuous, which closes off favourable branching histories of the future.

Video: Boxer Floyd Mayweather, best fighter of his generation and one of the quickest and best defensive fighters ever

The most extreme example in extreme sports is probably ‘free soloing’ — climbing mountains without ropes where one mistake means instant death. If you want to see an example of genuine expertise and the value of fast feedback then watch Alex Honnold.

Video: Alex Honnold ‘free solos’ El Sendero Luminoso (terrifying)

Music is similar to sport. There is very fast feedback, learning, and a clear hierarchy of expertise.

Video: Glenn Gould playing the Goldberg Variations (slow version)

Our culture treats expertise/high performance in fields like sport and music very differently to maths/science education and politics/government. As Alan Kay observes, music and sport expertise is embedded in the broader culture. Millions of children spend large amounts of time practising hard skills. Attacks on them as ‘elitist’ don’t get the same damaging purchase as in other fields and the public don’t mind about elite selection for sports teams or orchestras.

‘Two ideas about this are that a) these [sport/music] are activities in which the basic act can be seen clearly from the first, and b) are already part of the larger culture. There are levels that can be seen to be inclusive starting with modest skills. I think a very large problem for the learning of both science and math is just how invisible are their processes, especially in schools.’ Kay 

When it comes to maths and science education, the powers-that-be (in America and Britain) try very hard and mostly successfully to ignore the question: where are critical thresholds for valuable skills that develop true expertise. This is even more a problem with the concept of ‘thinking rationally’, for which some basic logic, probability, and understanding of scientific reasoning is a foundation. Discussion of politics and government almost totally ignores the concept of training people to update their opinions in response to new evidence — i.e adapt to feedback. The ‘rationalist community’ — people like Scott Alexander who wrote this fantastic essay (Moloch) about why so much goes wrong, or the recent essays by Eliezer Yudkowsky — are ignored at the apex of power. I will return to the subject of how to create new education and training programmes for elite decision-makers. It is a good time for UK universities to innovate in this field, as places like Stanford are already doing. Instead of training people like Cameron and Adonis to bluff with PPE, we need courses that combine rational thinking with practical training in managing complex projects. We need people who practice really hard making predictions in ways we know work well (cf. Tetlock) then update in response to errors.

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A more general/abstract approach to reforming government

If we want to get much higher performance in government, then we need to think rigorously about: the selection of people and teams, their education and training, their tools, and the institutions (incentives and so on) that surround and shape them.

Almost all analysis of politics and government considers relatively surface phenomena. For example, the media briefly blasts headlines about Carillion’s collapse or our comical aircraft carriers but there is almost no consideration of the deep reasons for such failures and therefore nothing tends to happen — the media caravan moves on and the officials and ministers keep failing in the same ways. This is why, for example, the predicted abject failure of the traditional Westminster machinery to cope with Brexit negotiations has not led to self-examination and learning but, instead, mostly to a visible determination across both sides of the Brexit divide in SW1 to double down on long-held delusions.

Progress requires attacking the ‘system of systems’ problem at the right ‘level’. Attacking the problems directly — let’s improve policy X and Y, let’s swap ‘incompetent’ A for ‘competent’ B — cannot touch the core problems, particularly the hardest meta-problem that government systems bitterly fight improvement. Solving the explicit surface problems of politics and government is best approached by a more general focus on applying abstract principles of effective action. We need to surround relatively specific problems with a more general approach. Attack at the right level will see specific solutions automatically ‘pop out’ of the system. One of the most powerful simplicities in all conflict (almost always unrecognised) is: ‘winning without fighting is the highest form of war’. If we approach the problem of government performance at the right level of generality then we have a chance to solve specific problems ‘without fighting’ — or, rather, without fighting nearly so much and the fighting will be more fruitful.

This is not a theoretical argument. If you look carefully at ancient texts and modern case studies, you see that applying a small number of very simple, powerful, but largely unrecognised principles (that are very hard for organisations to operationalise) can produce extremely surprising results.

We have no alternative to trying. Without fundamental changes to government, we will lose our hourly game of Russian roulette with technological progress.

‘The combination of physics and politics could render the surface of the earth uninhabitable… [T]he ever accelerating progress of technology and changes in the mode of human life … gives the appearance of approaching some essential singularity in the history of the race beyond which human affairs, as we know them, could not continue.’ John von Neumann

As Steve Hsu says: Pessimism of the Intellect, Optimism of the Will.


Ps. There is an interesting connection between the nature of counterfactual reasoning in the fast-moving world of extreme sports and the theoretical paper I posted yesterday on state-of-the-art AI. The human ability to interrogate stored representations of their environment with counter-factual questions is fundamental to the nature of intelligence and developing expertise in physical and mental skills. It is, for now, absent in machines.

State-of-the-art in AI #1: causality, hypotheticals, and robots with free will & capacity for evil (UPDATED)

Judea Pearl is one of the most important scholars in the field of causal reasoning. His book Causality is the leading textbook in the field.

This blog has two short parts — a paper he wrote a few months ago and an interview he gave a few days ago.

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He recently wrote a very interesting (to the very limited extent I understand it) short paper about the limits of state-of-the-art AI systems using ‘deep learning’ neural networks — such as the AlphaGo system which recently conquered the game of GO and AlphaZero which blew past centuries of human knowledge of chess in 24 hours — and how these systems could be improved.

The human ability to interrogate stored representations of their environment with counter-factual questions is fundamental and, for now, absent in machines. (All bold added my me.)

‘If we examine the information that drives machine learning today, we find that it is almost entirely statistical. In other words, learning machines improve their performance by optimizing parameters over a stream of sensory inputs received from the environment. It is a slow process, analogous in many respects to the evolutionary survival-of-the-fittest process that explains how species like eagles and snakes have developed superb vision systems over millions of years. It cannot explain however the super-evolutionary process that enabled humans to build eyeglasses and telescopes over barely one thousand years. What humans possessed that other species lacked was a mental representation, a blue-print of their environment which they could manipulate at will to imagine alternative hypothetical environments for planning and learning…

‘[T]he decisive ingredient that gave our homo sapiens ancestors the ability to achieve global dominion, about 40,000 years ago, was their ability to sketch and store a representation of their environment, interrogate that representation, distort it by mental acts of imagination and finally answer “What if?” kind of questions. Examples are interventional questions: “What if I act?” and retrospective or explanatory questions: “What if I had acted differently?” No learning machine in operation today can answer such questions about actions not taken before. Moreover, most learning machines today do not utilize a representation from which such questions can be answered.

‘We postulate that the major impediment to achieving accelerated learning speeds as well as human level performance can be overcome by removing these barriers and equipping learning machines with causal reasoning tools. This postulate would have been speculative twenty years ago, prior to the mathematization of counterfactuals. Not so today. Advances in graphical and structural models have made counterfactuals computationally manageable and thus rendered meta-statistical learning worthy of serious exploration

Figure: the ladder of causation

Screenshot 2018-03-12 11.22.54

‘An extremely useful insight unveiled by the logic of causal reasoning is the existence of a sharp classification of causal information, in terms of the kind of questions that each class is capable of answering. The classification forms a 3-level hierarchy in the sense that questions at level i (i = 1, 2, 3) can only be answered if information from level j (j ≥ i) is available. [See figure]… Counterfactuals are placed at the top of the hierarchy because they subsume interventional and associational questions. If we have a model that can answer counterfactual queries, we can also answer questions about interventions and observations… The translation does not work in the opposite direction… No counterfactual question involving retrospection can be answered from purely interventional information, such as that acquired from controlled experiments; we cannot re-run an experiment on subjects who were treated with a drug and see how they behave had then not given the drug. The hierarchy is therefore directional, with the top level being the most powerful one. Counterfactuals are the building blocks of scientific thinking as well as legal and moral reasoning…

‘This hierarchy, and the formal restrictions it entails, explains why statistics-based machine learning systems are prevented from reasoning about actions, experiments and explanations. It also suggests what external information need to be provided to, or assumed by, a learning system, and in what format, in order to circumvent those restrictions

[He describes his approach to giving machines the ability to reason in more advanced ways (‘intent-specific optimization’) than standard approaches and the success of some experiments on real problems.]

[T]he value of intent-base optimization … contains … the key by which counterfactual information can be extracted out of experiments. The key is to have agents who pause, deliberate, and then act, possibly contrary to their original intent. The ability to record the discrepancy between outcomes resulting from enacting one’s intent and those resulting from acting after a deliberative pause, provides the information that renders counterfactuals estimable. It is this information that enables us to cross the barrier between layer 2 and layer 3 of the causal hierarchy… Every child undergoes experiences where he/she pauses and thinks: Can I do better? If mental records are kept of those experiences, we have experimental semantic to counterfactual thinking in the form of regret sentences “I could have done better.” The practical implications of this new semantics is worth exploring.’

The paper is here: http://web.cs.ucla.edu/~kaoru/theoretical-impediments.pdf.

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By chance this evening I came across this interview with Pearl in which he discuses some of the ideas above less formally, HERE.

‘The problems that emerged in the early 1980s were of a predictive or diagnostic nature. A doctor looks at a bunch of symptoms from a patient and wants to come up with the probability that the patient has malaria or some other disease. We wanted automatic systems, expert systems, to be able to replace the professional — whether a doctor, or an explorer for minerals, or some other kind of paid expert. So at that point I came up with the idea of doing it probabilistically.

‘Unfortunately, standard probability calculations required exponential space and exponential time. I came up with a scheme called Bayesian networks that required polynomial time and was also quite transparent.

‘[A]s soon as we developed tools that enabled machines to reason with uncertainty, I left the arena to pursue a more challenging task: reasoning with cause and effect.

‘All the machine-learning work that we see today is conducted in diagnostic mode — say, labeling objects as “cat” or “tiger.” They don’t care about intervention; they just want to recognize an object and to predict how it’s going to evolve in time.

‘I felt an apostate when I developed powerful tools for prediction and diagnosis knowing already that this is merely the tip of human intelligence. If we want machines to reason about interventions (“What if we ban cigarettes?”) and introspection (“What if I had finished high school?”), we must invoke causal models. Associations are not enough — and this is a mathematical fact, not opinion.

‘As much as I look into what’s being done with deep learning, I see they’re all stuck there on the level of associations. Curve fitting. That sounds like sacrilege, to say that all the impressive achievements of deep learning amount to just fitting a curve to data. From the point of view of the mathematical hierarchy, no matter how skillfully you manipulate the data and what you read into the data when you manipulate it, it’s still a curve-fitting exercise, albeit complex and nontrivial.

‘I’m very impressed, because we did not expect that so many problems could be solved by pure curve fitting. It turns out they can. But I’m asking about the future — what next? Can you have a robot scientist that would plan an experiment and find new answers to pending scientific questions? That’s the next step. We also want to conduct some communication with a machine that is meaningful, and meaningful means matching our intuition.

‘If a machine does not have a model of reality, you cannot expect the machine to behave intelligently in that reality. The first step, one that will take place in maybe 10 years, is that conceptual models of reality will be programmed by humans. The next step will be that machines will postulate such models on their own and will verify and refine them based on empirical evidence. That is what happened to science; we started with a geocentric model, with circles and epicycles, and ended up with a heliocentric model with its ellipses.

We’re going to have robots with free will, absolutely. We have to understand how to program them and what we gain out of it. For some reason, evolution has found this sensation of free will to be computationally desirable… Evidently, it serves some computational function.

‘I think the first evidence will be if robots start communicating with each other counterfactually, like “You should have done better.” If a team of robots playing soccer starts to communicate in this language, then we’ll know that they have a sensation of free will. “You should have passed me the ball — I was waiting for you and you didn’t!” “You should have” means you could have controlled whatever urges made you do what you did, and you didn’t.

[When will robots be evil?] When it appears that the robot follows the advice of some software components and not others, when the robot ignores the advice of other components that are maintaining norms of behavior that have been programmed into them or are expected to be there on the basis of past learning. And the robot stops following them.’

Please leave links to significant critiques of this paper or work that has developed the ideas in it.

If interested in the pre-history of the computer age and internet, this paper explores it.

Unrecognised simplicities of effective action #1: expertise and a quadrillion dollar business

‘The combination of physics and politics could render the surface of the earth uninhabitable.’ John von Neumann.

Introduction

This series of blogs considers:

  • the difference between fields with genuine expertise, such as fighting and physics, and fields dominated by bogus expertise, such as politics and economic forecasting;
  • the big big problem we face – the world is ‘undersized and underorganised’ because of a collision between four forces: 1) our technological civilisation is inherently fragile and vulnerable to shocks, 2) the knowledge it generates is inherently dangerous, 3) our evolved instincts predispose us to aggression and misunderstanding, and 4) there is a profound mismatch between the scale and speed of destruction our knowledge can cause and the quality of individual and institutional decision-making in ‘mission critical’ institutions – our institutions are similar to those that failed so spectacularly in summer 1914 yet they face crises moving at least ~103 times faster and involving ~106 times more destructive power able to kill ~1010 people;
  • what classic texts and case studies suggest about the unrecognised simplicities of effective action to improve the selection, education, training, and management of vital decision-makers to improve dramatically, reliably, and quantifiably the quality of individual and institutional decisions (particularly 1) the ability to make accurate predictions and b) the quality of feedback);
  • how we can change incentives to aim a much bigger fraction of the most able people at the most important problems;
  • what tools and technologies can help decision-makers cope with complexity.

[I’ve tweaked a couple of things in response to this blog by physicist Steve Hsu.]

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Summary of the big big problem

The investor Peter Thiel (founder of PayPal and Palantir, early investor in Facebook) asks people in job interviews: what billion (109) dollar business is nobody building? The most successful investor in world history, Warren Buffett, illustrated what a quadrillion (1015) dollar business might look like in his 50th anniversary letter to Berkshire Hathaway investors.

‘There is, however, one clear, present and enduring danger to Berkshire against which Charlie and I are powerless. That threat to Berkshire is also the major threat our citizenry faces: a “successful” … cyber, biological, nuclear or chemical attack on the United States… The probability of such mass destruction in any given year is likely very small… Nevertheless, what’s a small probability in a short period approaches certainty in the longer run. (If there is only one chance in thirty of an event occurring in a given year, the likelihood of it occurring at least once in a century is 96.6%.) The added bad news is that there will forever be people and organizations and perhaps even nations that would like to inflict maximum damage on our country. Their means of doing so have increased exponentially during my lifetime. “Innovation” has its dark side.

‘There is no way for American corporations or their investors to shed this risk. If an event occurs in the U.S. that leads to mass devastation, the value of all equity investments will almost certainly be decimated.

‘No one knows what “the day after” will look like. I think, however, that Einstein’s 1949 appraisal remains apt: “I know not with what weapons World War III will be fought, but World War IV will be fought with sticks and stones.”’

Politics is profoundly nonlinear. (I have written a series of blogs about complexity and prediction HERE which are useful background for those interested.) Changing the course of European history via the referendum only involved about 10 crucial people controlling ~£107  while its effects over ten years could be on the scale of ~108 – 10people and ~£1012: like many episodes in history the resources put into it are extremely nonlinear in relation to the potential branching histories it creates. Errors dealing with Germany in 1914 and 1939 were costly on the scale of ~100,000,000 (108) lives. If we carry on with normal human history – that is, international relations defined as out-groups competing violently – and combine this with modern technology then it is extremely likely that we will have a disaster on the scale of billions (109) or even all humans (~1010). The ultimate disaster would kill about 100 times more people than our failure with Germany. Our destructive power is already much more than 100 times greater than it was then: nuclear weapons increased destructiveness by roughly a factor of a million.

Even if we dodge this particular bullet there are many others lurking. New genetic engineering techniques such as CRISPR allow radical possibilities for re-engineering organisms including humans in ways thought of as science fiction only a decade ago. We will soon be able to remake human nature itself. CRISPR-enabled ‘gene drives’ enable us to make changes to the germ-line of organisms permanent such that changes spread through the entire wild population, including making species extinct on demand. Unlike nuclear weapons such technologies are not complex, expensive, and able to be kept secret for a long time. The world’s leading experts predict that people will be making them cheaply at home soon – perhaps they already are. These developments have been driven by exponential progress much faster than Moore’s Law reducing the cost of DNA sequencing per genome from ~$108 to ~$10in roughly 15 years.

screenshot-2017-01-16-12-24-13

It is already practically possible to deploy a cheap, autonomous, and anonymous drone with facial-recognition software and a one gram shaped-charge to identify a relevant face and blow it up. Military logic is driving autonomy. For example, 1) the explosion in the volume of drone surveillance video (from 71 hours in 2004 to 300,000 hours in 2011 to millions of hours now) requires automated analysis, and 2) jamming and spoofing of drones strongly incentivise a push for autonomy. It is unlikely that promises to ‘keep humans in the loop’ will be kept. It is likely that state and non-state actors will deploy low-cost drone swarms using machine learning to automate the ‘find-fix-finish’ cycle now controlled by humans. (See HERE for a video just released for one such program and imagine the capability when they carry their own communication and logistics network with them.)

In the medium-term, many billions are being spent on finding the secrets of general intelligence. We know this secret is encoded somewhere in the roughly 125 million ‘bits’ of information that is the rough difference between the genome that produces the human brain and the genome that produces the chimp brain. This search space is remarkably small – the equivalent of just 25 million English words or 30 copies of the King James Bible. There is no fundamental barrier to decoding this information and it is possible that the ultimate secret could be described relatively simply (cf. this great essay by physicist Michael Nielsen). One of the world’s leading experts has told me they think a large proportion of this problem could be solved in about a decade with a few tens of billions and something like an Apollo programme level of determination.

Not only is our destructive and disruptive power still getting bigger quickly – it is also getting cheaper and faster every year. The change in speed adds another dimension to the problem. In the period between the Archduke’s murder and the outbreak of World War I a month later it is striking how general failures of individuals and institutions were compounded by the way in which events moved much faster than the ‘mission critical’ institutions could cope with such that soon everyone was behind the pace, telegrams were read in the wrong order and so on. The crisis leading to World War I was about 30 days from the assassination to the start of general war – about 700 hours. The timescale for deciding what to do between receiving a warning of nuclear missile launch and deciding to launch yourself is less than half an hour and the President’s decision time is less than this, maybe just minutes. This is a speedup factor of at least 103.

Economic crises already occur far faster than human brains can cope with. The financial system has made a transition from people shouting at each other to a a system dominated by high frequency ‘algorithmic trading’ (HFT), i.e. machine intelligence applied to robot trading with vast volumes traded on a global spatial scale and a microsecond (10-6) temporal scale far beyond the monitoring, understanding, or control of regulators and politicians. There is even competition for computer trading bases in specific locations based on calculations of Special Relativity as the speed of light becomes a factor in minimising trade delays (cf. Relativistic statistical arbitrage, Wissner-Gross). ‘The Flash Crash’ of 9 May 2010 saw the Dow lose hundreds of points in minutes. Mini ‘flash crashes’ now blow up and die out faster than humans can notice. Given our institutions cannot cope with economic decisions made at ‘human speed’, a fortiori they cannot cope with decisions made at ‘robot speed’. There is scope for worse disasters than 2008 which would further damage the moral credibility of decentralised markets and provide huge chances for extremist political entrepreneurs to exploit. (* See endnote.)

What about the individuals and institutions that are supposed to cope with all this?

Our brains have not evolved much in thousands of years and are subject to all sorts of constraints including evolved heuristics that lead to misunderstanding, delusion, and violence particularly under pressure. There is a terrible mismatch between the sort of people that routinely dominate mission critical political institutions and the sort of people we need: high-ish IQ (we need more people >145 (+3SD) while almost everybody important is between 115-130 (+1 or 2SD)), a robust toolkit for not fooling yourself including quantitative problem-solving (almost totally absent at the apex of relevant institutions), determination, management skills, relevant experience, and ethics. While our ancestor chiefs at least had some intuitive feel for important variables like agriculture and cavalry our contemporary chiefs (and those in the media responsible for scrutiny of decisions) generally do not understand their equivalents, and are often less experienced in managing complex organisations than their predecessors.

The national institutions we have to deal with such crises are pretty similar to those that failed so spectacularly in summer 1914 yet they face crises moving at least ~103 times faster and involving ~106 times more destructive power able to kill ~1010 people. The international institutions developed post-1945 (UN, EU etc) contribute little to solving the biggest problems and in many ways make them worse. These institutions fail constantly and do not  – cannot – learn much.

If we keep having crises like we have experienced over the past century then this combination of problems pushes the probability of catastrophe towards ‘overwhelmingly likely’.

*

What Is To be Done? There’s plenty of room at the top

‘In a knowledge-rich world, progress does not lie in the direction of reading information faster, writing it faster, and storing more of it. Progress lies in the direction of extracting and exploiting the patterns of the world… And that progress will depend on … our ability to devise better and more powerful thinking programs for man and machine.’ Herbert Simon, Designing Organizations for an Information-rich World, 1969.

‘Fascinating that the same problems recur time after time, in almost every program, and that the management of the program, whether it happened to be government or industry, continues to avoid reality.’ George Mueller, pioneer of ‘systems engineering’ and ‘systems management’ and the man most responsible for the success of the 1969 moon landing.

Somehow the world has to make a series of extremely traumatic and dangerous transitions over the next 20 years. The main transition needed is:

Embed reliably the unrecognised simplicities of high performance teams (HPTs), including personnel selection and training, in ‘mission critical’ institutions while simultaneously developing a focused project that radically improves the prospects for international cooperation and new forms of political organisation beyond competing nation states.

Big progress on this problem would automatically and for free bring big progress on other big problems. It could improve (even save) billions of lives and save a quadrillion dollars (~$1015). If we avoid disasters then the error-correcting institutions of markets and science will, patchily, spread peace, prosperity, and learning. We will make big improvements with public services and other aspects of ‘normal’ government. We will have a healthier political culture in which representative institutions, markets serving the public (not looters), and international cooperation are stronger.

Can a big jump in performance – ‘better and more powerful thinking programs for man and machine’ – somehow be systematised?

Feynman once gave a talk titled ‘There’s plenty of room at the bottom’ about the huge performance improvements possible if we could learn to do engineering at the atomic scale – what is now called nanotechnology. There is also ‘plenty of room at the top’ of political structures for huge improvements in performance. As I explained recently, the victory of the Leave campaign owed more to the fundamental dysfunction of the British Establishment than it did to any brilliance from Vote Leave. Despite having the support of practically every force with power and money in the world (including the main broadcasters) and controlling the timing and legal regulation of the referendum, they blew it. This was good if you support Leave but just how easily the whole system could be taken down should be frightening for everybody .

Creating high performance teams is obviously hard but in what ways is it really hard? It is not hard in the same sense that some things are hard like discovering profound new mathematical knowledge. HPTs do not require profound new knowledge. We have been able to read the basic lessons in classics for over two thousand years. We can see relevant examples all around us of individuals and teams showing huge gains in effectiveness.

The real obstacle is not financial. The financial resources needed are remarkably low and the return on small investments could be incalculably vast. We could significantly improve the decisions of the most powerful 100 people in the UK or the world for less than a million dollars (~£106) and a decade-long project on a scale of just ~£107 could have dramatic effects.

The real obstacle is not a huge task of public persuasion – quite the opposite. A government that tried in a disciplined way to do this would attract huge public support. (I’ve polled some ideas and am confident about this.) Political parties are locked in a game that in trying to win in conventional ways leads to the public despising them. Ironically if a party (established or new) forgets this game and makes the public the target of extreme intelligent focus then it would not only make the world better but would trounce their opponents.

The real obstacle is not a need for breakthrough technologies though technology could help. As Colonel Boyd used to shout, ‘People, ideas, machines – in that order!’

The real obstacle is that although we can all learn and study HPTs it is extremely hard to put this learning to practical use and sustain it against all the forces of entropy that constantly operate to degrade high performance once the original people have gone. HPTs are episodic. They seem to come out of nowhere, shock people, then vanish with the rare individuals. People write about them and many talk about learning from them but in fact almost nobody ever learns from them – apart, perhaps, from those very rare people who did not need to learn – and nobody has found a method to embed this learning reliably and systematically in institutions that can maintain it. The Prussian General Staff remained operationally brilliant but in other ways went badly wrong after the death of the elder Moltke. When George Mueller left NASA it reverted to what it had been before he arrived – management chaos. All the best companies quickly go downhill after the departure of people like Bill Gates – even when such very able people have tried very very hard to avoid exactly this problem.

Charlie Munger, half of the most successful investment team in world history, has a great phrase he uses to explain their success that gets to the heart of this problem:

‘There isn’t one novel thought in all of how Berkshire [Hathaway] is run. It’s all about … exploiting unrecognized simplicities… It’s a community of like-minded people, and that makes most decisions into no-brainers. Warren [Buffett] and I aren’t prodigies. We can’t play chess blindfolded or be concert pianists. But the results are prodigious, because we have a temperamental advantage that more than compensates for a lack of IQ points.’

The simplicities that bring high performance in general, not just in investing, are largely unrecognised because they conflict with many evolved instincts and are therefore psychologically very hard to implement. The principles of the Buffett-Munger success are clear – they have even gone to great pains to explain them and what the rest of us should do – and the results are clear yet still almost nobody really listens to them and above average intelligence people instead constantly put their money into active fund management that is proved to destroy wealth every year!

Most people think they are already implementing these lessons and usually strongly reject the idea that they are not. This means that just explaining things is very unlikely to work:

‘I’d say the history that Charlie [Munger] and I have had of persuading decent, intelligent people who we thought were doing unintelligent things to change their course of action has been poor.’ Buffett.

Even more worrying, it is extremely hard to take over organisations that are not run right and make them excellent.

‘We really don’t believe in buying into organisations to change them.’ Buffett.

If people won’t listen to the world’s most successful investor in history on his own subject, and even he finds it too hard to take over failing businesses and turn them around, how likely is it that politicians and officials incentivised to keep things as they are will listen to ideas about how to do things better? How likely is it that a team can take over broken government institutions and make them dramatically better in a way that outlasts the people who do it? Bureaucracies are extraordinarily resistant to learning. Even after the debacles of 9/11 and the Iraq War, costing many lives and trillions of dollars, and even after the 2008 Crash, the security and financial bureaucracies in America and Europe are essentially the same and operate on the same principles.

Buffett’s success is partly due to his discipline in sticking within what he and Munger call their ‘circle of competence’. Within this circle they have proved the wisdom of avoiding trying to persuade people to change their minds and avoiding trying to fix broken institutions.

This option is not available in politics. The Enlightenment and the scientific revolution give us no choice but to try to persuade people and try to fix or replace broken institutions. In general ‘it is better to undertake revolution than undergo it’. How might we go about it? What can people who do not have any significant power inside the system do? What international projects are most likely to spark the sort of big changes in attitude we urgently need?

This is the first of a series. I will keep it separate from the series on the EU referendum though it is connected in the sense that I spent a year on the referendum in the belief that winning it was a necessary though not sufficient condition for Britain to play a part in improving the quality of government dramatically and improving the probability of avoiding the disasters that will happen if politics follows a normal path. I intended to implement some of these ideas in Downing Street if the Boris-Gove team had not blown up. The more I study this issue the more confident I am that dramatic improvements are possible and the more pessimistic I am that they will happen soon enough.

Please leave comments and corrections…

* A new transatlantic cable recently opened for financial trading. Its cost? £300 million. Its advantage? It shaves 2.6 milliseconds off the latency of financial trades. Innovative groups are discussing the application of military laser technology, unmanned drones circling the earth acting as routers, and even the use of neutrino communication (because neutrinos can go straight through the earth just as zillions pass through your body every second without colliding with its atoms) – cf. this recent survey in Nature.

On the referendum #21: Branching histories of the 2016 referendum and ‘the frogs before the storm’

‘Politics is gambling for high stakes with other people’s money… Politics is a job that can be compared with navigation in uncharted waters. One has no idea how the weather or the currents will be or what storms one is in for. In politics, there is the added fact that one is largely dependent on the decisions of others, decisions on which one was counting and which then do not materialise; one’s actions are never completely one’s own. And if the friends on whose support one is relying change their minds, which is something that one cannot vouch for, the whole plan miscarries… One’s enemies one can count on – but one’s friends!’ Bismarck.

‘The most important thing is not to fool yourself and you are the easiest person to fool.’ Feynman. 

‘He lies like an eyewitness.’ Russian proverb.

In January 2014 I left the Department for Education and spent the next 18 months away from politics. A few days after the 2015 election I wrote a blog about Michael Gove’s new job touching on the referendum. When I wrote it I assumed I would carry on studying and would not be involved in it. About ten days later I was asked by an assortment of MPs, rich businessmen, and campaigners including Matthew Elliott to help put together an organisation that could fight the referendum. I was very reluctant and prevaricated but ended up agreeing. I left my happy life away from SW1 and spent eight weeks biking around London persuading people to take what was likely to be a car crash career decision – to quit their jobs and join a low probability proposition: hacking the political system to win a referendum against almost every force with power and money in politics. In September we had an office, in October ‘Vote Leave’ went public, in April we were designated the official campaign, 10 weeks later we won.

Why and how? The first draft of history was written in the days and weeks after the 23 June and the second draft has appeared over the past few weeks in the form of a handful of books. There is no competition between them. Shipman’s is by far the best and he is the only one to have spoken to key people. I will review it soon. One of his few errors is to give me the credit for things that were done by others, often people in their twenties like Oliver Lewis, Jonny Suart, and Cleo Watson who, unknown outside the office, made extreme efforts and ran rings around supposed ‘experts’. His book has encouraged people to exaggerate greatly my importance.

I have been urged by some of those who worked on the campaign to write about it. I have avoided it, and interviews, for a few reasons (though I had to write one blog to explain that with the formal closing of VL we had made the first online canvassing software that really works in the UK freely available HERE). For months I couldn’t face it. The idea of writing about the referendum made me feel sick. It still does but a bit less.

For about a year I worked on this project every day often for 18 hours and sometimes awake almost constantly. Most of the ‘debate’ was moronic as political debate always is. Many hours of life I’m never getting back were spent dealing with abysmal infighting among dysfunctional egomaniacs while trying to build a ~£10 million startup in 10 months when very few powerful people thought the probability of victory was worth the risk of helping us. (Two rare heroes who put up a lot of their own money and supported the team were Peter Cruddas and Stuart Wheeler.) Many of those involved regarded their TV appearances as by far the most important aspect of the campaign. Many regarded Vote Leave as ‘the real enemy’.

It is hard to explain the depth of TV derangement that gobbles up SW1 souls. Much of politics involves very similar tragi-comic scenes re-created by some of the basic atoms of human nature – fear, self-interest and vanity. The years, characters, and contexts change, the atoms shuffle, but the stories are the same year after year, century after century. Delusions and vanity dominate ‘rationality’ and ‘public service’. Progress, when it comes, is driven by the error-correcting institutions of science and markets when political institutions limit the damage done by decision makers at the apex of centralised hierarchies. It rarely comes from those people, and, when it does, it is usually accidental or incidental to their motives.

Discussions about things like ‘why did X win/lose?’ are structured to be misleading and I could not face trying to untangle everything. There are strong psychological pressures that lead people to create post facto stories that seem to add up to ‘I always said X and X happened.’ Even if people do not think this at the start they rapidly construct psychologically appealing stories that overwrite memories. Many involved with this extraordinary episode feel the need to justify themselves and this means a lot of rewriting of history. I also kept no diary so I have no clear source for what I really thought other than some notes here and there. I already know from talking to people that my lousy memory has conflated episodes, tried to impose patterns that did not actually exist and so on – all the usual psychological issues. To counter all this in detail would require going through big databases of emails, printouts of appointment diaries, notebooks and so on, and even then I would rarely be able to reconstruct reliably what I thought. Life’s too short.

I’ve learned over the years that ‘rational discussion’ accomplishes almost nothing in politics, particularly with people better educated than average. Most educated people are not set up to listen or change their minds about politics, however sensible they are in other fields. But I have also learned that when you say or write something, although it has roughly zero effect on powerful/prestigious people or the immediate course of any ‘debate’, you are throwing seeds into a wind and are often happily surprised. A few years ago I wrote something that was almost entirely ignored in SW1 but someone at Harvard I’d never met read it. This ended up having a decisive effect on the referendum.

A warning. Politics is not a field which meets the two basic criteria for true expertise (see below). An effect of this is that arguments made by people who win are taken too seriously. People in my position often see victory as confirmation of ideas they had before victory but people often win for reasons they never understand or even despite their own efforts. Cameron’s win in 2015 was like this – he fooled himself about some of the reasons why he’d won and this error contributed to his errors on the referendum. Maybe Leave won regardless of or even despite my ideas. Maybe I’m fooling myself like  Cameron. Some of my arguments below have as good an empirical support as is possible in politics (i.e. not very good objectively) but most of them do not even have that. Also, it is clear that almost nobody agrees with me about some of my general ideas. It is more likely that I am wrong than 99% of people who work in this field professionally. Still, cognitive diversity is inherently good for political analysis so I’ll say what I think and others will judge if there’s anything to learn.

Apologies for the length but I didn’t have time to make it shorter. The next ones will be short.

*

Reality has branching histories, not ‘a big why’

Much political analysis revolves around competing simple stories based on one big factor such that, in retrospect, ‘it was always clear that immigration would trump economic interest / Cameron’s negotiation was never going to be enough / there is an unstoppable populist tide’, and so on. Alternatives are quickly thought to have been impossible (even if X argued the exact opposite repeatedly). The big event must have had an equally big single cause. Confirmation bias kicks in and evidence seeming to suggest that what actually happened would happen looms larger. People who are quite wrong quickly persuade themselves they were ‘mostly right’ and ‘had a strong feeling’ unlike, of course, the blind fools around them. Soon our actual history seems like the only way things could have played out. Brexit had to happen. Trump had to win.

You see these dynamics all the time in historical accounts. History tends to present the 1866 war between Prussia and Austria as almost inevitable but historians spend much less time on why Bismarck pulled back from war in 1865 and how he might have done the same in 1866 (actually he prepared the ground so he could do this and he kept the option open until the last minute). The same is true about 1870. When some generals tried to bounce him into a quick preventive war against Russia in the late 1880s he squashed them flat warning against tying the probability of a Great Power war to ‘the passions of sheep stealers’ in the Balkans (a lesson even more important today than then). If he had wanted a war, students would now be writing essays on why the Russo-German War of 1888 was ‘inevitable’. Many portray the war that broke out in August 1914 as ‘inevitable’ but many decisions in the preceding month could have derailed it, just as decisions derailed general war in previous Balkan crises. Few realise how lucky we were to avoid nuclear war during the Cuban Missile crisis (cf. Vasili Arkhipov) and other terrifying near-miss nuclear wars. The whole 20th Century history of two world wars and a nuclear Cold War might have been avoided if one of the assassination attempts on Bismarck had succeeded. If Cohen-Blind’s aim had been very slightly different in May 1866 when he fired five bullets at Bismarck, then the German states would certainly have evolved in a different way and it is quite plausible that there would have been no unified German army with its fearsome General Staff, no World War I, no Lenin and Hitler, and so on. The branching histories are forgotten and the actual branch taken, often because of some relatively trivial event casting a huge shadow (perhaps as small as a half-second delay by Cohen-Blind), seems overwhelmingly probable. This ought to, but does not, make us apply extreme intelligent focus to those areas that can go catastrophically wrong, like accidental nuclear war, to try to narrow the range of possible histories but instead most people in politics spend almost all their time on trivia.

We evolved to make sense of this nonlinear and unpredictable world with stories. These stories are often very powerful. On one hand the work of Kahneman et al on ‘irrationality’ has given an exaggerated impression. The fact that we did not evolve to think as natural Bayesians does not make us as ‘irrational’ as some argue. We evolved to avoid disasters where the probability of disaster X happening was unknowable but the outcome was fatal. Rationality is more than ‘Bayesian updating’. On the other hand our stories do often obscure the branching histories of reality and they remain the primary way in which history is told. The mathematical models that illuminate complex reality in the physical sciences do not help us much with history yet. Only recently has reliable data science begun to play an important role in politics.

Andrew Marr wrote recently about the referendum with a classic post facto ‘big event must be caused by one big factor’ story:

‘Connected to this is the big “why?”. I don’t think we voted to leave the EU because of clever tacticians or not-quite-clever-enough pollsters, or even because Johnson decided that one of his columns was better than another. I think we voted to leave because so many British people had been left behind economically and culturally for so long, and were furious about it; and because, from the 2008 financial crisis onwards, they had accumulated so much contempt for the political elites. In these circumstances any referendum narrows down to a single question: “Are you happy with the way things are?” The answer was “no”.’ Andrew Marr, October 2016.

‘The big why?’ is psychologically appealing but it is a mistake. In general terms it is the wrong way to look at history and it is specifically wrong about the referendum. If it were accurate we would have won by much more than we did given millions who were not ‘happy with the way things are’ and would like to be out of the EU reluctantly voted IN out of fear. Such stories oversimplify and limit thinking about the much richer reality of branching histories.

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Branching histories in 2016: three powerful forces, many possible campaigns

Sometimes the outcome of a vote is clear before a campaign starts such that it is reasonable to say ‘the campaign didn’t matter’ other than in the negative sense that, provided it avoids huge disasters, the twists and turns, the exact messages and adverts, thousands of decisions taken and so on very likely had no impact on the binary outcome. For example, Reagan’s re-election campaign in 1984 or Blair’s re-election campaign in 2001 were campaigns like this. Both won by so much and were clearly predicted by very large and historically very unusual poll leads well in advance. It is not plausible to say that the weeks of campaigning affected who won. At most the campaigns affected the scale of victory.

The referendum was not like this. Throughout the second half of 2015 and the beginning of 2016 the averages of polls – the only sensible way to look at polls – showed clear IN leads. All polls showed significant shifts towards Leave in the last five weeks (then a shift towards Remain at the end but this was at least partly because London-based pollsters changed their methodology thinking that they were making them more accurate – they fooled themselves). Polls tracking deeper attitudes that had been consistent for years suddenly changed in the last few months in ways that were significant given the close outcome. Recent claims that the polls ‘really’ showed Leave ahead all the time should be taken with very large pinches of salt given their dodgy statistical claims, charlatan authors like Matt Goodwin (who treats data dishonestly), and the inherent impossibility of discovering the truth of such a question.

One example from our private ICM polls (I will post the data tables for all these): Vote Leave asked people to choose between these options regularly to probe attitudes to the EU that are more informative than just the referendum question. The 11 point gain for ‘strong out’ is much bigger than the margin of error, is supported by other data, and is clearly significant.

screenshot-2016-11-27-13-58-44

The cold reality of the referendum is no clear story, no ‘one big causal factor’, and no inevitability – it was ‘men going at it blind’. The result was an emergent property of many individual actions playing out amid a combination of three big forces (see below). Many of these actions were profoundly nonlinear and interdependent and the result that we actually witnessed was very close. If about 600,000 people – just over 1% of registered voters – had decided differently, IN would have won. This is a small enough margin that it could easily have happened if quite a few specific events and decisions had turned out differently. If just one person had behaved differently the dominant story now would be ‘the economy was always going to trump a revolt against the elites, the status quo and “the economy stupid” always win’ – which is what the overwhelming majority of pundits said before 23 June and in some cases had drafted for their columns after the vote.

For example, if Michael Gove had stayed out of the campaign then Vote Leave would almost certainly have either collapsed (which it nearly did anyway) or been forced into fighting the campaign on a losing message like ‘Go Global’, a firm favourite for many years among a subset of MPs and Farage’s inner circle (Leave.EU adopted this as its first slogan) and a total loser with the public. (Therefore another counterfactual: why did Cameron and Osborne not try very hard to get a clear commitment from Gove that all he would do is issue a statement but would carry on with his day job and would not campaign? I hope he would have refused but it was worth a shot and they didn’t try very hard.)

Without Boris, Farage would have been a much more prominent face on TV during the crucial final weeks, probably the most prominent face. (We had to use Boris as leverage with the BBC to keep Farage off and even then they nearly screwed us as ITV did.) It is extremely plausible that this would have lost us over 600,000 vital middle class votes.

Without Victoria Woodcock, an absolutely phenomenal manager and by far the single most important person in the management of Vote Leave (and who would have been running Downing Street now but for the Gove-Boris debacle – more branching histories), we would not have been able to build anything like the structure we did and this could easily have cost us the winning margin of votes.

Anybody who says ‘I always knew X would win’ is fooling themselves. What actually happened was one of many branching histories and in many other branches of this network – branches that almost happened and still seem almost real to me – we lost.

Problems with Vote Leave

This is not a claim that ‘we won because of the Vote Leave campaign’. Our campaign failed to do much that we should have done. There were powerful connections between:

  • infighting over who appeared on broadcast and strategy,
  • the lack of resources (many kept clear because of the infighting and many used infighting as an excuse to keep clear of something they thought was doomed),
  • the extreme difficulty of finding a governance system that could work,
  • four crucial posts held by the wrong people (including the disastrous John Mills as first Chairman),
  • the fundamental structure of how the media works (see below),
  • the extreme difficulty of getting prominent people to say on TV what research showed was necessary to win, and
  • the lack of anything resembling a well-organised mass movement.

Despite many years to prepare, the eurosceptic community had built remarkably little to prepare for the battle. On the ground were many small ineffective and often warring little groups and essentially no serious machinery (though Business for Britain had begun to build a business network). All this had to be built almost entirely from scratch in an environment in which many of those in charge of the small groups were sure we would lose, were less interested in winning than they were in ‘preserving our group’s identity Dominic’, and were keen to get their hands on cash being handed out by Leave.EU on condition that they not contribute to the campaign with Vote Leave. At various points UKIP HQ sent out emails to UKIP activists telling them not to work with Vote Leave and some senior activists were told by Farage’s gang that they would lose their UKIP jobs if they helped our ground campaign (luckily most of those out on the ground ignored these instructions but they were disruptive).

The office implemented the winning message in ~125 million leaflets and nearly a billion targeted digital adverts regardless of all complaints. We recruited more active volunteers (~12,000) in 10 months than UKIP in 25 years (~7,000 according to Farage). Our GOTV effort targeted crucial voters identified by traditional polling, a new type of experimental polling, the ground campaign, and the social media campaign, all overseen by the data science team. But until the last 4-5 weeks we had a big problem getting those going on TV to give the same message. The office could only do so much. If Boris, Gove, and Gisela had not supported us and picked up the baseball bat marked ‘Turkey/NHS/£350 million’ with five weeks to go, then 650,000 votes might have been lost. In the awful weekly campaign committee meetings, there were constant complaints and arguments for variations on ‘Go Global’ (until all the polls swung our way and people remembered ‘I’ve always said stick with 350 million’.) The Big Three knocked this back despite great pressure.

Some people had spent a quarter of a century talking about things that appealed to about 10% of the population and they would not pay attention to what millions of normal people actually knew and thought (‘I’ve spent years trying to ignore the NHS in elections Dominic and I’m not going to change now’ said many like Peter Bone). Media planning was extremely hard. Paul Stephenson’s media team of half a dozen, massively outnumbered by hundreds of officials, did a fantastic job but we could have done so much more if more MPs had been more determined and more supportive.

It should be remembered that the net effect of Conservative MPs was strongly supportive of IN. We won despite the net effort of Conservative Party MPs, not because of them, though the support from a small fraction was vital. Although Leave voters were more enthusiastic and determined than Remain voters, Cameron and Osborne were more focused on winning than most Leave MPs were. (Almost all Labour MPs seemed to be in a parallel universe until they got intelligence from their constituencies about postal votes after which they panicked ineffectually.)

Most of the MPs we dealt with were not highly motivated to win and lacked extreme focus, even those who had been boring everybody about this for decades. They sort of wanted to win but they had other priorities. They were very happy having dinner parties and gossiping. They were very happy coming to meetings with people they thought were important. This wasted enormous amounts of time as we had to create a string of Potemkin committees for people to attend while the core team actually did the campaign, then reinvent them as people became convinced that there were other secret meetings that they were being excluded from. They were very happy to be on the Today Programme. But they didn’t want to win that much. Not enough to work weekends. Not enough to stop having all their usual skiing holidays and winter beach holidays. Not enough to get out on the streets day after day.  Not enough to miss a great shooting weekend. Not enough, most of them, to risk annoying a Prime Minister who they thought would still control their next job after 23 June.

This lack of motivation is connected to another important psychology – the willingness to fail conventionally. Most people in politics are, whether they know it or not, much more comfortable with failing conventionally than risking the social stigma of behaving unconventionally. They did not mind losing so much as being embarrassed, as standing out from the crowd. (The same phenomenon explains why the vast majority of active fund management destroys wealth and nobody learns from this fact repeated every year.)

Our core campaign team were not like this. They sacrificed weekends, holidays, and family events. They worked like dogs week in week out for little money often treated with appalling rudeness by people calling from their beach loungers (Boris, Gisela and Gove were three notable exceptions and all three were liked by junior staff partly because of their good, therefore rare, manners). We were happy to risk looking stupid to win. We knew that almost nobody in SW1 understood or agreed with what we were doing. We also knew we had more chance of winning if we did not explain a lot of it – most importantly the entire digital and data science element which (combined with the ground campaign and GOTV) gave us a chance to exploit strong network effects  (and which we hid from the Board and MPs, see HERE).

Example… We were urged by everyone to hire a big advertising agency and do traditional posters. ‘When can we discuss our posters?’ I was asked constantly by people who would then try to explain to me their creative ideas (‘we need another Labour Isn’t Working, Dominic, I’ve got an idea for a picture of the globe and arrows…’). One of the few reliable things we know about advertising amid the all-pervasive charlatanry is that, unsurprisingly, adverts are more effective the closer to the decision moment they hit the brain. Instead of spending a fortune on an expensive agency (with 15% going to them out of ‘controlled expenditure’) and putting up posters to be ‘part of the national conversation’ weeks or months before the vote, we decided to 1) hire extremely smart physicists to consider everything from first principles, 2) put almost all our money into digital (~98%), 3) hold the vast majority of our budget back and drop it all right at the end with money spent on those adverts that experiments had shown were most effective (internal code name ‘Waterloo’). When things are digital you can be more empirical and control the timing. The world of advertising agencies and PR companies were sure we had screwed up because they did not see what we were doing. (Tim Bell told everybody we were doomed because we hadn’t hired one of his companies.) This points to another important issue – it is actually hard even for very competent and determined people to track digital communication accurately, and it is important that the political media is not set up to do this. There was not a single report anywhere (and very little curiosity) on how the official Leave campaign spent 98% of its marketing budget. There was a lot of coverage of a few tactical posters.

There were some MP heroes.

Example… Steve Baker often disagreed with me, sometimes very strongly, but he was a rare person in the campaign – an honest man. Not only did Steve win some important Parliamentary battles he also played a vital role during the attempted coup of 25 January. If he had thrown in his lot with the coup, it might have proved fatal. Instead he spoke honestly about the situation. We did not agree and we were both under pressure from a set of people who thought that ‘if they [HQ/MPs] control the campaign we will lose, we [HQ/MPs] must control it’. We came to an agreement that we both stuck to. With five weeks to go, there was an attempt to revive the coup by a couple of VL Board members working with players from the January coup like Malcolm Pearson. The demand was to replace the Big Three (Boris, Gisela, Gove) and the core campaign team with Farage, and replace £350 million / NHS with ‘go global’ trade babble. This didn’t get past the usual weekend boozy chats partly because of Steve Baker telling them he thought it a mad plan. This also shows how volatile the situation was right until the end and how few prominent eurosceptics even then understood that a) the £350 million / NHS argument was necessary to win and b) their ‘go global’ message was a total loser.

Other MPs also made significant personal sacrifices – backbenchers like Anne Marie Trevelyan and Graham Stringer, and ministers like George Eustice and Dominic Raab.

Rough balance of forces

The IN side started with huge structural advantages.

  1. IN started in 2015 well ahead in the polls and had the advantage of having the status quo on its side which is intrinsically easier to explain than change is, as lots of historical data around the world shows. Usually the ‘change’ campaign has to start considerably ahead in order to win as it loses support as the campaign goes on. This argument was even stronger with something so much bigger and more complex like the EU. VL had to persuade millions of people to risk a profound change. Those on the IN side made this point repeatedly for many months. They were right then. After 23 June many of them say the exact opposite – it’s so complex to explain all the wonders of the EU, they say, and so easy to argue for change. This is laughable.
  2. IN had the government at its heart including the Downing Street machine, the Cabinet Office, and Government departments and agencies all of which added up to thousands of people including hundreds of press officers. Cameron and Heywood also instructed Permanent Secretaries not to share EU material with Secretaries of State supporting Vote Leave in order that they did not have access to new information about all the ways in which EU law affected policy. (In general Whitehall has made great efforts to hide the scope of EU control. It also preserves, Potemkin-style, old processes like circulating Cabinet papers ‘for approval’ where the only acceptable response is ‘approve’ – it is not actually legally possible not to ‘approve’ but still the papers are sent round via the absurd red box system daily.) VL had a few dozen effective people and no access to the official machine other than some leaks. We had a research team of about five. MPs proved largely useless in helping this team.
  3. IN controlled one side of the renegotiation and its timing. VL was at the mercy of events and could not get any ministers supporting us until the process ended.
  4. IN controlled the timing of the referendum. VL had to plan resources on the basis of many scenarios.
  5. IN controlled the Cabinet and junior ministers – bribes for support and threats to deter. They had the chance to set the terms for how ministers engaged in the campaign (though they partly blew this). VL had to meet ministers in secret, could guarantee them no jobs, and (as was pointed out to me by many) could not dodge the basic truth that purely from a personal career perspective it was usually better to support the PM.
  6. IN controlled the governing party and the Parliamentary timetable and procedures. VL had to work with a small number of MPs many of whom had spent many years in constant opposition to their own leadership and were unused to any sort of discipline or collective action.
  7. IN set the legal rules. VL faced a huge imbalance in how these worked. For example, Cameron even during the official campaign could do huge events at places like the British Museum and the IN campaign did not have to account for such events as part of their £7 million. Meanwhile VL was told by the Electoral Commission that if people we did not even know put up huge signs that appeared on TV we might get billed for them. There were many other consequences of the imbalance. E.g. the Government’s legal timetable meant we had to commit before the official start of the campaign to a load of activity that would occur after the official start of the campaign without knowing if we would be the official campaign and therefore legally entitled to spend this money. We therefore had to choose between either a) not do various things, be sure we would not break the law, and lower the chances of winning or b) do the right thing for the campaign and riski being judged to have broken the law. Obviously we did (b) though we had to hide this choice from some of those on our Board as this was exactly the sort of thing some of them were very weak about.
  8. IN had access to huge resources – financial, personnel etc. IN had the support of almost every entity with power in Britain, Europe, and the world from the senior civil service to the CBI to the big investment banks, to Obama and the world bureaucracy (G20, UN, IMF etc).  Very few senior people were prepared to risk supporting us. Those who did mostly did so in a small way and on their own terms without getting involved in our campaign. While IN could send out name after name to deliver their message, we could depend on very few names who would deliver our message. The Government machine, the Commission, and the Cabinet Office were effective in scaring off prominent people from supporting us; many of them told us (some embarrassed) about the phone calls they’d had and their ‘duty to shareholders’ and so on. Advanced media planning was almost impossible and we had to shuffle things around at short notice constantly. IN had millions more than us before the campaign ever started and used this money for direct voter communication. We could not afford this. We sent out one 10 million voter mailing to people identified by the physicists just before the spending limits started and we could only do this by tricking some of those on our Board about the numbers. (I was also  helped by Peter Cruddas saying, ‘Don’t worry about the fundraising situation, don’t listen to everybody panicking, just do whatever it takes to do the campaign, if the money doesn’t come I guarantee I’ll put in whatever you need’. I knew I could trust him. This gave us vital flexibility and also meant we could ignore some of those on the Board who were more focused on whether they may be liable for a bill post-23/6 than they were on winning.)
  9. IN had the support of most journalists and senior management in the main broadcasters. The broadcasters let the Government set the agenda on TV for almost the entire campaign, apart from ten crucial days after the immigration numbers on 26 May. VL had the support of some powerful papers but we were overwhelmed on TV news. (Two broadcast journalists who were conspicuous by their unusual professionalism and determination to act fairly despite the behaviour of some of their management were Laura K and Allegra Stratton.)
  10. IN started with legal access to vast amounts of electoral data from at least three political parties, unofficial / illegal access to vast amounts of data from things like CCHQ data and the Crosby/Messina models built during the campaign, and vast amounts of commercial data. (CCHQ laughably claimed that there were ‘Chinese walls’ that prevented any abuse of Party data.) VL had none of these things. We could not even afford to buy standard commercial datasets (though the physicists found ingenious ways around this). We had no way even to acquire the electoral roll until the official process allowed us in early 2016, after which we had to wait a couple of months for LAs to fulfil their legal obligations to provide us with the data (which they did patchily and often late).
  11. IN had a great boost to its fortunes in the form of a network linking Nigel Farage, Aaron Banks, assorted peers (e.g. Malcolm Pearson), MPs (e.g. Bill Cash), businessmen (e.g. Richard Smith), and a handful of Vote Leave Board members (including the one-time Chairman John Mills) and some staff foisted on us (one of whom won the title of the most repellent person I’ve met in politics – Nigel Griffiths, an ex-MP who some female staff refused to be in the same room with). Farage put off millions of (middle class in particular) voters who wanted to leave the EU but who were very clear in market research that a major obstacle to voting Leave was ‘I don’t want to vote for Farage, I’m not like that’. He also put off many prominent business people from supporting us. Over and over they would say ‘I agree with you the EU is a disaster and we should get out but I just cannot be on the same side as a guy who makes comments about people with HIV’.

On 25 January 2016 a network of these characters launched a coup. But for the actions of Stephen Parkinson, Paul Stephenson,  and Victoria Woodcock (supported by most but not all of the office) it would have succeeded. This would have given control of the official campaign to the Farage crowd. They ran with vapid slogans like ‘Be in the know’. Ironically for a group of people who claim to be anti-SW1 they rehashed the classic losing SW1 eurosceptic trope for 25 years – ‘Go Global’ – showing how little they understood the electorate and mass communication. They rejected the connection between immigration, £350 million and the NHS, which was absolutely vital, as the IN side has said after 23 June (see below). They published dumb offensive videos. They talked about privatising the NHS. They built little grassroots organisation and their claims about social media were (and remain ) laughable. Farage himself admitted after 23 June that they did not have the organisation to run the campaign if they had won designation: ‘quite what we would have done if we had got it I’m not really sure!’, which sums them up (Shipman, Location 4,150). The media would have covered this gang’s official campaign as a version of their own book – a bunch of childish dodgy boozers on an ego trip.

Before the 2015 election Farage said to me at Stuart Wheeler’s that he knew he could not be the leading face of the campaign – ‘I’m one of the generals but I can’t lead the army’ he said, to my relief. When I next saw him in the summer, I was amazed at how his tune had changed, his obsession with the debates, and his pessimism. One can only understand some of the behaviour from those around Farage if you realise that much of their operation was about positioning Farage for what they assumed would be defeat.

One of the biggest problems during the campaign and biggest misconceptions after concerns this issue. Those who argued ‘we need one campaign’ were wrong. Those who argue now ‘we would have won by more if there’d been one campaign’ are wrong. One campaign would have meant total bedlam and 60-40 defeat.

If MPs had had extreme focus on winning then they would not have used Farage as leverage against us viz official designation and therefore much of the infighting could have been avoided as Farage would have done a sensible deal with us early, realising much earlier that we would not compromise over him running the campaign under any circumstances. By encouraging Farage to think that he could get a much more prominent position, people like Bill Cash nearly destroyed everything.

Given all these huge advantages, if their campaign had been of equal effectiveness to Vote Leave then, all else remaining equal, Cameron would almost certainly (>95% likely) have won.

Why did all these forces not add up to overwhelming and devastating firepower? If you want to understand the combination of things that gives us largely dysfunctional government and therefore undermined the IN campaign – a mix of selecting and promoting the wrong people, wrong education and training, bad incentives, anti-adaptive institutions and so on – then read this in which I explain in detail why Whitehall does not and cannot work properly.

The approximate truth

The closest approximation to the truth that we can get is that Leave won because of a combination of 1) three big, powerful forces with global impact: the immigration crisis, the financial crisis, and the euro crisis which created conditions in which the referendum could be competitive; 2) Vote Leave implemented some unrecognised simplicities in its operations that focused attention more effectively than the other side on a simple and psychologically compelling story, thus taking advantage of those three big forces; and 3) Cameron and Osborne operated with a flawed model of what constitutes effective political action and had bad judgement about key people (particularly his chief of staff and director of communications) therefore they made critical errors. Even if (1) and (2) had played out the same, I think that if that duo had made one of a few crucial decisions differently they would very likely have won.

When I started to research opinion in 2014-15 and compared it to my experience of the euro campaign (1999-2002), it was clear three forces had changed opinion on the EU.

1) The immigration crisis. 15 years of immigration and, recently, a few years of the migration crisis from the East and Africa, dramatically portrayed on TV and social media, had a big effect. In 2000, focus groups were already unhappy with immigration but did not regard it as a problem caused by the EU. By 2015, the EU was blamed substantially for the immigration/asylum crisis and this was entangled with years of news stories about ‘European courts’ limiting action against terrorists and criminals. Actually often these stories concerned the Strasbourg court of the ECHR (not the ECJ) though, ironically, the EU’s adoption of its Charter of Fundamental Rights meant that many issues concerning the ECHR became relevant to the EU debate, something that almost nobody in SW1 realised and we tried and largely failed to explain (one of the very few who did understand this was Boris’s wife, an accomplished lawyer, who I discussed this with in autumn 2015).

2) The 2008 financial crisis. This undermined confidence in Government, politicians, big business, banks, and almost any entity thought to be speaking for those with power and money. Contra many pundits, Miliband was right that the centre of gravity has swung against free markets. Even among the world of Thatcherite small businesses and entrepreneurs opinion is deeply hostile to the way in which banks and  public company executive pay work. Over and over again outside London people would rant about how they had not/barely recovered from this recession ‘while the politicians and bankers and businessmen in London all keep raking in the money and us mugs on PAYE are paying for the bailouts, now they’re saying we’ve just got to put up with the EU being crap or else we’ll be unemployed, I don’t buy it, they’ve been wrong about everything else…’ All those amazed at why so little attention was paid to ‘the experts’ did not, and still do not, appreciate that these ‘experts’ are seen by most people of all political views as having botched financial regulation, made a load of rubbish predictions, then forced everybody else outside London to pay for the mess while they got richer and dodged responsibility. They are right. This is exactly what happened.

Many Tory MPs and ‘free market’ pundits / think tankers are living in a fantasy world in which they want hostility to big business to end even though everybody can see that those who failed largely escaped responsibility and have even gone back to doing the same things. (I’ve argued since 2001 for big changes on executive pay to almost zero effect. SW1 is full of people who think they’re ‘defending markets’ but are actually defending the opposite – corporate looting. In the 1930s Britain put people in jail because of what happened in the 1920s. We should have done the same after 2008.)

3) The euro crisis. Britain joined the EEC because it was a basket case in the 1970s and ‘Europe’ was seen as a modernising force that could help us recover and improve the economy and living standards. As the euro crisis hit, millions saw Greece in chaos, even flames, for month after month. This undermined confidence in the EU as a modern successful force – ‘it’s so bad even Germany’s in trouble now because of the euro’, ‘not even Germany can afford to sort this out’, people would say.

Together these three big forces undermined confidence in the EU project as a modern force for progress that brings prosperity and solves problems and pushed it into about 30-35% of the population (younger, richer, better educated) which increasingly saw the EU in terms of ‘are you racist / supporter of Farage?’ This feeling was central in 1975. It diminished gradually but was still partly there 1999-2002 when I was doing focus groups on the euro. (It is why I had so many arguments at the time with eurosceptics explaining to them that if we accepted Blair’s framing of the euro debate as IN/OUT of the EU, we would lose. Our two slogans were therefore ‘Europe yes, euro no’ and ‘Keep the pound, keep control’.)

Second, they undermined confidence in those in charge. There had been strong anti-Westminster feelings growing for over a decade. In 2004 with James Frayne and my uncle I set up the campaign to fight the referendum on the North East Regional Assembly as a training exercise for an EU referendum (then envisaged after Blair’s 2005 victory). We came from behind and won 80-20 (not a misprint) despite having almost no money, no support, and the entire North East establishment against us because we exploited this feeling (‘politicians talk, we pay‘ was our slogan). SW1 ignored the result. It did not appreciate the scale of this growing force even after the financial crash and expenses scandal. Normal electoral politics and the structural grip of established political parties fooled insiders about the extent of support for people like Cameron. Cameron won negatively – because he was not Brown or Miliband. There was very little positive feeling for him. They fought the referendum with him and Osborne at the front as if they were fighting Brown or Miliband and asking people to make a choice: this is not how most people saw it.

These three big forces and the failure of the parties to cope, combined with the daily resentment of paying taxes for the bill of the 2008 Crash, meant that in a vote like 2016 where people did not have to vote to stop Brown or Miliband ‘stealing my money’, millions who were unpersuaded by Cameron/Osborne felt free to vote positively for something (‘take back control’) and against a duo they disliked, distrusted, and saw as representative of politicians’ failure over many years.

These three big forces had global impact and had much more effect on people who pay a normal amount of attention to politics than every speech, article, pamphlet and ‘campaign’ about the EU over 15 years, the sum total of which had almost no discernible effect.

Those who think I am exaggerating the relative lack of influence of conscious SW1 activity could consider another example – the Gove education reforms 2010-14 (which I was closely involved with). These reforms were one of the most prominent stories of the 2010-15 Government with thousands of stories and broadcast discussions. I researched public attitudes to these reforms after I resigned from government in January 2014 (contrary to widespread belief the Cameron operation spent very little time and resources before 2014 on researching public opinion, they were focused on the media rather than the public). Approximately nothing of our arguments  – including the years of speeches by Blair too – had got through to the public.The entire SW1 media debate had approximately no impact on public opinion. People had some idea of some changes if they had kids in school but knew almost nothing of the arguments. Consider how much more motivated people were to learn about this than they were about the EU. (Part of the reason is that the language that Cameron and SW1 generally used was about ‘choice, competition’ and so on. I was almost totally unsuccessful in persuading people to talk about the issue in a different way which is one of the reasons I spent so little time on communication and almost all my time on management in the DfE. Gove knew the problem but also knew that there was no chance of getting Cameron to do things differently.)

This is relevant to the immigration argument in particular. Many pundits who described themselves as ‘modernisers’ wrote columns over the years arguing that immigration was an issue because Cameron was making foolish promises about it and the media therefore paid more attention to it. This is wrong. Cameron’s foolish promises certainly made his situation worse but it is wrong to think that public interest in an issue is proportional to the attention paid by politicians and newspapers in SW1. The public only pays attention to a tiny subset of issues that politicians and the media bang on about. It is largely impossible to predict which things will catch fire and which will not, though process stories and ‘scandals’ almost always have zero effect and insiders repeatedly get this wrong. Long before there was any prominent media discussion of ‘the Australian points system’ you could hear it being discussed in focus group after focus group to an extent that was very surprising to me and was very surprising to every single person I discussed it with, including Farage (who adopted the policy because of focus groups, the causal chain was not – Farage talks >> focus groups respond).

Making these three forces even more powerful was the nature of the reaction from those in charge in the EU and Britain – a general failure not only to grip the problems but even to show that they understood what the problems were. There was clearly no sensible movement for reform of the EU. As it lurched from crisis to crisis, its only response was ‘the EU needs more power’ (this is, of course, the founding logic of the Monnet-Delors system). The British Government clearly had no sensible plan for dealing with the EU’s crises and dysfunction. Worse, their responses were often obviously rubbish, such as the ‘tens of thousands’ immigration promise that people could see had no chance of being met yet politicians just kept repeating it. People naturally concluded – these guys in London don’t grasp the seriousness of the problems, they haven’t a clue what to do, and are treating us like idiots. Cameron’s renegotiation did not change this view. The Government therefore entered the campaign in a very different state to Wilson in 1975.

These three forces meant that by summer 2015 only about a third of the electorate positively wanted to be inside the EU. Another third strongly wanted to leave and were not worried about the economy. Another fifth had roughly the view that – the EU is rubbish, I’d like to be outside, but I’m worried about the short-term effects on jobs and living standards so maybe I’ll vote IN (see the ICM table above). Further, our research showed that the strong Leave third was significantly more enthusiastic about the referendum than the strong Remain third and the swing fifth, and therefore more likely to vote.

Vote Leave exploited these forces

I will go into this in much more detail and I will ignore all management/operational issues here.

Our story rested on five simple foundations that came from listening very hard to what people really knew, thought, and said:

1. ‘Let’s take back control’. The overall theme. When I researched opinion on the euro the best slogan we could come up with was ‘keep control’. I therefore played with variations of this. A lot of people have given me a lot of credit for coming up with it but all I really did was listen. (NB. ‘back’ plays into a strong evolved instinct – we hate losing things, especially control.)

2. ‘The official bill of EU membership is £350 million per week – let’s spend our money on our priorities like the NHS instead.’ (Sometimes we said ‘we send the EU £350m’ to provoke people into argument. This worked much better than I thought it would. There is no single definitive figure because there are different sets of official figures but the Treasury gross figure is slightly more than £350m of which we get back roughly half, though some of this is spent in absurd ways like subsidies for very rich landowners to do stupid things.)

Pundits and MPs kept saying ‘why isn’t Leave arguing about the economy and living standards’. They did not realise that for millions of people, £350m/NHS was about the economy and living standards – that’s why it was so effective. It was clearly the most effective argument not only with the crucial swing fifth but with almost every demographic. Even with UKIP voters it was level-pegging with immigration. Would we have won without immigration? No. Would we have won without £350m/NHS? All our research and the close result strongly suggests No. Would we have won by spending our time talking about trade and the Single Market? No way (see below).

NB. Unlike most of those on our side the IN campaign realised the effectiveness of this, as Cooper, Coetze and others said after 23 June. E.g. ‘The power of their £350 million a week can’t be overstated.’ Andrew Cooper, director of strategy for the IN campaign.

Some people now claim this was cynical and we never intended to spend more on the NHS. Wrong. Boris and Gove were agreed and determined to do exactly this. On the morning of 24 June they both came into HQ. In the tiny ‘operations room’ amid beer cans, champagne bottles, and general bedlam I said to Boris – on day one of being PM you should immediately announce the extra £100 million per week for the NHS [the specific pledge we’d made] is starting today and more will be coming – you should start off by being unusual, a political who actually delivers what they promise. ‘Absolutely. ABSOLUTELY. We MUST do this, no question, we’ll park our tanks EVERYWHERE’ he said. Gove strongly agreed. If they had not blown up this would have happened. The opposite impression was created because many Tories who did not like us talking about the NHS reverted to type within seconds of victory and immediately distanced themselves from it and the winning campaign. Unlike Gove and Boris they did not learn from the campaign, they did not listen to the public. Until people trust that the NHS is a financial priority for Tories, they will have no moral authority to discuss management issues. This obvious fact is psychologically hard to absorb because of the strength of gang feelings in politics.

(There are already myths about some of these events. The press conference of 24 June is now written up as the two of them ‘terrified of what they had done’ but this is completely wrong. They were subdued partly because they were genuinely sad about Cameron and partly because they did not want to be seen as dancing on his grave. Some of the media created the psychologically compelling story that they were regretful / frightened about victory but this was not at all their mood in HQ on the morning of 24 June. Boris came in punching the air like Maradona after a great goal, hugging staff and clearly euphoric. It is completely wrong to portray him as regretful.)

3. ‘Vote Leave to take back control of immigration policy. If we stay there will be more new countries like Turkey joining and you won’t get a vote. Cameron says he wants to “pave the road” from Turkey to here. That’s dangerous. If we leave we can have democratic control and a system like Australia’s. It’s safer to take back control.’

I was surprised at what a shock it was to IN when we hit them with Turkey. By the time this happened they were in an almost impossible position. I wanted them to announce a veto. It would not have been believed and would have had the opposite effect – people would have taken the danger of Turkey joining more seriously. If your life depended on winning for IN, the answer is clear: they should have said long before the campaign started as part of the renegotiation process that they would veto any accession.

4. ‘The euro is a nightmare, the EU is failing, unemployment is a disaster, their debts and pensions are a disaster, if we stay YOU will be paying the bills. It’s safer to take back control and have a new relationship based on free trade and friendly cooperation instead of the European Court being in charge of everything…’ (This is not an official text, just a summary of the notion off the top of my head.)

5. Anti-Establishment. E.g. We aligned our campaign with those who were furious with executive pay / corporate looting (about 99% of the country). We aligned ourselves with the public who had been let down by the system.

Mandelson regarded this as ‘sheer nerve, sheer chutzpah’. It was obvious. The hard thing was sticking to it despite the sensibilities of many of our own supporters. One of the most effective TV performances of the campaign was the day Boris hit the theme of corporate looting in a market square. No10 were rightly panicked and in response pushed out Heseltine a few hours later to make a very personal attack on Boris. This made sense tactically but was a strategic error. All such personal attacks helped persuade Boris to up the ante. This was vital with a month to go when the immigration figures came out. Rudd and others argue that Cameron should have attacked Boris and others more. Wrong. They should have played it Zen publicly and had a much better black ops team.

Cameron/Osborne mistakes

I’ll go into this separately but just to give a few examples…

1. Cameron never had to offer the referendum in the first place. His sudden U-turn was a classic example of how his Downing Street operation lurched without serious thought in response to media pressure, not because of junior people but because of Cameron himself and his terrible choice of two main advisers (Llewellyn and Oliver). This happened many times and I wrote about all the damage it caused on other issues after I left government (HERE). This was the biggest example. It was a product of a deeper error – a combination of his failure of party management (misleading them about the best way to handle the party) and failure to understand how swing voters really think and therefore the dangers of a vote (see below).

2. If Cameron/Osborne had had a top notch person like David Plouffe running their campaign and they did as they were told then they would have won (>95% confidence), all else being equal. They were warned many times by their closest friends about Oliver and Llewellyn, including by Gove, but would not listen.

3. Their renegotiation was flawed from the start and badly undermined their central message. They compounded their errors in 2015 by accepting the pathetic deal in 2016.  If they had walked away in February then Vote Leave would quickly have imploded and the flying monkeys would have taken over the campaign.

4. They made themselves too prominent in the campaign and were too crude. Lacking a feel for psychology they gradually undermined their own message. Oliver thought Obama’s ‘back of the queue’ was brilliant. It was counterproductive. They thought ratcheting up the warnings to DEFCON 1 was effective. It was counterproductive.

5. They doubled down on ‘tens of thousands’. They thought they would lose credibility if they didn’t. The opposite was true. They should have dropped this in 2015 – for example, in an exclusive to the Independent on a Saturday in early August 2015 – and gone into the campaign without it. Every time they defended it they were helping us.

6. They suckered themselves into over-prioritising their coalition versus message. Blair’s campaign against us in the North East did the same. When you do this you lose focus and clarity which is usually fatal. The error was perhaps most visible the day Cameron unveiled an absurd poster that effectively listed all the ‘serious people’ on their side and – creative genius! – a blank page for us. A total waste of valuable time. The fact of being the Government meant the broadcasters let them lead the news almost all the time but they often wasted it like this. (I would bet that that ad was never put in focus groups or if it was the results were ignored.)

7. One of my basic criticisms of Cameron/Osborne from the start was the way they steered by pundit. During the 2015 election Crosby partly corrected this and they partly learned the lesson. But left to their own devices in the referendum when under pressure they defaulted to their instincts at a crucial moment. The reaction to the dreadful murder was an example of how the media and SW1 can live effectively in a parallel universe. Somehow they convinced themselves that this event might undo over a decade of growing hostility for those in power. They therefore tried to push the theme that actually MPs are great, ‘they are in it for good reasons’ and so on. The media led themselves into a dead end and No10, defaulting to their instincts of steering by pundit, followed. As soon as I saw Osborne and Matt Hancock wasting their time tweeting broken multicoloured hearts and encouraging #weloveourMP, I knew they had screwed their own OODA loop. We knew from focus groups (conducted by the brilliant Henry de Zoete who also played a crucial role in coordinating the digital and data science teams) that opinion outside London was extremely different to that of MPs and those in charge of most news. We went straight back to what we knew were the winning messages leaving Hancock and co to tweet broken hearts.

BUT BUT… Roland Rudd and others have attacked them for their basic strategy of focus on the economy and argue there should have been ‘a positive campaign for the EU’. WRONG. Cameron and Osborne were right about this big call. There was not enough time or money to change basic attitudes. As the campaign developed and there were signs of pressure from Rudd and others I crossed my fingers and hoped they would shift strategy. No10 were right to ignore him.

I suspect that in general big mistakes cause defeat much more often than excellent moves cause victory. There are some theoretical reasons to suspect this is true from recent statistical analysis of human and computer decisions in chess. Two results are particularly interesting. 1) The very best computers seem to make moves that preserve  the widest possible choices in the future, just as the most effective person in politics for whom we have good sources, Bismarck, operated always on the principle of ‘keep two irons in the fire’. (We tried to mimic this by adopting a message that we thought had the highest probability of  winning in the largest number of plausible branching futures, hence £350m/NHS.) 2) Even great humans are distinguishable from great computers by their propensity to make clear tactical errors occasionally amid the fog of war. This is significant enough that it wipes out the advantage of going first – i.e. it being ‘your move’ is seen as a plus but in fact it is a minus for humans because of the probability of a significant error, while for computers this effect is absent. (See Human and Computer Preferences at Chess, 2014. It would be very interesting to know if these results are supported by the recent success of Deep Mind with computer GO.)

Summary of the false dichotomy

False: ‘Leave won because of the campaign.’ E.g. Without 15 years of out of control immigration, our message of ‘take back control’ would not have had enough traction. Campaigns can ride big waves but they almost never make them.

False: ‘Leave won because of a big event [immigration, 2008 crash etc], the campaign was irrelevant.’ E.g. If the campaign had not deployed £350 million and the NHS (which almost nobody on our side liked), we would not have neutralised/overwhelmed Project Fear.

True: ‘Leave won because 1) three big forces created conditions in which the contest was competitive, AND 2) Vote Leave exploited the situation imperfectly but effectively, AND 3) Cameron/Osborne made big mistakes. If just one of these had been different, it is very likely IN would have won.’

Overall, the now-mocked conventional wisdom that ‘the status quo almost always wins in referendums like this’ obviously has a lot of truth to it and it only proved false this time because of a combination of events that was improbable.

*

A ‘miracle’ to get 48%? Beaten by lies? Corbyn the AWOL saviour?

Since losing many inside the IN campaign now talk dejectedly as if they could never have won and tell rationalising fairy tales. They are wrong. They almost did win. Some have latched onto the idea that they were overwhelmed by an epic, global force of ‘right-wing populism’. Mandelson defends himself by saying  48% looks ‘like a miracle’ given the populist tide. Most have latched onto the idea that their ‘complex truth’ was overwhelmed by ‘simple lies’ and they are happy with their comforting ‘post-truth’ sobriquet – a delusion that leaves them very vulnerable to being shocked again. Many have even argued that they lost because they could not persuade Corbyn to make more speeches.

These stories are psychologically preferable to the idea that their own errors caused defeat (just as it is for some of those in Hilary’s campaign) but should not be taken seriously.

The least plausible claim is that Corbyn sabotaged what was otherwise a winning campaign. This is argued mainly by the same people (including Mandelson) who in a party context also argue that Corbyn is a joke who nobody takes seriously. The idea that more speeches by Corbyn would have persuaded vital swing voters has no good evidence. These people wanted to ‘take back control’. Corbyn’s message was – there should be not just more immigration but no limits on it. There are not many branching histories in which this is a winner.

This ‘epic global force’ of ‘populism’ was thought by the same people before 23 June to be puny in comparison with the force of the combined Establishment hammering a message of economic fear in support of the status quo. Having underestimated certain trends in public opinion the same people are now exaggerating them (see below).

This is connected to ‘complexity’. Month after month they argued (including to us in private discussions) that they would win largely because they had the advantage of the status quo – an advantage proved in votes around the world over many years. They were right. That was a big advantage. It is much simpler to argue for the status quo than for a very complex change – that is exactly why most ‘change’ referendums lose, just as they briefed the media. Now they say ‘The EU is very complex, it requires a lot of information to explain it’ (Craig Oliver). Their claim that actually they had the ‘complex’ argument to make against our ‘simple lies’ is laughable for exactly the reasons they gave themselves before they came unstuck.

Connected to this idea is that the great rationalists Cameron and Osborne – they of Project Fear and their comic ’emergency budget’ and in 2015 the pictures of Salmond picking pockets designed successfully to persuade the English that the Scots would steal their money – were undone by a great surge of ’emotion’. Osborne is taking this delusion so far he is writing a book titled ludicrously ‘The age of unreason’. When you lose and you blame it on millions of people being overtaken by ‘unreason’ – after previously winning by exploiting nationalist hostility – it’s a sure sign that you are the one not reasoning straight and able to face your errors. For the likes of Osborne it is ‘irrational’ to reject the views of people like him. For most of us, people like Osborne are not experts to be trusted – they are charlatans not to be taken seriously.

Many of those who blame defeat on ‘lies’, including Cameron, Osborne, and Clegg themselves told flat-out lies. One example will do. Cameron and Osborne claimed repeatedly on TV, almost always unchallenged, that their new deal meant ‘after six months if you haven’t got a job you have to leave’. This is not an argument over the fairness of using a gross/net figure, like ‘£350 million’, or even a properly bogus figure like the Treasury’s £4,000 per household figure. It is a different category of claim – a flat out 100% lie. (For more details see HERE.) How much time did TodayNewsnight, and the Guardian spend explaining to people that the PM and Chancellor were lying through their teeth? Approximately none. Why? Because very few of those complaining about lies really are cross about ‘lies’ – they are cross they lost and they are not so interested in discussing a lie that undermines the pro-EU campaign’s attempt to neutralise fear of immigration.

Further, many of the same people spent the entire campaign saying ‘Vote Leave has admitted a Leave vote means leaving the Single Market, this is what will happen make no mistake…’ and now say ‘the Single Market was not an issue, Vote Leave never had a policy on it and there is no mandate for leaving it’. Cameron, Osborne, Mandelson, Campbell and Clegg spent much of the last 20 years lying through their teeth to further their own interests and prestige. Now they whine about ‘lies’. They deserved worse than they got – and reasonable Remain-ers deserved better leadership.

Fools and knaves

Many of those who worked on the IN side are now wrongly attacked as fools by pundits who would have praised them as geniuses had they won, while many on the OUT campaign are wrongly praised.

Example… ‘If Remain wins Cameron ought to be hailed as the genius strategist of western democratic politics’ (Rentoul). Pundits who wrongly hailed Cameron as a genius after the 2015 election now wrongly describe him as a bumbling oaf. He was neither – he was the best of a bad bunch picked pseudo-randomly in a broken system and out of his depth. 600,000 votes either way does not make one set of people geniuses and another set of people morons. Geniuses in politics are rarer than in maths and physics and nobody involved in the referendum on either side is remotely close to one. Some of those who worked on the IN side were much more able than many on the winning side. It does not make sense to label people on the IN side idiots because of errors made by Cameron, Osborne, Llewellyn, and Oliver.

Example: many have said to me ‘you were so clever to hold back on immigration until the start of purdah’. Wrong. It is true that we did not do much on immigration before the 10 week official campaign. That is because, as I wrote in 2014, we did not need to. It was far more important to plant other seeds and recruit support that would have been put off if we had focused early on immigration. Immigration was a baseball bat that just needed picking up at the right time and in the right way. The right time was before purdah and we set in motion during January-April a series of things like the free referendum address with the right message but we could not persuade many prominent people to do what was needed until after 26 May. The right way was via the NHS (unifying) – not ‘we want our country back’ of Farage (divisive). The timing was not ‘a brilliant move’ by me, it was a combination of good luck and seizing a tactical chance to persuade people of something I’d failed to do for weeks, but such things get rewritten as such if you win.

It is also foolish to see the conflict in terms of who is ‘nicer’ and ‘nastier’. I don’t think the people on our side are nicer. There are lovely and loathsome people, liars and charlatans on both sides.

Many OUT-ers talk as if we were destined to win. Wrong. The IRA used to say ‘you have to get lucky every time but we only have to get lucky once’. For Leave to win a string of events had to happen many of which were independently improbable or 50-50 and therefore the combination was very improbable. The result was certainly not an inevitable outcome of ‘the great British public simply voting for democracy’ as some romantics delude themselves.

*

Oblonsky and the frogs before the thunderstorm: fashion, delusions of the educated, and the Single Market

‘I feel that, in some ways, this was a conflict between good forces in society and bad forces. I feel that the bad forces on 23 June won a very significant victory.’ Matthew Parris.

Matt Ridley: Matthew, you’re not saying that 17 million people are, deep down, racists? 

Matthew Parris: Yes. (Spectator, December 2016)

Why is almost all political analysis and discussion so depressing and fruitless? I think much has to do with the delusions of better educated people. It is easier to spread memes in SW1, N1, and among Guardian readers than in Easington Colliery.

Generally the better educated are more prone to irrational political opinions and political hysteria than the worse educated far from power. Why? In the field of political opinion they are more driven by fashion, a gang mentality, and the desire to pose about moral and political questions all of which exacerbate cognitive biases, encourage groupthink, and reduce accuracy. Those on average incomes are less likely to express political views to send signals; political views are much less important for signalling to one’s immediate in-group when you are on 20k a year. The former tend to see such questions in more general and abstract terms, and are more insulated from immediate worries about money. The latter tend to see such questions in more concrete and specific terms and ask ‘how does this affect me?’. The former live amid the emotional waves that ripple around powerful and tightly linked self-reinforcing networks. These waves rarely permeate the barrier around insiders and touch others.

These factors are deepened by the fact that almost all of those whose job it is to explain politics and campaigns have never been responsible for a complex organisation in general or a campaign in particular, so they are unsuited to understand how politics ripples out from decisions at the centre through dysfunctional bureaucracies to the ground. They almost always exaggerate the extent to which important decisions have been considered carefully by people who know what they are talking about. (The worse educated are actually often helped by their lack of education towards the truth.) They constantly discuss complex systems as though errors can be eradicated instead of asking how quickly errors are adapted to and learned from. This perspective biases them in favour of existing centralised systems that fail continually and against innovations with decentralised systems. They understand little about the challenges faced by small businesses and the lower middle classes.

The more closely involved people are in the media and politics the more they are driven by fashion and the feeling, rarely acknowledged and almost always rationalised, that ‘this is my gang’. Look at all those in SW1 who tweet attacks on Dacre to each other then retweet the praise from their friends, then look at those who attack them. Look at Robert Peston tweeting pictures of the London Eye and Habermas quotes on election night and his opponents ranting about ‘elites’. Both sides are just like football team fans defending their in-group and attacking their out-group enemies. The more they think of themselves as original the more likely they are to be conformist – and conformist within very narrow parameters.  We all fool ourselves but the more educated are particularly overconfident that they are not fooling themselves. They back their gang then fool themselves that they have reached their views by sensible, intelligent, reasoning.

This makes them particularly vulnerable to ‘influence operations’. It also makes them vulnerable to repeated errors about what the sort of people who ignore politics other than for a few weeks before voting time are thinking. It creates something of a paradox: it is almost impossible to get a good feel of public opinion, or of ‘the winning strategy’, by listening to those whose job it is to speculate about it. However often this happens, the lesson is never learned. It is very hard to see how it could change as it is so entangled with our evolved nature.

There is a wonderful passage in Anna Karenina that sums this up, much better than any ‘political scientist’ has done:

Oblonsky never chose his tendencies and opinions any more than he chose the style of his hat or coat. He always wore those which happened to be in fashion. Moving in a certain circle where a desire for some form of mental activity was part of maturity, he was obliged to hold views in the same way he was obliged to wear a hat. If he had a reason for preferring Liberalism to the Conservatism of many in his set, it was not that he considered the liberal outlook more rational but because it corresponded better with his mode of life… The Liberal Party said that marriage was an obsolete tradition which ought to be reformed, and indeed family life gave Oblonsky very little pleasure, forcing him to tell lies and dissemble, which was quite contrary to his nature. The Liberal Party said, or rather assumed, that religion was only a curb on the illiterate, and indeed Oblonsky could not stand through even the shortest church service without aching feet, or understand the point of all that dreadful high-flown talk about the other world when life in this world was really rather pleasant… Liberalism had become a habit with Oblonsky and he enjoyed his newspaper, as he did his after-dinner cigar, for the slight haze it produced in his brain.’

Towards the end of the novel, there is a discussion about the then big issue of Turkish atrocities and the rise of pan-Slavism. The old prince replies to the intellectuals who are talking rubbish about ‘the national feeling’ that they think is ‘sweeping the country’:

‘Yes, all the papers say the same thing. That’s true. So much the same that they are just like frogs before a storm! You can’t hear anything for their croaking.’

Many will reply, ‘Oblonsky is a dilettante, not a serious character, you can’t compare him with people like Robert Peston’. Oblonsky isn’t a dummy, he’s brighter than many of the posh duffers in his club. And also consider Anna’s husband, Karenin – a terrifying reminder that the ‘serious characters’ in politics are really no better than Oblonsky regarding fashion. In politics, just about all of us are some combination of Oblonsky and Karenin. If you think you aren’t, you’re probably fooling yourself. If you’re on TV a lot, you’re almost definitely fooling yourself.

There are many examples of how real Oblonskys, who control practically all important cultural institutions, think. They believed things about Stalin’s regime so outlandish that it is hard to appreciate now. They were more in favour of Britain joining the euro, not because they understood ‘the complexities’ better but because they were suckered into thinking about it as a moral test – are you on the side of the ‘baddies’ or the goodies’? As the BBC Europe editor said to me back then, in similar terms to Matthew Parris about the 2016 referendum, ‘the thing is Dominic, we like foreigners and cappuccinos and we hate racists’. Polls show that better educated people are less likely to have accurate views about the science of evolution and genetics (their desire to send moral signals suckers them into believing fairy tales).

The conformity of the educated is in some ways a good thing – most obviously, a basic consensus about things like not killing one’s domestic opponents that is extremely unusual historically. But it has many bad effects too. There is a collective lack of imagination which makes the system very susceptible to disastrous shocks. They share a narrow set of ideas about how the world works which mistakes their own view as the only possible sensible approach. They are aways writing about how ‘shocking’ things are to them – things that never were as low probability events as they imagine.  They can’t imagine something like Stalin deliberately creating a famine or deliberately murdering millions. They tell themselves that Hitler will be ‘more sensible in power’ and ‘engagement’ is the right path. Western liberals (like Clinton and many pro-euro campaigners) and conservatives (like Bush) talked of relations with Putin as if he is a normal western politician rather than an ex-KGB mafia overlord with views very far from western liberals. They tell each other ‘I can’t imagine President Trump, it just can’t happen’. Many conservatives are now telling themselves that they should not take Trump too literally but that too is a failure of imagination – his character is clear to those unblinded by gang mentality and he will govern in character.

The referendum was a great example of this. Large numbers of people better educated than average – the sort of people who work as producers at the BBC – talked about their vote like this:

‘Farage is racist, he hates gay people and made that comment about foreigners with HIV, he wants to turn the clock back and pull the drawbridge up, I’m not like that, my friends aren’t like that, I am on the other side to people like that, I am tolerant and modern, I will vote IN.’

All over the country sentiments almost identical to this were expressed in large numbers. The idea that millions of graduates voted because they ‘studied the issues’ is laughable to anybody who spent time measuring opinion honestly. Almost none of these people know more about what a Customs Union is than a bricky in Darlington. They did not vote on the basis of thinking hard about the dynamics of EMU or about how Brussels will cope with issues like gene drives. Millions thought – there’s two gangs and I know which one I’m in. Another subset of the better educated feared the short-term economic disruption of a Leave vote would cost them money. They also did not vote on the basis of deep consideration of the issues.

The modern day Oblonsky reads an op-ed about how ‘the CBI warns of the dangers of leaving the Single Market’ and ‘the dangers of racist extremists’ and, having no idea of what ‘the Single Market’ is, jabbers away at their dinner party about how concerned they are about leaving ‘the Single Market’, and a warm haze of knowing one is on the ‘good’ side of the argument envelops the brain.

When it comes to the central issues of the nature of the EU’s trading relationships and what a UK-EU relationship might look like outside the EU, we are dealing with a particularly strong example of this phenomenon. Not only do the Oblonskys not know what they are talking about, neither do almost any of the supposed experts and specialists.

Lots of people said to me ‘when are you going to set out the details of the UK-EU trade relationship if you win?’ What would have been the point of that?! Approximately nobody knows anything about the important details of how the EU works including the MPs who have spent years talking about it and the journalists who cover it – indeed, often those who talk about it most are the most ignorant (and most overconfident). This is still true six months after the vote – imagine how much more true it was in the six months before the vote.

I am not aware of a single MP or political journalist who understands the Single Market – its history, its nature, its dynamics, its legal system, the complex interactions between law, economics, business, history and so on. Cameron, Osborne and Clegg certainly don’t. Neither does Bill Cash. Neither does any head of the CBI. Neither do Jon Snow, Robert Peston, Evan Davis or John Humphreys so they do a rubbish job of exposing politicians’ ignorance.

The number of people who do is tiny. In our campaign there were two – Oliver Lewis and Richard Howell – who understood a large fraction of it and the common misconceptions. They constantly had to explain to MPs, MEPs, and journalists why their ideas were misunderstandings. Maybe there is a business/economics journalist somewhere who really understands it. There are certainly some exceptional lawyers who understand narrow aspects extremely well, though few of these also understand the political and business dimensions. I have spoken to many very successful business people and never met one on either side who understands the Single Market in depth. In the entire campaign I am not aware of a single programme on TV that even tried to delve into these issues seriously (Newsnight was particularly bad, combining smugness and vapidity such as dropping Evan Davis by helicopter on an offshore platform to babble about ‘sovereignty’ trying to make the Leave side look like a bunch of weirdo cranks). British elites handed over power to the Monnet-Delors project with barely one-in-a-thousand understanding in detail why, what it involved, and its likely evolution (and that  one-in-a-thousand almost all concluded that the public could not be trusted to know the truth – I’ll explore another time the ideas of this tiny group).

Further, it was clear that Cameron/Osborne intended to run a campaign based on hysterical warnings and bogus arguments/figures while ignoring the big questions about how the EU works and its trajectory. No10 tried to turn the whole complex issue into a question about whether the economy would grow a little bit slower over the next few years – a trivial issue relative to the significance of the overall question. They are not a duo who have ever engaged the public on a serious matter in a serious way. Their brains don’t work like that. They formed early habits of looking at everything through a very narrow prism of SW1 conventional political wisdom. Given this, the way the media works, how outnumbered we were among the influential broadcast media, and the way in which the media (inevitably to some extent) takes its lead from No10, why would I have tried to run a campaign based on educating normal people to a far higher level than the professionals and ‘experts’ who were fighting and covering the campaign? It would have been impossible to get even two sensible MPs to explain the same complex argument about such things on TV without cocking it up – it was hard enough to get people to say ‘let’s spend our money on our priorities’ without days of arguing. (If the vote had happened in 2017 and we’d had all that time to build sensibly more could have been done.)

We did try to get the media to focus on deeper questions of how the EU is run, its problems, its evolution and so on. We knew from the research that the more coverage of the EU, its powers, its record, its plans and so on the better for us. We had little to fear from serious policy discussion and much to gain. But we largely failed. (A big speech from Gove was turned by the Financial Times – yes, the FT that bemoaned the ‘low quality debate’ – into a story about whether he had ‘gaffed’ by mentioning Albania, though in plastering ‘Albania’ all over the place the FT accidentally helped us.) No10 calls up the BBC and says, ‘we’ve got a business letter tomorrow with dozens of household companies warning of Armageddon.’ If we published something worthy on the Eurozone’s debt and demographic nightmare, the structural problems of the Eurozone and implications of the Five Presidents’ Report, how far did this get? ‘Sounds boring. Who’s fronting it? Got any new names? Any chance of Boris putting the boot into Dave and George?’, is the first question from the BBC TV producer who has no interest in ‘the arguments’.

It was not in our power to change basics of how the media works. We therefore  twisted them to our advantage to hack the system.

Hack the medium, hack the message: ‘the alternative government’ launches Sunday 29 May

The media is obsessed with process and the snakes and ladders of careers. Many hacks said to me words to the effect: ‘I don’t care about the issues, I care about whether Cameron will still be PM at the end of the year.’ We could not match No10 in the golden currency of ‘names’. But we could give the media an even more valuable currency – a leadership story. When Boris and Gove decided to go for it after 29 May  immigration numbers, we launched the story of ‘the alternative government’.

The media were understandably obsessed with this story so we served it up to them in such a way that they also had to cover our message. For 10 days, we dominated the news with a set of stories on the Australian points system, VAT on fuel, Turkey, the NHS and so on all based on ‘it’s safer to take back control’. Broadcasters lapped it up – even ITV News which barely pretended to be impartial was useful.

What did the public hear? They heard that prominent Conservatives, particularly Boris and Gove, did not trust Cameron’s promises or warnings and that there was an alternative path – we could ‘take back control’, have ‘an Australian style immigration system’, and ‘spend our money on our priorities like the NHS’. In an environment in which the central arguments concerning trade and the economy were incomprehensible to the ‘experts’ themselves and the history and dynamics of the EU either unknown to or suppressed by broadcasters, people chose between two simple stories. Vote Leave’s was more psychologically compelling, given the three powerful forces at work and No10’s errors.

(NB. Whoever leaked the Hilary email story was probably doing something similar. This played into the media obsession with scandal and process such that they spent a ridiculous amount of time on it despite probably 80% of them wanting Hilary to win. It shows how powerfully the media is in the grip of dynamics they rarely reflect on themselves. Putin’s communication maestro, Surkov, uses these sorts of tricks all the time. Cf. Peter Pomerantsev’s great book, a must read for any MP before they pontificate on Putin’s mafia government.)

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The political media and how to improve it

High prestige pundits and editors yield great power over the stories told (and have far more power over politicians like Cameron, unfortunately, than they realise) but the field is not based on real expertise. Fields dominated by real expertise are distinguished by two features: 1) there is enough informational structure in the environment such that reliable predictions are possible despite complexity and 2) there is effective feedback so learning is possible.

Neither condition applies generally to politics or the political media. In the most rigorous studies done, it has been shown that in general political experts are little better than the proverbial dart throwing chimp and that those most confident in their big picture views and are most often on TV  – people like Robert Peston, Jon Snow, and Evan Davis – are the least accurate political ‘experts’ (cf. HERE).

We know that cognitive diversity is vital for political accuracy yet almost all political institutions and the media – including the dominant people at Newsnight, the Economist, the FT, and Parliament – are actually remarkably homogenous, as discussed above, and they herd around very similar ideas about how the world works. Scientists and entrepreneurs in particular are almost totally excluded from political influence.

There is no structure to hold them to account either internally or externally so, like anyone when not forced to be rigorous, they fool themselves. It is normal to write month after month that the IN campaign cannot lose because of XYZ then just as confidently and authoritatively explain why IN lost without any intermediate step of identifying and explaining errors.

Despite the rise of social media most people get most of their news from TV. TV coverage of politics rarely illuminates much because there is no clear way to decide who is right about anything. The format makes it almost impossible for any useful discussion to happen. Interviewers, politicians, and pundits talk past each other with no clarity about assumptions. Questions are vague, often meaningless, posed by interviewers who rarely have more than a thin bluffer’s understanding of any policy issue and the same is usually true of those answering; the more famous the interviewer, the less likely it is they know anything about, say, education policy and like David Cameron they are bluffing. (As soon as a story is deemed ‘political’ it is taken out of the hands of specialists (who are very rarely actually specialists anyway) and given to ‘political’ hacks who have no idea of the policy.) Most of those professionally involved are much more interested in the ‘horse race’ political dimension than the policy. They obsess on process and scandal but most people have no interest in the process or ‘scandals’  because they assume ‘they’re all dodgy in some way’. Nobody tries to make predictions that can be checked and the shows don’t take what is said seriously enough to catalogue it. Simplistic stories compete so political analysis is dominated by endless false dichotomies.

Those making the shows do not understand how people learn so the dead format recycles grim clichés like Evan Davis saying ‘… economy down the plug hole’, while filming an actual plug hole, or Nick Robinson saying ‘… will the economy take off’ standing in front of a plane actually taking off (both of these have happened). Every night the News contains reports that are a mix of incomprehensible, facile, and boring to millions while also usually at best simplistic and often just wrong when it comes to policy / issues. The possibilities of the medium are largely ignored.

Insiders think of the masses as being irrational in paying so little attention to political debate. I think they are rational. If you want to understand politics you should read serious things and invest time and effort in researching public opinion. You should particularly make an effort to invert your point of view and consider opinions very different to your own. Time spent watching/listening to shows like Newsnight and Today is not just wasted – it is actively distorting reality and making you less informed. I often meet people who are cleverer than those in politics and successful but they have deluded views about politics because they pay too much attention to political analysis. Overall, unless you are professionally involved in politics you will be better off if you stop >95% active reading of political analysis. You will miss occasional worthwhile things but the effort of sorting them is not worth it. If something is genuinely very good / unusual and you have avoided isolating yourself in an echo chamber that insulates you from opinions very different to your own then someone reliable will send it to you. Even if you are professionally involved in politics I would do roughly the same. Extreme focus on important things you can control will repay far far more than time spent reading speculation about things you can’t control.

I read very little punditry during the campaign – just enough to preserve a sense of the gaps between the ‘croaking frogs’ and the real world. If I’d had less infighting to deal with I’d have read even less as I could have been less concerned about tracking certain things. In my entire time in the DfE (three years) I never listened to Today once (I listened to a handful of interviews on the web). I focused on managing priorities and saying ‘No, stop, that’s a waste of time’ every day.

This situation is particularly ironic because the media industry is in a panic about the internet, falling ad revenues and profits, the collapse of print journalism and so on.

A better way…

There is a better way.

Example 1. Shows should require precise quantitative predictions about well-formed questions as Superforecasters do. Newspapers should do the same when interviewing people. The next step is using this process to push people towards admitting conditional errors like ‘if I am proved wrong about X by date Y then I will admit I was wrong to claim Z’. If political shows pushed their guests to do this and kept track of the predictions it could have a big positive effect. (Next time they come on you can flash up their record on a screen so the public can see how often they are right.) It is vital to change incentives so people are encouraged to admit errors and learn instead of fooling themselves constantly. For those who refuse it would be easy to develop a protocol that categorises their vague comments and puts numbers on them. This will push them to ‘correct the record’.

Example 2. Rip up the format for political shows and base broadcasts on a) an empirical assessment of what people actually know and b) the science of how people really learn and how best to communicate. Instead of the tedious low-information interviews, imagine what could be done if one had a mix of artists, scientists, and policy specialists trying really hard to use the possibilities of film to explain things, then used cutting edge data science to test how effective they were as part of a learning cycle driving higher quality. A news broadcast now contains much less  information content and much higher noise than reading. The only way to improve this is experimenting with formats in a scientific way. Doing this would force those making the news to think more about policy and the audience would be much more engaged. People are interested in policy and ‘how X will affect me, my family, and my community’. It would also obviously require a lot of changes in the media but this is coming anyway because existing business models are blowing up.

Example 3. Pay for this partly by firing most of your political commentators like Dan Hodges. Broadcasters, fire 90% of your political correspondents. They are a waste of money. Hire a much smaller number of much better people with radically different skills and backgrounds and a different focus. (By doing #1 you will soon see who is more/less accurate so you’ll have a good benchmark.)

Together these changes could improve the quality by a factor of x10 or more. The same principle of focusing on precise quantitative predictions about well-formed questions could also be used to improve policy making and management of bureaucracies by developing clusters of well-formed questions that ‘surround’ a vaguer big question that is not so susceptible to measurement.

For example, break down ‘will Britain leaving the EU be a success?‘ into dozens of simpler more precise questions that can be quantified and which together give a useful part of an overall answer. This process could be put on a prediction platform for little money and dramatically improve the quality of decisions. The Superforecasters new consultancy could do this pretty simply with little help and not much money.

As usual in systems that are failing, the youngest people understand the problems and possibilities best while the most senior / prestigious figures can’t think of anything to do other than get overpaid for what they’ve always done. If you run a big media company, you should replace the expensive old schoolers like Jon Snow with  younger, cheaper, and brighter new schoolers with an extreme focus on the public, not SW1.

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An example of a simple, powerful media story that is wrong and contributed to forecasting errors on Brexit – ‘the centre ground’

One of the most misleading stories in politics is the story of ‘the centre ground’. In this story people’s views are distributed on an X-axis with ‘extreme left’ at one end, ‘extreme right’ at the other end, and ‘the centre ground’ in the middle. People in ‘the centre’ are ‘moderate’. ‘Extremists’ are always ‘lurching’ while ‘sensible moderates’ are urged to ‘occupy the centre’.

This story is one of the dominant features of political discussion and the basis for endless interviews, columns, and attempts at political ‘strategy’. The story is deeply flawed and where it is not trivially true it is deeply misleading.

Swing voters who decide elections – both those who swing between Conservative/Labour and those who swing between IN/OUT – do not think like this. They support much tougher policies on violent crime than most Tory MPs AND much higher taxes on the rich than Blair, Brown, and Miliband. They support much tougher anti-terrorism laws than most Tory MPs AND they support much tougher action on white collar criminals and executive pay than Blair, Brown, and Miliband.

One of the key delusions that ‘the centre ground’ caused in SW1 concerned immigration. Most people convinced themselves that ‘swing voters’ must have a ‘moderate’ and ‘centre ground’ view between Farage and Corbyn. Wrong. About 80% of the country including almost all swing voters agreed with UKIP that immigration was out of control and something like an Australian points system was a good idea. This was true across party lines.

This was brought home to me very starkly one day. I was conducting focus groups of Conservative voters. I talked with them about immigration for 20 minutes (all focus groups now start with immigration and tend to revert to it within two minutes unless you stop them). We then moved onto the economy. After two minutes of listening I was puzzled and said – who did you vote for? Labour they all said. An admin error by the company meant that I had been talking to core Labour voters, not core Tory voters.  On the subject of immigration, these working class / lower middle class people were practically indistinguishable from all the Tories and UKIP people I had been talking to.

The media tried to categorise Vote Leave as ‘right wing’ while Tory MPs and Farage’s gang were screaming at me about our championing the NHS and our attacks on the indefensible pay of FTSE CEOs. SW1 did not understand our appeal but the crucial voters did because they do not think as the ‘experts’ think they think. We tried to speak to a majority in the country. Cameron and Osborne have never won even 40%. They approached it as they did previous battles but this greatly limited their appeal. Most UKIP and Tory voters (rather than MPs/insiders) agreed with us on the NHS and executive pay while also agreeing with us on the need to take back control of immigration policy from a system that has obviously failed. Our campaign was neither Left nor Right in the eyes of the crucial audience.

The media made a similar mistake with Trump. Trump did lots of things wrong and the post facto re-branding of his campaign as ‘brilliant’ is very silly. BUT he had a national message the core of which appealed to a big majority and which defied categorisation as Left/Right. Again the media do not realise this – they label it, like Vote Leave, as ‘populist right’ (abetted by some charlatan academics). But the reason why it is successful is exactly because it is not a simple right-wing message.

It doesn’t occur to SW1 and the media that outside London their general outlook is seen as extreme. Have an immigration policy that guarantees free movement rights even for murderers, so we cannot deport them or keep them locked up after they are released? Extreme. Have open doors to the EU and don’t build the infrastructure needed? Extreme. Take violent thugs who kick women down stairs on CCTV, there is no doubt about their identity, and either don’t send them to jail or they’re out in a few months? Extreme. Have a set of policies that stops you dealing with the likes of ‘the guy with the hook’ for over a decade while still giving benefits to his family? Extreme. Ignore warnings about the dangers of financial derivatives, including from the most successful investor in the history of the world, and just keep pocketing the taxes from the banks and spending your time on trivia rather than possible disasters? Extreme. Make us – living on average wages without all your lucky advantages – pay for your bailouts while you keep getting raises and bonuses? Extreme and stupid – and contemptible.

These views are held across educational lines, across party lines, and across class lines. Cameron, Blair, and Evan Davis agree about lots of these things and tell people constantly why they are wrong to think differently but to millions they are the extremists.

(This is not a post facto rationalisation. I wrote about the centre ground and the EU in 2014 HERE.)

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Why I got involved and my role

Winning the referendum against Cameron was not the way I wanted things to happen. I thought the chances of winning a referendum against a PM on the other side, with all the possibilities for him to mobilise the system behind IN, were low. Many prominent Eurosceptics (not all) lobbied for it out of a combination of self-promotion and not knowing how to solve the real problem – what should the UK-EU relationship be? The referendum was very useful for many Out-ers: it provided a much simpler political focus than figuring out a complex positive agenda, removed the need for difficult thinking and action, and gave people a chance to pose on the side of ‘democracy’. I thought it foolish to push for a referendum while simultaneously not building a serious movement to win it. (I had tried to start building such a movement in 2004 after the euro battle was clearly won but could not persuade crucial people so decided to drop the issue for a while.) Romantic long shots are rarely wise in politics particularly if there is a better path.

I thought it wiser and safer to wait for Cameron to go then try to capture the Tory leadership and change the UK-EU relationship from Downing Street with someone who actually wanted to solve the problems (Cameron’s best friends would not claim that he wanted to spend his time trying to solve these deep problems, he wanted not to think about the EU and got into an existential battle he never wanted). If you are going to have a referendum, then have it when controlling the institutions and when you can set the agenda. A British PM could invite the EU to evolve such as to include a) those in the euro, Single Market and ‘free movement’, and b) those outside some or all of those three but with free trade and friendly cooperation between all. The chances are low that there would have been support for fundamental change but then a divorce could have happened after a serious clarifying debate which would have occurred ~2018-25, including the Eurozone countries figuring out what they would do. This would have been a much better way to proceed than the charade of Cameron’s ‘renegotiation’. Either Europe would have embraced a new and more open architecture (unlikely) or the Government would have won a Leave referendum with 60-70% and prompted a lot of clarifying thought across Europe.

I also thought it foolish of Cameron to cave into the pressure and promise a referendum in 2013. So did Gove and Osborne both of whom told Cameron not to do it. He mistakenly thought it would take the wind out of UKIP’s sails and did not understand why it would actually boost UKIP and Farage. (This was not hard to foresee and I suspect part of the problem was that Cameron did not appreciate that him promising a referendum would be thought by most as just a typical pre-election lie.) The idea that there was an irresistible force for a referendum is pushed by Farage’s and Cameron’s supporters. They are both wrong. The country supported one but without any passion outside the small fraction who had long been passionate about it. Most Tory MPs did not want it. Most Tory donors thought the timing was wrong and wanted a focus on stopping Miliband who they feared. Those MPs who did want it could mostly have been bought off or distracted in other ways – a mix of some policy, gongs, bribes, and so on in the usual fashion. Putting a date on the vote was particularly mistaken – it would have been far better to leave it open-ended ‘in the next Parliament’.

Once the election happened there was a sudden panic among OUT-ers. UKIP was an organisational disaster. There was no national campaign prepared. There were many tiny groups who often hated each other more than they wanted to win and were conditioned to expect failure and defeat. There was an abundance of people who thought that the campaign was quite simple – put me on TV, they thought, and the nation will appreciate my natural leadership. There was practically nothing of what was actually needed. Many quickly flipped into panic mode assuming the vote was unwinnable.

Having opposed the push for a referendum, I was faced with an uncomfortable choice in May 2015. Either keep out of politics, refuse to help, and then feel miserable about the tragicomic campaign, or re-engage with people I did not want to work with, feel miserable about the tragicomic campaign, and in almost every way make my life worse. In many ways irrationally, I chose the latter. My thinking was something like this: the chance of changing the whole political system (more profoundly than in a normal election) comes along very rarely, the chaos of the eurosceptics and the complacency of Cameron creates a very slim bridge to seize control and do it, a small chance of very high impact is worth the gamble. About a month or so later my wife was pregnant. If the timing had been slightly different I might well have stayed retired.

Why do it?

I thought that Leaving would improve the probability of 1) Britain contributing positively to the world and 2) minimising dangers. I thought it would:

  1. minimise Britain’s exposure to the problems caused by the EU;
  2. improve the probability that others in Europe would change course before more big crises hit, e.g. by limiting free movement which is the biggest threat to continued free trade;
  3. require and therefore hopefully spark big changes in the fundamental wiring of UK government including an extremely strong intelligent focus on making Britain the best place in the world for science and education;
  4. improve the probability of building new institutions for international cooperation to minimise the probability of disasters.

The foundation problem with the EU was best summarised by the brilliant physicist David Deutsch, the man who extended Alan Turing’s 1936 paper on computation into the realm of quantum mechanics. Deutsch said:

‘The EU is incompatible with Britain’s more advanced political culture. I’m voting Leave… [E]rror correction is the basic issue, and I can’t foresee the EU improving much in this respect… [P]reserving the institutions of error correction is more important than any policy… Whether errors can be corrected without violence is not a “concern” but a condition for successfully addressing concerns.’

Healthy and effective systems like our immune system and the English common law allow constant and rapid error-correction. Unhealthy and ineffective systems like the EU and modern Whitehall departments block error-correction. They are extremely centralised and hierarchical therefore information processing is blocked and problems are not solved. In politics this often leads to disasters when more and more resources are devoted to reinforcing failure. NB. This most fundamental question played effectively no role in the debate.

This fundamental problem generates its other problems. It arises because of how Monnet and Delors created its institutions deliberately in opposition to the Anglo-American system they bitterly opposed. The Foreign Office romantic delusion of ‘influence’ was peddled by every PM since Thatcher. Every one left office having demonstrated how empty the hope is. True influence comes from demonstrating success – not sitting in meetings for forty years in an institution that is programmed on principles that guarantee worse error-correction than the evolved institutions  of the Anglo-American system.

I will go into the problems of the EU another time. I will just make one important point here.

I thought very strongly that 1) a return to 1930s protectionism would be disastrous, 2) the fastest route to this is continuing with no democratic control over immigration or  human rights policies for terrorists and other serious criminals, therefore 3) the best practical policy is to reduce (for a while) unskilled immigration and increase high skills immigration particularly those with very hard skills in maths, physics and computer science, 4) this requires getting out of the EU, 5) hopefully it will prod the rest of Europe to limit immigration and therefore limit the extremist forces that otherwise will try to rip down free trade.

One of our campaign’s biggest failures was to get even SW1 to think seriously about this, never mind millions of voters. Instead the false idea spread and is still dominant that if you are on the side of free trade, think controlled immigration generally a positive force, and want more international cooperation rather than a return to competing nation states then you must support the EU. I think this error is caused by the moral signalling and gang mentality described above.

What was my role?

My role mainly involved:

a) trying to suppress/divert/overcome internal coalition warfare to a level where about ten crucial people were protected enough to do their jobs,

b) building the team,

c) management,

d) taking a small number of important decisions about policy, message, money, and the machine,

e) providing clear focus and priorities, including the vital job that nobody likes of saying ‘no’ to hundreds of people (thus making (a) harder), and

f) dealing with big problems.

The media tends to suggest my role was mainly talking to them. This is wrong. The same happened with my role in the DfE. In both projects my main role was management. Serious management means extreme focus and this requires saying No an awful lot. Contrary to the media story, I dislike confrontation and rows like most people but I am very strongly motivated by doing things in a certain way and am not motivated by people in SW1 liking me. This is often confused with having a personality that likes fighting with people. One of the basic reasons so much in politics is mismanaged is that so often those responsible are more interested in social relations than in results and unlike in other more successful fields the incentives are not structured to control this instinct.

Many have written that I got involved with this because of ‘hate’ or ‘loathing’ for Cameron. Wrong. I do not hate Cameron. I do not respect him, which is different. I thought that he was in politics for bad reasons – essentially because he was someone who wanted ‘To Be’, not someone who wanted ‘To Do’ (see the Colonel Boyd speech) and his priority was himself and a small gang, not the public. I also thought Cameron was mostly (not all) bad at the job, despite having some of the  necessary temperamental characteristics, and was flattered by having Brown then Miliband as opponents. I didn’t object to him blocking me from Government in 2010. He was entitled not to hire someone who did not take him seriously and ignored the orders of his Chief of Staff.

I spent a few years of my life (1999-2002) trying to stop Blair on the euro before anyone had heard of Cameron. In 2004 I co-founded the campaign that won the referendum on the North East Regional Assembly 80-20 as a training exercise for a possible future EU referendum. My motivation was the issue itself – not personal antipathy for Cameron or anybody else. I’ve never been a party person. I’m not Tory, libertarian, ‘populist’ or anything else. I follow projects I think are worthwhile.

Farage’s motley crew claim that I did this campaign in order to lose it deliberately then get a job in No 10 with Cameron. It is pointless to discuss this theory though the fact that they understood so little about the political environment, and struggled to use Google, was an important fact.

I am not clever, I have a hopeless memory, and have almost no proper ‘circle of competence’. I made lots of mistakes in the campaign. I have had success in building and managing teams. This success has not relied on a single original insight of any kind. It comes from applying what Charlie Munger calls unrecognised simplicities of effective action that one can see implemented by successful people/organisations.

Effective because they work reliably, simple enough that even I could implement them, and ‘unrecognised’ because they are hiding in plain sight but are rarely stolen and used. I found 10-15 highly motivated people who knew what they were doing and largely left them to get on with it while stopping people who did not know what they were doing interfering with them, we worked out a psychologically compelling simple story, and we applied some simple management principles that I will write about another time. It is hard to overstate the relative importance in campaigns of message over resources. Our success is an extreme example given the huge imbalance in forces on either side. In many ways Trump’s victory has little resemblance to what we did but in this respect he is another example.

We also got lucky.

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I will post a number of blogs of the referendum to try to answer some basic questions including:

What were the main political, operational, financial/budgetary, and data/digital lessons from the campaign?

What worked and did not work?

How confident can we be about these judgements?

There is a natural set of categories and I will post links to blogs below:

  • Some basic numbers that summarise important elements.
  • Strategy, message, polls.
  • Policy.
  • Data and digital.

On data science, digital marketing, canvassing software made available for download. (NB. There has been some confusion about this blog. The VICS system is a web-based canvassing tool, the first proper one that works in the UK – it was one component of our overall data science approach and should not be equated with it. It is not a data science tool – it provided data to the data science team.)

  • The ground campaign.
  • The media.
  • Internal politics and the infighting.
  • Dynamics that affect ‘what next’.
  • The rules: how could they be improved to make future votes serve the public better?

Please leave comments and corrections below. I am happy to approve hostile comments if they have substance and will moderate comments to avoid putting sensible people off reading them.

A review of Tetlock’s ‘Superforecasting’ (2015)

Spectator Review, October 2015

Forecasts have been fundamental to mankind’s journey from a small tribe on the African savannah to a species that can sling objects across the solar system with extreme precision. In physics, we developed models that are extremely accurate across vastly different scales from the sub-atomic to the visible universe. In politics we bumbled along making the same sort of errors repeatedly.

Until the 20th century, medicine was more like politics than physics. Its forecasts were often bogus and its record grim. In the 1920s, statisticians invaded medicine and devised randomised controlled trials. Doctors, hating the challenge to their prestige, resisted but lost. Evidence-based medicine became routine and saved millions of lives. A similar battle has begun in politics. The result could be more dramatic.

In 1984, Philip Tetlock, a political scientist, did something new – he considered how to assess the accuracy of political forecasts in a scientific way. In politics, it is usually impossible to make progress because forecasts are so vague as to be useless. People don’t do what is normal in physics – use precise measurements – so nobody can make a scientific judgement in the future about whether, say, George Osborne or Ed Balls is ‘right’.

Tetlock established a precise measurement system to track political forecasts made by experts to gauge their accuracy. After twenty years he published the results. The average expert was no more accurate than the proverbial dart-throwing chimp on many questions. Few could beat simple rules like ‘always predict no change’.

Tetlock also found that a small fraction did significantly better than average. Why? The worst forecasters were those with great self-confidence who stuck to their big ideas (‘hedgehogs’). They were often worse than the dart-throwing chimp. The most successful were those who were cautious, humble, numerate, actively open-minded, looked at many points of view, and updated their predictions (‘foxes’). TV programmes recruit hedgehogs so the more likely an expert was to appear on TV, the less accurate he was. Tetlock dug further: how much could training improve performance?

In the aftermath of disastrous intelligence forecasts about Iraq’s WMD, an obscure American intelligence agency explored Tetlock’s ideas. They created an online tournament in which thousands of volunteers would make many predictions. They framed specific questions with specific timescales, required forecasts using numerical probability scales, and created a robust statistical scoring system. Tetlock created a team – the Good Judgement Project (GJP) – to compete in the tournament.

The results? GJP beat the official control group by 60% in year 1 and by 78% in year 2. GJP beat all competitors so easily the tournament was shut down early.

How did they do it? GJP recruited a team of hundreds, aggregated the forecasts, gave extra weight to the most successful, and applied a simple statistical rule. A few hundred ordinary people and simple maths outperformed a bureaucracy costing tens of billions.

Tetlock also found ‘superforecasters’. These individuals outperformed others by 60% and also, despite a lack of subject-specific knowledge, comfortably beat the average of professional intelligence analysts using classified data (the size of the difference is secret but was significant).

Superforecasters explores the nature of these unusual individuals. Crucially, Tetlock has shown that training programmes can yield big improvements. Even a mere sixty minute tutorial on some basics of statistics improves performance by 10%. The cost:benefit ratio of training forecasting is huge.

It would be natural to assume that this work must be the focus of intense thought and funding in Whitehall. Wrong. Whitehall has ignored this entire research programme. Whitehall experiences repeated predictable failure while simultaneously seeing no alternative to their antiquated methods, like 1950s doctors resisting randomised control trials that threaten prestige.

This may change. Early adopters could use Tetlock’s techniques to improve performance. Success sparks mimicry. Everybody reading this could do one simple thing: ask their MP whether they have done Tetlock’s training programme. A website could track candidates’ answers before the next election. News programmes could require quantifiable predictions from their pundits and record their accuracy.

We now expect that every medicine is tested before it is used. We ought to expect that everybody who aspires to high office is trained to understand why they are so likely to make mistakes forecasting complex events. The cost is tiny. The potential benefits run to trillions of pounds and millions of lives. Politics is harder than physics but Tetlock has shown that it doesn’t have to be like astrology.

Superforecasting: the art and science of prediction, by Philip Tetlock (Random House, 352 pages)

Ps. When I wrote this (August/September 2015) I was assembling the team to fight the referendum. One of the things I did was hire people with very high quantitative skills, as I describe in this blog HERE.

On the referendum #20: the campaign, physics and data science – Vote Leave’s ‘Voter Intention Collection System’ (VICS) now available for all

‘If you don’t get this elementary, but mildly unnatural, mathematics of elementary probability into your repertoire, then you go through a long life like a one-legged man in an ass-kicking contest. You’re giving a huge advantage to everybody else. One of the advantages of a fellow like Buffett … is that he automatically thinks in terms of decision trees and the elementary math of permutations and combinations… It’s not that hard to learn. What is hard is to get so you use it routinely almost everyday of your life. The Fermat/Pascal system is dramatically consonant with the way that the world works. And it’s fundamental truth. So you simply have to have the technique…

‘One of the things that influenced me greatly was studying physics… If I were running the world, people who are qualified to do physics would not be allowed to elect out of taking it. I think that even people who aren’t [expecting to] go near physics and engineering learn a thinking system in physics that is not learned so well anywhere else… The tradition of always looking for the answer in the most fundamental way available – that is a great tradition.’ Charlie Munger, Warren Buffet’s partner.

During the ten week official campaign the implied probability from Betfair odds of IN winning ranged between 60-83% (rarely below 66%) and the probability of OUT winning ranged between 17-40% (rarely above 33%). One of the reasons why so few in London saw the result coming was that the use by campaigns of data is hard to track even if you know what to look for and few in politics or the media know what to look for yet. Almost all of Vote Leave’s digital communication and data science was invisible even if you read every single news story or column ever produced in the campaign or any of the books so far published (written pre-Shipman’s book).

Today we have made a software product available for download – Vote Leave’s ‘Voter Intention Collection System’ (VICS) – click HERE. It was named after Victoria Woodcock, Operations Director, known as Vics, who was the most indispensable person in the campaign. If she’d gone under a bus, Remain would have won. When comparing many things in life the difference between average and best is say 30% but some people are 50 times more effective than others. She is one of them. She had ‘meetings in her head’ as people said of Steve Wozniak. If she had been Cameron’s chief of staff instead of Llewellyn and Paul Stephenson had been director of communications instead of Oliver and he’d listened to them, then other things being equal Cameron would still be on the No10 sofa with a glass of red and a James Bond flick. They were the operational/management and communications foundation of the campaign. Over and over again, those two – along with others, often very junior – saved us from the consequences of my mistakes and ignorance.

Among the many brilliant things Vics did was manage the creation of VICS. When we started the campaign I had many meetings on the subject of canvassing software. Amazingly there was essentially no web-based canvassing software system for the UK that allowed live use and live monitoring. There have been many attempts by political parties and others to build such systems. All failed, expensively and often disastrously.

Unfortunately, early on (summer 2015) Richard Murphy was hired to manage the ground campaign. He wanted to use an old rubbish system that assumed the internet did not exist. This was one of the factors behind his departure and he decided to throw in his lot with Farage et al. He then inflicted this rubbish system on Grassroots Out which is one of the reasons why it was an organisational/management disaster and let down its volunteers. After Vote Leave won the official designation, many GO activists defected, against official instructions from Farage, and plugged into VICS. Once Murphy was replaced by Stephen Parkinson (now in No10) and Nick Varley, the ground campaign took off.

We created new software. This was a gamble but the whole campaign was a huge gamble and we had to take many calculated risks. One of our central ideas was that the campaign had to do things in the field of data that have never been done before. This included a) integrating data from social media, online advertising, websites, apps, canvassing, direct mail, polls, online fundraising, activist feedback, and some new things we tried such as a new way to do polling (about which I will write another time) and b) having experts in physics and machine learning do proper data science in the way only they can – i.e. far beyond the normal skills applied in political campaigns. We were the first campaign in the UK to put almost all our money into digital communication then have it partly controlled by people whose normal work was subjects like quantum information (combined with political input from Paul Stephenson and Henry de Zoete, and digital specialists AIQ). We could only do this properly if we had proper canvassing software. We built it partly in-house and partly using an external engineer who we sat in our office for months.

Many bigshot traditional advertising characters told us we were making a huge error. They were wrong. It is one of the reasons we won. We outperformed the IN campaign on data despite them starting with vast mounts of data while we started with almost zero, they had support from political parties while we did not, they had early access to the electoral roll while we did not, and they had the Crosby/Messina data and models from the 2015 election while we had to build everything from scratch without even the money to buy standard commercial databases (we found ways to scrape equivalents off the web saving hundreds of thousands of pounds).

If you want to make big improvements in communication, my advice is – hire physicists, not communications people from normal companies and never believe what advertising companies tell you about ‘data’ unless you can independently verify it. Physics, mathematics, and computer science are domains in which there are real experts, unlike macro-economic forecasting which satisfies neither of the necessary conditions – 1) enough structure in the information to enable good predictions, 2) conditions for good fast feedback and learning. Physicists and mathematicians regularly invade other fields but other fields do not invade theirs so we can see which fields are hardest for very talented people. It is no surprise that they can successfully invade politics and devise things that rout those who wrongly think they know what they are doing. Vote Leave paid very close attention to real experts. (The theoretical physicist Steve Hsu has a great blog HERE which often has stuff on this theme, e.g. HERE.)

More important than technology is the mindset – the hard discipline of obeying Richard Feynman’s advice: ‘The most important thing is not to fool yourself and you are the easiest person to fool.’ They were a hard floor on ‘fooling yourself’ and I empowered them to challenge everybody including me. They saved me from many bad decisions even though they had zero experience in politics and they forced me to change how I made important decisions like what got what money. We either operated scientifically or knew we were not, which is itself very useful knowledge. (One of the things they did was review the entire literature to see what reliable studies have been done on ‘what works’ in politics and what numbers are reliable.) Charlie Munger is one half of the most successful investment partnership in world history. He advises people – hire physicists. It works and the real prize is not the technology but a culture of making decisions in a rational way and systematically avoiding normal ways of fooling yourself as much as possible. This is very far from normal politics.

(One of the many ways in which Whitehall and Downing Street should be revolutionised is to integrate physicist-dominated data science in decision-making. There are really vast improvements possible in Government that could save hundreds of billions and avoid many disasters. Leaving the EU also requires the destruction of the normal Whitehall/Downing Street system and the development of new methods. A dysfunctional broken system is hardly likely to achieve the most complex UK government project since beating Nazi Germany, and this realisation is spreading – a subject I will return to.)

In 2015 they said to me: ‘If the polls average 50-50 at the end you will win because of differential turnout and even if the average is slightly behind you could easily win because all the pollsters live in London and hang out with people who will vote IN and can’t imagine you winning so they might easily tweak their polls in a way they think is making them more accurate but is actually fooling themselves and everybody else.’ This is what happened. Almost all the pollsters tweaked their polls and according to Curtice all the tweaks made them less accurate. Good physicists are trained to look for such errors. (I do not mean to imply that on 23 June I was sure we would win. I was not. Nor was I as pessimistic as most on our side. I will write about this later.)

VICS allows data to be input centrally (the electoral roll, which in the UK is a nightmare to gather from all the LAs) and then managed at a local level, whether that be at street level, constituency or wider areas. Security levels can be set centrally to ensure that no-one can access the whole database. During the campaign we used VICS to upload data models which predicted where we thought Leave voters were likely to be so that we could focus our canvassing efforts, which was important given limited time and resources on the ground. The model produced star ratings so that local teams could target the streets more likely to contain Leave voters.

Data flowed in on the ground and was then analysed by the data science team and integrated with all the other data streaming in. Data models helped us target the ground campaign resources and in turn data from the ground campaign helped test and refine the models in a learning cycle – i.e. VICS was not only useful to the ground campaign but also helped improve the models used for other things. (This was the point of our £50 million prize for predicting the results of the European football championships, which gathered data from people who usually ignore politics – I’m still frustrated we couldn’t persuade someone to insure a £350 million prize which is what I wanted to do.) In the official 10 week campaign we served about one billion targeted digital adverts, mostly via Facebook and strongly weighted to the period around postal voting and the last 10 days of the campaign. We ran many different versions of ads, tested them, dropped the less effective and reinforced the most effective in a constant iterative process. We combined this feedback with polls (conventional and unconventional) and focus groups to get an overall sense of what was getting through. The models honed by VICS also were used to produce dozens of different versions of the referendum address (46 million leaflets) and we tweaked the language and look according to the most reliable experiments done in the world (e.g. hence our very plain unbranded ‘The Facts’ leaflet which the other side tested, found very effective, and tried to copy). I will blog more about this.

These canvassing events represented 80-90% of our ground effort in the last few months, hence some of the reports by political scientists derived from Events pages on the campaign websites, which did not include canvassing sessions, are completely misleading about what actually happened (this includes M Goodwin who is badly confused and confusing, and kept telling the media duff information after he was told it was duff). There was also a big disinformation campaign by Farage’s gang, including Bone and Pursglove, who told the media ‘Vote Leave has no interest in the ground campaign’. This was the opposite of the truth. By the last 10 weeks we had over 12,000 people doing things every week (we had many more volunteers than this but the 12,000 were regularly active). When Farage came to see me for the last time (as always fixated only on his role in the debates and not the actual campaign which he was sure was lost) he said that he had 7,000 activists who actually did anything. He was stunned when I said that we had over 12,000. I think Farage et al believe their own spin on this subject and were deluded not lying. (Obviously there was a lot of overlap between these two figures.) These volunteers delivered about 70 million leaflets out of a total ~125 million that were delivered one way or another.

While there were some fantastic MPs who made huge efforts on the ground – e.g. Anne Marie Trevelyan – it was interesting how many MPs, nominally very committed to Leave, did nothing useful in their areas nor had any interest in ground campaigning and data. Many were far more interested in trying to get on TV and yapping to hacks than in gathering useful data, including prominent MPs on our Board and Campaign Committee, some of whom contributed ZERO useful data in the entire campaign. Some spent much of the campaign having boozy lunches with Farage gossiping about what would happen after we lost. Because so many of them proved untrustworthy and leaked everything I kept the data science team far from prying eyes – when in the office, if asked what they did they replied ‘oh I’m just a junior web guy’. It would have been better if we could have shared more but this was impossible given some of the characters.

VICS is the first of its kind in the UK and provided new opportunities. It is, of course, far from ideal. It was developed very quickly, we had to cut many corners, and it could be improved on. But it worked. Many on the ground, victims of previous such attempts, assumed it would blow up under the pressure of GOTV. It did not. It worked smoothly right through peak demand. This was also because we solved the hardware problem by giving it to Rackspace which did a great job – they have a system that allows automatic scaling depending on the demand so you don’t have to worry about big surges overwhelming the system.

There were many things we could have done much better. Our biggest obstacle was not the IN campaign and its vast resources but the appalling infighting on our own side driven by all the normal human motivations described in Thucydides – fear, interest, the pursuit of glory and so on. Without this obstacle we would have done far more on digital/data. Having seen what is offered by London’s best communications companies, vast improvements in performance are clearly possible if you hire the right people. A basic problem for people in politics is that approximately none have the hard skills necessary to distinguish great people from charlatans. It was therefore great good fortune that I was friends with our team before the campaign started.

During the campaign many thousands of people donated to Vote Leave. They paid for VICS. Given we spent a lot of money developing it and there is nothing equivalent available on the market and Vote Leave is no more (barring a very improbable event), we thought that we would make VICS available for anybody to use and improve though strictly on the basis that nobody can claim any intellectual property rights over it. It is being made available in the spirit of the open source movement and use of it should be openly acknowledged. Thanks again to the thousands of people who made millions of sacrifices – because of you we won everywhere except London, Scotland and Northern Ireland against the whole Government machine supported by almost every organisation with power and money.

I will write more about the campaign once the first wave of books is published.

PS. Do not believe the rubbish peddled by Farage and the leave.EU team about social media. E.g. a) They boasted publicly that they paid hundreds of thousands of pounds for over half a million Facebook ‘Likes’ without realising that b) Facebook’s algorithms no longer optimised news feeds for Likes (it is optimised for paid advertising). Leave.EU wasted hundreds of thousands just as many big companies spent millions building armies of Likes that were rendered largely irrelevant by Facebook’s algorithmic changes. This is just one of their blunders. Vote Leave put our money into targeted paid adverts, not buying Likes to spin stories to gullible hacks, MPs, and donors. Media organisations should have someone on the political staff who is a specialist in data or have a route to talk to their organisation’s own data science teams to help spot snake oil merchants.

PPS. If you are young, smart, and interested in politics, think very hard before studying politics / ‘political science’ / PPE at university. You will be far better off if you study maths or physics. It will be easy to move into politics later if you want to and you will have more general skills with much wider application and greater market value. PPE does not give such useful skills – indeed, it actually causes huge problems as it encourages people like Cameron and Ed Balls to ‘fool themselves’ and spread bad ideas with lots of confidence and bluffing. You can always read history books later but you won’t always be able to learn maths. If you have these general skills, then you will be much more effective than the PPE-ers you will compete against. In a few years, this will be more obvious as data science will be much more visible. A new interdisciplinary degree is urgently needed to replace PPE for those who want to go into politics. It should include the basics of modelling and involve practical exposure to people who are brilliant at managing large complex organisations.

PPPS. One of the projects that the Gove team did in the DfE was funding the development of a ‘Maths for Presidents’ course, in the same spirit as the great Berkeley course ‘Physics for Presidents’, based on ideas of Fields Medallist Tim Gowers. The statistics of polling would be a good subject for this course. This course could have a big cultural effect over 20 years if it is supported wisely.

On the Referendum #5: reports of an anti-EU advertising campaign; Greece, the euro, and predictions

The Sunday Telegraph reports that a group of businessmen, including Arron Banks (the UKIP donor), plan a £20 million advertising campaign in September as part of an effort to win the referendum on the EU.

Brief thoughts…

Contrary to some phone calls I’ve had, this is not an example of ‘Eurosceptic infighting’.

Now, there are no spending limits as the Bill has not gone through Parliament. If a group of rich businessmen want to use this period to spend money persuading people of the problems of the EU and how Britain can do better, good luck to them. The important question is: does their campaign have the right messages so it is persuasive?

The Exploratory Committee that was announced last week is not the NO campaign. As I explained on Friday (here), it is a vehicle to coordinate discussions, raise money etc so that a professional NO campaign can exist. I am talking to people about how this can best be done, raising money, and trying to persuade appropriate people to leave their jobs to do this campaign.

As I have said hundreds of times over the past few weeks and will say thousands of times in the next few months, in order to win the referendum many people with very different views will have to find ways to cooperate. Libertarians, socialists and others have to find common ground. Also, all sorts of people and groups will, quite reasonably, want to do their own thing.

It is understandable that in the absence of an official NO campaign, motivated businesspeople are looking to do useful things. My concern is building the foundations of an official NO campaign in the right way such that it can grow into what will be an unprecedented organisational network over the next year. Scale and complexity require organisational innovations.

Greece, the euro, prediction, accountability

On a different subject, Greece and the euro is much in the news… When I was working on the campaign against Britain joining the euro, we did many debates/events/TV shows etc with people like Adair Turner, Ken Clarke, Heseltine, Peter Mandelson, Chris Patten et al.

Our businessmen, such as Stanley Kalms and Simon Wolfson, argued that the euro had been badly constructed and would cause problems for the existing members particularly Greece and Ireland. Turner, Clarke et al breezily wafted away such fears and said we would be proved wrong.

Almost the only extended conversation I have had with Ed Llewellyn, David Cameron’s ‘chief of staff’, was in a restaurant around 2002 when, I think, he was working for Ashdown. He of course said that the euro was a great idea, would work out brilliantly, and we would inevitably join. He is leading the No.10 renegotiation team.

As people who follow this blog know, one of the themes I have explored a lot is the issue of predictions in politics. Physics is so successful because it has an architecture for correcting errors of prediction. Politics has lacked this. Tetlock’s Good Judgement Project (with IARPA) is the most interesting project I know of to inject rigour into the issue of political prediction in order to improve performance radically.

Now that a referendum is coming, Clarke, Heseltine and others are all over the BBC making predictions about the ‘chaos’ and ‘loss of jobs’ that would come from leaving the EU. Because politics does not operate on the basis of being held accountable for predictions, they are almost never asked anything like – ‘but given your false predictions on the euro, why should we have confidence in your predictions on the EU, perhaps you simply have an emotional attachment to the EU that is not susceptible to evidence?’ In politics, ‘Bayesian updating’ is not fashionable particularly when moral signalling is so strong. Many in the BBC see the EU debate, as they saw the euro debate, simply as ‘internationalists v racists’ which makes them even less inclined to challenge people like Ken Clarke who is routinely allowed to make factually wrong assertions without challenge on the Today programme. I blogged about this in an earlier blog in this series HERE.

In comments below, please leave the best examples of quotes from the likes of Clarke, Turner, Mandelson etc along the lines of ‘don’t worry about Greece and Ireland, the euro will be great for them’. 

 

Complexity, ‘fog and moonlight’, prediction, and politics III – von Neumann and economics as a science

The two previous blogs in this series were:

Part I HERE.

Part II HERE.

All page references unless otherwise stated are to my essay, HERE.

Since the financial crisis, there has been a great deal of media and Westminster discussion about why so few people predicted it and what the problems are with economics and financial theory.

Absent from most of this discussion is the history of the subject and its intellectual origins. Economics is clearly a vital area of prediction for people in politics. I therefore will explore some intellectual history to provide context for contemporary discussions about ‘what is wrong with economics and what should be done about it’.

*

It has often been argued that the ‘complexity’ of human behaviour renders precise mathematical treatment of economics impossible, or that the undoubted errors of modern economics in applying the tools of mathematical physics are evidence of the irredeemable hopelessness of the goal.

For example, Kant wrote in Critique of Judgement:

‘For it is quite certain that in terms of merely mechanical principles of nature we cannot even adequately become familiar with, much less explain, organized beings and how they are internally possible. So certain is this that we may boldly state that it is absurd for human beings even to attempt it, or to hope that perhaps some day another Newton might arise who would explain to us, in terms of natural laws unordered by any intention, how even a mere blade of grass is produced. Rather, we must absolutely deny that human beings have such insight.’

In the middle of the 20th Century, one of the great minds of the century turned to this question. John Von Neumann was one of the leading mathematicians of the 20th Century. He was also a major contributor to the mathematisation of quantum mechanics, created the field of ‘quantum logic’ (1936), worked as a consultant to the Manhattan Project and other wartime technological projects, and was one of the two most important creators of modern computer science and artificial intelligence (with Turing) which he developed partly for immediate problems he was working on (e.g. the hydrogen bomb and ICBMs) and partly to probe the general field of understanding complex nonlinear systems.  In an Endnote of my essay I discuss some of these things.

Von Neumann was regarded as an extraordinary phenomenon even by  the cleverest people in the world. The Nobel-winning physicist and mathematician Wigner said of von Neumann:

‘I have known a great many intelligent people in my life. I knew Planck, von Laue and Heisenberg. Paul Dirac was my brother in law; Leo Szilard and Edward Teller have been among my closest friends; and Albert Einstein was a good friend, too. But none of them had a mind as quick and acute as Jansci von Neumann. I have often remarked this in the presence of those men and no one ever disputed me… Perhaps the consciousness of animals is more shadowy than ours and perhaps their perceptions are always dreamlike. On the opposite side, whenever I talked with the sharpest intellect whom I have known – with von Neumann – I always had the impression that only he was fully awake, that I was halfway in a dream.’

Von Neumann also had a big impact on economics. During breaks from pressing wartime business, he wrote ‘Theory of Games and Economic Behaviour’ (TGEB) with Morgenstern. This practically created the field of ‘game theory’ which one sees so many references to now. TGEB was one of the most influential books ever written on economics. (The movie The Beautiful Mind gave a false impression of Nash’s contribution.) In the Introduction, his explanation of some foundational issues concerning economics, mathematics, and prediction is clearer for non-specialists than any other thing I have seen on the subject and cuts through a vast amount of contemporary discussion which fogs the issues.

This documentary on von Neumann is also interesting:

*

There are some snippets from pre-20th Century figures explaining concepts in terms recognisable through the prism of Game Theory. For example, Ampère wrote ‘Considerations sur la théorie mathématique du jeu’ in 1802 and credited Buffon’s 1777 essay on ‘moral arithmetic’ (Buffon figured out many elements that Darwin would later harmonise in his theory of evolution). Cournot discussed what would later be described as a specific example of a ‘Nash equilibrium’ viz duopoly in 1838.  The French mathematician Emile Borel also made contributions to early ideas.

However, Game Theory really was born with von Neumann. In December 1926, he presented the paper ‘Zur Theorie der Gesellschaftsspiele’ (On the Theory of Parlour Games, published in 1928, translated version here) while working on the Hilbert Programme [cf. Endnote on Computing] and quantum mechanics. The connection between the Hilbert Programme and the intellectual origins of Game Theory can perhaps first be traced in a 1912 lecture by one of the world’s leading mathematicians and founders of modern set theory, Zermelo, titled ‘On the Application of Set Theory to Chess’ which stated of its purpose:

‘… it is not dealing with the practical method for games, but rather is simply giving an answer to the following question: can the value of a particular feasible position in a game for one of the players be mathematically and objectively decided, or can it at least be defined without resorting to more subjective psychological concepts?’

He presented a theorem that chess is strictly determined: that is, either (i) white can force a win, or (ii) black can force a win, or (iii) both sides can force at least a draw. Which of these is the actual solution to chess remains unknown. (Cf. ‘Zermelo and the Early History of Game Theory’, by Schwalbe & Walker (1997), which argues that modern scholarship is full of errors about this paper. According to Leonard (2006), Zermelo’s paper was part of a general interest in the game of chess among intellectuals in the first third of the 20th century. Lasker (world chess champion 1897–1921) knew Zermelo and both were taught by Hilbert.)

Von Neumman later wrote:

‘[I]f the theory of Chess were really fully known there would be nothing left to play.  The theory would show which of the three possibilities … actually holds, and accordingly the play would be decided before it starts…  But our proof, which guarantees the validity of one (and only one) of these three alternatives, gives no practically usable method to determine the true one. This relative, human difficulty necessitates the use of those incomplete, heuristic methods of playing, which constitute ‘good’ Chess; and without it there would be no element of ‘struggle’ and ‘surprise’ in that game.’ (p.125)

Elsewhere, he said:

‘Chess is not a game. Chess is a well-defined computation. You may not be able to work out the answers, but in theory there must be a solution, a right procedure in any position. Now, real games are not like that at all. Real life is not like that. Real life consists of bluffing, of little tactics of deception, of asking yourself what is the other man going to think I mean to do. And that is what games are about in my theory.’

Von Neumman’s 1928 paper proved that there is a rational solution to every two-person zero-sum game. That is, in a rigorously defined game with precise payoffs, there is a mathematically rational strategy for both sides – an outcome which both parties cannot hope to improve upon. This introduced the concept of the minimax: choose a strategy that minimises the possible maximum loss.

Zero-sum games are those where the payoffs ‘sum’ to zero. For example, chess or Go are zero-sum games because the gain (+1) and the loss (-1) sum to zero; one person’s win is another’s loss. The famous Prisoners’ Dilemma is a non-zero-sum game because the payoffs do not sum to zero: it is possible for both players to make gains. In some games the payoffs to the players are symmetrical (e.g. Prisoners’ Dilemma); in others, the payoffs are asymmetrical (e.g. the Dictator or Ultimatum games). Sometimes the strategies can be completely stated without the need for probabilities (‘pure’ strategies); sometimes, probabilities have to be assigned for particular actions (‘mixed’ strategies).

While the optimal minimax strategy might be a ‘pure’ strategy, von Neumann showed it would often have to be a ‘mixed strategy’ and this means a spontaneous return of probability, even if the game itself does not involve probability.

‘Although … chance was eliminated from the games of strategy under consideration (by introducing expected values and eliminating ‘draws’), it has now made a spontaneous reappearance. Even if the rules of the game do not contain any elements of ‘hazard’ … in specifying the rules of behaviour for the players it becomes imperative to reconsider the element of ‘hazard’. The dependence on chance (the ‘statistical’ element) is such an intrinsic part of the game itself (if not of the world) that there is no need to introduce it artificially by way of the rules of the game itself: even if the formal rules contain no trace of it, it still will assert itself.’

In 1932, he gave a lecture titled ‘On Certain Equations of Economics and A Generalization of Brouwer’s Fixed-Point Theorem’. It was published in German in 1938 but not in English until 1945 when it was published as ‘A Model of General Economic Equilibrium’. This paper developed what is sometimes called von Neumann’s Expanding Economic Model and has been described as the most influential article in mathematical economics. It introduced the use of ‘fixed-point theorems’. (Brouwer’s ‘fixed point theorem’ in topology proved that, in crude terms, if you lay a map of the US on the ground anywhere in the US, one point on the map will lie precisely over the point it represents on the ground beneath.)

‘The mathematical proof is possible only by means of a generalisation of Brouwer’s Fix-Point Theorem, i.e. by the use of very fundamental topological facts… The connection with topology may be very surprising at first, but the author thinks that it is natural in problems of this kind. The immediate reason for this is the occurrence of a certain ‘minimum-maximum’ problem… It is closely related to another problem occurring in the theory of games.’

Von Neumann’s application of this topological proof to economics was very influential in post-war mathematical economics and in particular was used by Arrow and Debreu in their seminal 1954 paper on general equilibrium, perhaps the central paper in modern traditional economics.

*

In the late 1930’s, von Neumann, based at the IAS in Princeton to which Gödel and Einstein also fled to escape the Nazis, met up with the economist Oskar Morgenstern who was deeply dissatisfied with the state of economics. In 1940, von Neumann began his collaboration on games with Morgenstern, while working on war business including the Manhattan Project and computers, that became The Theory of Games and Economic Behavior (TGEB). By December 1942, he had finished his work on this though it was not published until 1944.

In the Introduction of TGEB, von Neumann explained the real problems in applying mathematics to economics and why Kant was wrong.

‘It is not that there exists any fundamental reason why mathematics should not be used in economics.  The arguments often heard that because of the human element, of the psychological factors etc., or because there is – allegedly – no measurement of important factors, mathematics will find no application, can all be dismissed as utterly mistaken.  Almost all these objections have been made, or might have been made, many centuries ago in fields where mathematics is now the chief instrument of analysis [e.g. physics in the 16th Century or chemistry and biology in the 18th]…

‘As to the lack of measurement of the most important factors, the example of the theory of heat is most instructive; before the development of the mathematical theory the possibilities of quantitative measurements were less favorable there than they are now in economics.  The precise measurements of the quantity and quality of heat (energy and temperature) were the outcome and not the antecedents of the mathematical theory…

‘The reason why mathematics has not been more successful in economics must be found elsewhere… To begin with, the economic problems were not formulated clearly and are often stated in such vague terms as to make mathematical treatment a priori appear hopeless because it is quite uncertain what the problems really are. There is no point using exact methods where there is no clarity in the concepts and issues to which they are applied. [Emphasis added] Consequently the initial task is to clarify the knowledge of the matter by further careful descriptive work. But even in those parts of economics where the descriptive problem has been handled more satisfactorily, mathematical tools have seldom been used appropriately. They were either inadequately handled … or they led to mere translations from a literary form of expression into symbols…

‘Next, the empirical background of economic science is definitely inadequate. Our knowledge of the relevant facts of economics is incomparably smaller than that commanded in physics at the time when mathematization of that subject was achieved.  Indeed, the decisive break which came in physics in the seventeenth century … was possible only because of previous developments in astronomy. It was backed by several millennia of systematic, scientific, astronomical observation, culminating in an observer of unparalleled calibre, Tycho de Brahe. Nothing of this sort has occurred in economics. It would have been absurd in physics to expect Kepler and Newton without Tycho – and there is no reason to hope for an easier development in economics…

‘Very frequently the proofs [in economics] are lacking because a mathematical treatment has been attempted in fields which are so vast and so complicated that for a long time to come – until much more empirical knowledge is acquired – there is hardly any reason at all to expect progress more mathematico. The fact that these fields have been attacked in this way … indicates how much the attendant difficulties are being underestimated. They are enormous and we are now in no way equipped for them.

‘[We will need] changes in mathematical technique – in fact, in mathematics itself…  It must not be forgotten that these changes may be very considerable. The decisive phase of the application of mathematics to physics – Newton’s creation of a rational discipline of mechanics – brought about, and can hardly be separated from, the discovery of the infinitesimal calculus…

‘The importance of the social phenomena, the wealth and multiplicity of their manifestations, and the complexity of their structure, are at least equal to those in physics.  It is therefore to be expected – or feared – that mathematical discoveries of a stature comparable to that of calculus will be needed in order to produce decisive success in this field… A fortiori, it is unlikely that a mere repetition of the tricks which served us so well in physics will do for the social phenomena too.  The probability is very slim indeed, since … we encounter in our discussions some mathematical problems which are quite different from those which occur in physical science.’

Von Neumann therefore exhorted economists to humility and the task of ‘careful, patient description’, a ‘task of vast proportions’. He stressed that economics could not attack the ‘big’ questions – much more modesty is needed to establish an exact theory for very simple problems, and build on those foundations.

‘The everyday work of the research physicist is … concerned with special problems which are “mature”… Unifications of fields which were formerly divided and far apart may alternate with this type of work. However, such fortunate occurrences are rare and happen only after each field has been thoroughly explored. Considering the fact that economics is much more difficult, much less understood, and undoubtedly in a much earlier stage of its evolution as a science than physics, one should clearly not expect more than a development of the above type in economics either…

‘The great progress in every science came when, in the study of problems which were modest as compared with ultimate aims, methods were developed which could be extended further and further. The free fall is a very trivial physical example, but it was the study of this exceedingly simple fact and its comparison with astronomical material which brought forth mechanics. It seems to us that the same standard of modesty should be applied in economics… The sound procedure is to obtain first utmost precision and mastery in a limited field, and then to proceed to another, somewhat wider one, and so on.’

Von Neumann therefore aims in TGEB at ‘the behavior of the individual and the simplest forms of exchange’ with the hope that this can be extended to more complex situations.

‘Economists frequently point to much larger, more ‘burning’ questions…  The experience of … physics indicates that this impatience merely delays progress, including that of the treatment of the ‘burning’ questions. There is no reason to assume the existence of shortcuts…

‘It is a well-known phenomenon in many branches of the exact and physical sciences that very great numbers are often easier to handle than those of medium size. An almost exact theory of a gas, containing about 1025 freely moving particles, is incomparably easier than that of the solar system, made up of 9 major bodies… This is … due to the excellent possibility of applying the laws of statistics and probabilities in the first case.

‘This analogy, however, is far from perfect for our problem. The theory of mechanics for 2,3,4,… bodies is well known, and in its general theoretical …. form is the foundation of the statistical theory for great numbers. For the social exchange economy – i.e. for the equivalent ‘games of strategy’ – the theory of 2,3,4… participants was heretofore lacking. It is this need that … our subsequent investigations will endeavor to satisfy. In other words, only after the theory for moderate numbers of participants has been satisfactorily developed will it be possible to decide whether extremely great numbers of participants simplify the situation.’

[This last bit has changed slightly as I forgot to include a few things.]

While some of von Neumann’s ideas were extremely influential on economics, his general warning here about the right approach to the use of mathematics was not widely heeded.

Most economists initially ignored von Neumann’s ideas.  Martin Shubik, a Princeton mathematician, recounted the scene he found:

‘The contrast of attitudes between the economics department and mathematics department was stamped on my mind… The former projected an atmosphere of dull-business-as-usual conservatism… The latter was electric with ideas… When von Neumann gave his seminar on his growth model, with a few exceptions, the serried ranks of Princeton economists could scarce forebear to yawn.’

However, a small but influential number, including mathematicians at the RAND Corporation (the first recognisable modern ‘think tank’) led by John Williams, applied it to nuclear strategy as well as economics. For example, Albert Wohlstetter published his Selection and Use of Strategic Air Bases (RAND, R-266, sometimes referred to as The Basing Study) in 1954. Williams persuaded the RAND Board and the infamous SAC General Curtis LeMay to develop a social science division at RAND that could include economists and psychologists to explore the practical potential of Game Theory further. He also hired von Neumann as a consultant; when the latter said he was too busy, Williams told him he only wanted the time it took von Neumann to shave in the morning. (Kubrick’s Dr Strangelove satirised RAND’s use of game theory.)

In the 1990’s, the movie A Beautiful Mind brought John Nash into pop culture, giving the misleading impression that he was the principle developer of Game Theory. Nash’s fame rests principally on work he did in 1950-1 that became known as ‘the Nash Equilibrium’. In Non-Cooperative Games (1950), he wrote:

‘[TGEB] contains a theory of n-person games of a type which we would call cooperative. This theory is based on an analysis of the interrelationships of the various coalitions which can be formed by the players of the game. Our theory, in contradistinction, is based on the absence of coalitions in that it is assumed each participant acts independently, without collaboration or communication with any of the others… [I have proved] that a finite non-cooperative game always has at least one equilibrium point.’

Von Neumann remarked of Nash’s results, ‘That’s trivial you know. It’s just a fixed point theorem.’ Nash himself said that von Neumann was a ‘European gentleman’ but was not impressed by his results.

In 1949-50, Merrill Flood, another RAND researcher, began experimenting with staff at RAND (and his own children) playing various games. Nash’s results prompted Flood to create what became known as the ‘Prisoners’ Dilemma’ game, the most famous and studied game in Game Theory. It was initially known as ‘a non-cooperative pair’ and the name ‘Prisoners’ Dilemma’ was given it by Tucker later in 1950 when he had to think of a way of explaining the concept to his psychology class at Stanford and hit on an anecdote putting the payoff matrix in the form of two prisoners in separate cells considering the pros and cons of ratting on each other.

The game was discussed and played at RAND without publishing. Flood wrote up the results in 1952 as an internal RAND memo accompanied by the real-time comments of the players. In 1958, Flood published the results formally (Some Experimental Games). Flood concluded that ‘there was no tendency to seek as the final solution … the Nash equilibrium point.’ Prisoners’ Dilemma has been called ‘the E. coli of social psychology’ by Axelrod, so popular has it become in so many different fields. Many studies of Iterated Prisoners’ Dilemma games have shown that generally neither human nor evolved genetic algorithm players converge on the Nash equilibrium but choose to cooperate far more than Nash’s theory predicts.

Section 7 of my essay discusses some recent breakthroughs, particularly the paper by Press & Dyson. This is also a good example of how mathematicians can invade fields. Dyson’s professional fields are maths and physics. He was persuaded to look at the Prisoners’ Dilemma. He very quickly saw that there was a previously unseen class of strategies that has opened up a whole new field for exploration. This article HERE is a good summary of recent developments.

Von Neumann’s brief forays into economics were very much a minor sideline for him but there is no doubt of his influence. Despite von Neumann’s reservations about neoclassical economics, Paul Samuelson admitted that, ‘He darted briefly into our domain, and it has never been the same since.’

In 1987, the Santa Fe Institute, founded by Gell Mann and others, organised a ten day meeting to discuss economics. On one side, they invited leading economists such as Kenneth Arrow and Larry Summers; on the other side, they invited physicists, biologists, and computer scientists, such as Nobel-winning Philip Anderson and John Holland (inventor of genetic algorithms). When the economists explained their assumptions, Phil Anderson said to them, ‘You guys really believe that?

One physicist later described the meeting as like visiting Cuba – the cars are all from the 1950’s so on one hand you admire them for keeping them going, but on the other hand they are old technology; similarly the economists were ingeniously using 19th Century maths and physics on very out-of-date models. The physicists were shocked at how the economists were content with simplifying assumptions that were obviously contradicted by reality, and they were surprised at the way the economists seemed unconcerned about how poor their predictions were.

Twenty-seven years later, this problem is more acute. Some economists are listening to the physicists about fundamental problems with the field. Some are angrily rejecting the physicists’ incursions into their field.

Von Neumann explained the scientifically accurate approach to economics and mathematics. [Inserted later. I mean – the first part of his comments above that discusses maths, prediction, models, and economics and physics. As far as I know, nobody seriously disputes these comments – i.e. that Kant and the general argument that ‘maths cannot make inroads into economics’ are wrong. The later comments about building up economic theories from theories of 2, 3, 4 agents etc is a separate topic. See comments.] In other blogs in this series I will explore some of the history of economic thinking as part of a description of the problem for politicians and other decision-makers who need to make predictions.

Please leave corrections and comments below.

 

Complexity, ‘fog and moonlight’, prediction, and politics II: controlled skids and immune systems (UPDATED)

‘Politics is a job that can really only be compared with navigation in uncharted waters. One has no idea how the weather or the currents will be or what storms one is in for. In politics, there is the added fact that one is largely dependent on the decisions of others, decisions on which one was counting and which then do not materialise; one’s actions are never completely one’s own. And if the friends on whose support one is relying change their minds, which is something that one cannot vouch for, the whole plan miscarries… One’s enemies one can count on – but one’s friends!’ Otto von Bismarck.

‘Everything in war is very simple, but the simplest thing is difficult. The difficulties accumulate and end by producing a kind of friction that is inconceivable unless one has experienced war… Countless minor incidents – the kind you can never really foresee – combine to lower the general level of performance, so that one always falls short of the intended goal.  Iron will-power can overcome this friction … but of course it wears down the machine as well… Friction is the only concept that … corresponds to the factors that distinguish real war from war on paper.  The … army and everything else related to it is basically very simple and therefore seems easy to manage. But … each part is composed of individuals, every one of whom retains his potential of friction… This tremendous friction … is everywhere in contact with chance, and brings about effects that cannot be measured… Friction … is the force that makes the apparently easy so difficult… Finally … all action takes place … in a kind of twilight, which like fog or moonlight, often tends to make things seem grotesque and larger than they really are.  Whatever is hidden from full view in this feeble light has to be guessed at by talent, or simply left to chance.’ Clausewitz.

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In July, I wrote a blog on complexity and prediction which you can read HERE.

I will summarise briefly its main propositions and add some others. All page references are to my essay, HERE. (Section 1 explores some of the maths and science issues below in more detail.)

Some people asked me after Part I – why is such abstract stuff important to practical politics? That is a big question but in a nutshell…

If you want to avoid the usual fate in politics of failure, you need to understand some basic principles about why people make mistakes and how some people, institutions, and systems cope with mistakes and thereby perform much better than most. The reason why Whitehall is full of people failing in predictable ways on an hourly basis is because, first, there is general system-wide failure and, second, everybody keeps their heads down focused on the particular and they ignore the system. Officials who speak out see their careers blow up. MPs are so cowed by the institutions and the scale of official failure that they generally just muddle along tinkering and hope to stay a step ahead of the media. Some understand the epic scale of institutional failure but they know that the real internal wiring of the system in the Cabinet Office has such a tight grip that significant improvement will be very hard without a combination of a) a personnel purge and b) a fundamental rewiring of power at the apex of the state. Many people in Westminster are now considering how this might happen. Such thoughts must, I think, be based on some general principles otherwise they are likely to miss the real causes of system failure and what to do.

In future blogs in this series, I will explore some aspects of markets and science that throw light on the question: how can humans and their institutions cope with these problems of complexity, uncertainty, and prediction in order to limit failures?

Separately, The Hollow Men II will focus on specifics of how Whitehall and Westminster work, including Number Ten and some examples from the Department for Education.

Considering the more general questions of complexity and prediction sheds light on why government is failing so badly and how it could be improved.

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Complexity, nonlinearity, uncertainty, and prediction

Even the simplest practical problems are often very complex. If a Prime Minister wants to line up 70 colleagues in Downing Street to blame them for his woes, there are 70! ways of lining them up and 70! [70! = 70 x 69 x 68 … x 2 x 1] is roughly 10100 (a ‘googol’), which is roughly ten billion times the estimated number of atoms in the universe (1090). [See comments.]

Even the simplest practical problems, therefore, can be so complicated that searching through the vast landscape of all possible solutions is not practical.

After Newton, many hoped that perfect prediction would be possible:

‘An intellect which at a certain moment would know all the forces that animate nature, and all positions of the beings that compose it, if this intellect were vast enough to submit the data to analysis, would condense in a single formula the movements of the greatest bodies of the universe and those of the tiniest atom; for such an intellect nothing would be uncertain and the future just like the past would be present before its eyes’ (Laplace).

However, most of the most interesting systems in the world – such as brains, cultures, and conflicts – are nonlinear. That is, a small change in input has an arbitrarily large affect on output. Have you ever driven through a controlled skid then lost it? A nonlinear system is one in which you can shift from ‘it feels great on the edge’ to ‘I’m steering into the skid but I’ve lost it and might die in a few seconds’ because of one tiny input change, like your tyre catches a cat’s eye in the wet. This causes further problems for prediction. Not only is the search space so vast it cannot be searched exhaustively, however fast our computers, but in nonlinear systems one has the added problem that a tiny input change can lead to huge output changes.

Some nonlinear systems are such that no possible accuracy of measurement of the current state can eliminate this problem – there is unavoidable uncertainty about the future state. As Poincaré wrote, ‘it may happen that small differences in the initial conditions produce very great ones in the final phenomena. A small error in the former will produce an enormous error in the latter. Prediction becomes impossible, and we have the fortuitous phenomenon.’ It does not matter that the measurement error is in the 20th decimal place – the prediction will still quickly collapse.

Weather systems are like this which is why, despite the enormous progress made with predictions, we remain limited to ~10-14 days at best. To push the horizon forward by just one day requires exponential increases in the resources required. Political systems are also nonlinear. If Cohen-Blind’s aim had been very slightly different in May 1866 when he fired five bullets at Bismarck, the German states would certainly have evolved in a different way and perhaps there would have been no fearsome German army led by a General Staff into World War I, no Lenin and Hitler, and so on.  Bismarck himself appreciated this very well. ‘We are poised on the tip of a lightning conductor, and if we lose the balance I have been at pains to create we shall find ourselves on the ground,’ he wrote to his wife during the 1871 peace negotiations in Versailles. Social systems are also nonlinear. Online experiments have explored how complex social networks cannot be predicted because of initial randomness combining with the interdependence of decisions.

In short, although we understand some systems well enough to make precise or statistical predictions, most interesting systems – whether physical, mental, cultural, or virtual – are complex, nonlinear, and have properties that emerge from feedback between many interactions. Exhaustive searches of all possibilities are impossible. Unfathomable and unintended consequences dominate. Problems cascade. Complex systems are hard to understand, predict and control.

Humans evolved in this complex environment amid the sometimes violent, sometimes cooperative sexual politics of small in-groups competing with usually hostile out-groups. We evolved to sense information, process it, and act. We had to make predictions amid uncertainty and update these predictions in response to feedback from our environment – we had to adapt because we have necessarily imperfect data and at best approximate models of reality. It is no coincidence that in one of the most famous speeches in history, Pericles singled out the Athenian quality of adaptation (literally ‘well-turning’) as central to its extraordinary cultural, political and economic success.

How do we make these predictions, how do we adapt? Much of how we operate depends on relatively crude evolved heuristics (rules of thumb) such as ‘sense movement >> run/freeze’. These heuristics can help. Further, our evolved nature gives us amazing pattern recognition and problem-solving abilities. However, some heuristics lead to errors, illusions, self-deception, groupthink and so on – problems that often swamp our reasoning and lead to failure.

I will look briefly at a) the success of science and mathematical models, b) the success of decentralised coordination in nature and markets, and c) the failures of political prediction and decision-making.

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The success of science and mathematical models

Our brains evolved to solve social and practical problems, not to solve mathematical problems. This is why translating mathematical and logical problems into social problems makes them easier for people to solve (cf. Nielsen.) Nevertheless, a byproduct of our evolution was the ability to develop maths and science. Maths gives us an abstract structure of certain knowledge that we can use to build models of the world. ‘[S]ciences do not try to explain, they hardly even try to interpret, they mainly make models. By a model is meant a mathematical construct which, with the addition of certain verbal interpretations, describes observed phenomena. The justification of such a mathematical construct is solely and precisely that it is expected … correctly to describe phenomena from a reasonably wide area’ (von Neumann).

Because the universe operates according to principles that can be approximated by these models, we can understand it approximately. ‘Why’ is a mystery. Why should ‘imaginary numbers’ based on the square root of minus 1, conceived five hundred years ago and living for hundreds of years without practical application, suddenly turn out to be necessary in the 1920s to calculate how subatomic particles behave? How could it be that in a serendipitous meeting in the IAS cafeteria in 1972, Dyson and Montgomery should realise that an equation describing the distribution of prime numbers should also describe the energy level of particles? We can see that the universe displays a lot of symmetry but we do not know why there is some connection between the universe’s operating principles and our evolved brains’ abilities to do abstract mathematics. Einstein asked, ‘How is it possible that mathematics, a product of human thought that is independent of experience, fits so excellently the objects of physical reality?’ Wigner replied to Einstein in a famous paper, ‘The Unreasonable Effectiveness of Mathematics in the Natural Sciences’ (1960) but we do not know the answer. (See ‘Is mathematics invented or discovered?’, Tim Gowers, 2011.)

The accuracy of many of our models gets better and better. In some areas such as quantum physics, the equations have been checked so delicately that, as Feynman said, ‘If you were to measure the distance from Los Angeles to New York to this accuracy, it would be exact to the thickness of a human hair’. In other areas, we have to be satisfied with statistical models. For example, many natural phenomenon, such as height and intelligence, can be modelled using ‘normal distributions’. Other phenomena, such as the network structure of cells, the web, or banks in an economy, can be modelled using ‘power laws’. [* See End] Why do statistical models work? Because ‘chance phenomena, considered collectively and on a grand scale, create a non-random regularity’ (Kolmogorov). [** See End]

Science has also built an architecture for its processes, involving meta-rules, that help correct errors and normal human failings. For example, after Newton the system of open publishing and peer review developed. This encouraged scientists to make their knowledge public, confident that they would get credit (instead of hiding things in code like Newton). Experiments must be replicated and scientists are expected to provide their data honestly so that others can test their claims, however famous, prestigious, or powerful they are. Feynman described the process in physics as involving, at its best, ‘a kind of utter honesty … [Y]ou should report everything that you think might make [your experiment or idea] invalid… [Y]ou must also put down all the facts which disagree with it, as well as those that agree with it… The easiest way to explain this idea is to contrast it … with advertising.’

The architecture of the scientific process is not perfect. Example 1. Evaluation of contributions is hard. The physicist who invented the arXiv was sacked soon afterwards because his university’s tick box evaluation system did not have a way to value his enormous contribution. Example 2. Supposedly ‘scientific’ advice to politicians can also be very overconfident. E.g. A meta-study of 63 studies of the costs of various energy technologies reveals: ‘The discrepancies between equally authoritative, peer-reviewed studies span many orders of magnitude, and the overlapping uncertainty ranges can support almost any ranking order of technologies, justifying almost any policy decision as science based’ (Stirling, Nature, 12/2010).

This architecture and its meta-rules are now going through profound changes, brilliantly described by the author of the seminal textbook on quantum computers, Michael Nielsen, in his book Reinventing Discovery – a book that has many lessons for the future of politics too. But overall the system clearly has great advantages.

The success of decentralised information processing in solving complex problems

Complex systems and emergent properties

Many of our most interesting problems can be considered as networks. Individual nodes (atoms, molecules, genes, cells, neurons, minds, organisms, organisations, computer agents) and links (biochemical signals, synapses, internet routers, trade routes) form physical, mental, and cultural networks (molecules, cells, organisms, immune systems, minds, organisations, internet, biosphere, ‘econosphere’, cultures) at different scales.

The most interesting networks involve interdependencies (feedback and feedforward) – such as chemical signals, a price collapse, neuronal firing, an infected person gets on a plane, or an assassination – and are nonlinear. Complex networks have emergent properties including self-organisation. For example, the relative strength of a knight in the centre of the chessboard is not specified in the rules but emerges from the nodes of the network (or ‘agents’) operating according to the rules.

Even in physics, ‘The behavior of large and complex aggregates of elementary particles … is not to be understood in terms of a simple extrapolation of the properties of a few particles. Instead, at each level of complexity entirely new properties appear’ (Anderson). This is more obvious in biological and social networks.

Ant colonies and immune systems: how decentralised information processing solves complex problems

Ant colonies and the immune system are good examples of complex nonlinear systems with ‘emergent properties’ and self-organisation.

The body cannot ‘know’ in advance all the threats it will face so the immune system cannot be perfectly ‘pre-designed’. How does it solve this problem?

There is a large diverse population of individual white blood cells (millions produced per day) that sense threats. If certain cells detect that a threat has passed a threshold, then they produce large numbers of daughter cells, with mutations, that are tested on captured ‘enemy’ cells. Unsuccessful daughter cells die while successful ones are despatched to fight. These daughter cells repeat the process so a rapid evolutionary process selects and reproduces the best defenders and continually improves performance. Other specialist cells roam around looking for invaders that have been tagged by antibodies. Some of the cells remain in the bloodstream, storing information about the attack, to guard against future attacks (immunity).

There is a constant evolutionary arms race against bacteria and other invaders. Bacteria take over cells’ machinery and communications. They reprogram cells to take them over or trigger self-destruction. They disable immune cells and ‘ride’ them back into lymph nodes (Trojan horse style) where they attack. They shape-change fast so that immune cells cannot recognise them. They reprogram immune cells to commit suicide. They reduce competition by tricking immune cells into destroying other bacteria that help the body fight infection (e.g. by causing diarrhoea to flush out competition).

NB. there is no ‘plan’ and no ‘central coordination’. The system experiments probabilistically, reinforces success, and discards failure. It is messy. Such a system cannot be based on trying to ‘eliminate failure’. It is based on accepting a certain amount of failure but keeping it within certain tolerances via learning.

Looking at an individual ant, it would be hard to know that an ant colony is capable of farming, slavery, and war.

‘The activity of an ant colony is totally defined by the activities and interactions of its constituent ants. Yet the colony exhibits a flexibility that goes far beyond the capabilities of its individual constituents. It is aware of and reacts to food, enemies, floods, and many other phenomena, over a large area; it reaches out over long distances to modify its surroundings in ways that benefit the colony; and it has a life-span orders of magnitude longer than that of its constituents… To understand the ant, we must understand how this persistent, adaptive organization emerges from the interactions of its numerous constituents.’ (Hofstadter)

Ant colonies face a similar problem to the immune system: they have to forage for food in an unknown environment with an effectively infinite number of possible ways to search for a solution. They send out agents looking for food; those that succeed return to the colony leaving a pheromone trail which is picked up by others and this trail strengthens. Decentralised decisions via interchange of chemical signals drive job-allocation (the division of labour) in the colony. Individual ants respond to the rate of what others are doing: if an ant finds a lot of foragers, it is more likely to start foraging.

Similarities between the immune system and ant colonies in solving complex problems

Individual white blood cells cannot access the whole picture; they sample their environment via their receptors. Individual ants cannot cannot access the whole picture; they sample their environment via their chemical processors. The molecular shape of immune cells and the chemical processing abilities of ants are affected by random mutations; the way individual cells or ants respond has a random element. The individual elements (cells / ants) are programmed to respond probabilistically to new information based on the strength of signals they receive.

Environmental exploration by many individual agents coordinated via feedback signals allows a system to probe many different probabilities, reinforce success, ‘learn’ from failure (e.g withdraw resources from unproductive strategies), and keep innovating (e.g novel cells are produced even amid a battle and ants continue to look for better options even after striking gold). ‘Redundancy’ allows local failures without breaking the system. There is a balance between exploring the immediate environment for information and exploiting that information to adapt.

In such complex networks with emergent properties, unintended consequences dominate. Effects cascade: ‘they come not single spies but in battalions’. Systems defined as ‘tightly coupled‘ – that is, they have strong interdependencies so that the behaviour of one element is closely connected to another – are not resilient in the face of nonlinear events (picture a gust of wind knocking over one domino in a chain).

Network topology

We are learning how network topology affects these dynamics. Many networks (including cells, brains, the internet, the economy) have a topology such that nodes are distributed according to a power law (not a bell curve), which means that the network looks like a set of  hubs and spokes with a few spokes connecting hubs. This network topology makes them resilient to random failure but vulnerable to the failure of critical hubs that can cause destructive cascades (such as financial crises) – an example of the problems that come with nonlinearity.

Similar topology and dynamics can be seen in networks operating at very different scales ranging from cellular networks, the brain, the financial system, the economy in general, and the internet. Disease networks often shows the same topology, with certain patients, such as those who get on a plane from West Africa to Europe with Ebola, playing the role of critical hubs connecting different parts of the network. Terrorist networks also show the same topology. All of these complex systems with emergent properties have the same network topology and are vulnerable to the failure of critical hubs.

Many networks evolve modularity. A modular system is one in which specific modules perform specific tasks, with links between them allowing broader coordination. This provides greater effectiveness and resilience to shocks. For example, Chongqing in China saw the evolution of a new ecosystem for designing and building motorbikes in which ‘assembler’ companies assemble modular parts built by competing companies, instead of relying on high quality vertically integrated companies like Yamaha. This rapidly decimated Japanese competition. Connections between network topology, power laws and fractals can be seen in work by physicist Geoffrey West both on biology and cities, for it is clear that just as statistical tools like the Central Limit Theorem demonstrate similar structure in completely different systems and scales, so similar processes occur in biology and social systems. [See Endnote.]

Markets: how decentralised information processing solves complex problems

[Coming imminently]

A summary of the progress brought by science and markets

The combination of reasoning, reliable accumulated knowledge, and a reliable institutional architecture brings steady progress, and occasional huge breakthroughs and wrong turns, in maths and science. The combination of the power of decentralised information processing to find solutions to complex problems and an institutional architecture brings steady progress, and occasional huge breakthroughs and wrong turns, in various fields that operate via markets.

Fundamental to the institutional architecture of markets and science is mechanisms that enable adaptation to errors. The self-delusion and groupthink that is normal for humans – being a side-effect of our nature as evolved beings – is partly countered by tried and tested mechanisms. These mechanisms are not based on an assumption that we can ‘eliminate failure’ (as so many in politics absurdly claim they will do). Instead, the assumption is that failure is a persistent phenomenon in a complex nonlinear world and it must be learned from and adapted to as quickly as possible. Entrepreneurs and scientists can be vain, go mad, or be prone to psychopathy – like public servants – but we usually catch it quicker and it causes less trouble. Catching errors, we inch forward ‘standing on the shoulders of giants’ as Newton put it.

Science has enabled humans to make transitions from numerology to mathematics, from astrology to astronomy, from alchemy to chemistry, from witchcraft to neuroscience, from tallies to quantum computation. Markets have been central to a partial transition in a growing fraction of the world from a) small, relatively simple, hierarchical, primitive, zero-sum hunter-gatherer tribes based on superstition (almost total ignorance of complex systems), shared aims, personal exchange and widespread violence, to b) large, relatively complex, decentralised, technological, nonzero-sum market-based cultures based on science (increasingly accurate predictions and control in some fields), diverse aims, impersonal exchange, trade, private property, and (roughly) equal protection under the law.

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The failures of politics: wrong predictions, no reliable mechanisms for fixing obvious errors

 ‘No official estimates even mentioned that the collapse of Communism was a distinct possibility until the coup of 1989.’ National Security Agency, ‘Dealing With the Future’, declassified report. 

However, the vast progress made in so many fields is clearly not matched in standards of government. In particular, it is very rare for individuals or institutions to make reliable predictions.

The failure of prediction in politics

Those in leading positions in politics and public service have to make all sorts of predictions. Faced with such complexity, politicians and others have operated mostly on heuristics (‘political philosophy’), guesswork, willpower and tactical adaptation. My own heuristics for working in politics are: focus, ‘know yourself’ (don’t fool yourself), think operationally, work extremely hard, don’t stick to the rules, and ask yourself ‘to be or to do?’.

Partly because politics is a competitive enterprise in which explicit and implicit predictions elicit countermeasures, predictions are particularly hard. This JASON report (PDF) on the prediction of rare events explains some of the technical arguments about predicting complex nonlinear systems such as disasters. Unsurprisingly, so-called ‘political experts’ are not only bad at predictions but are far worse than they realise. There are many prominent examples. Before the 2000 election, the American Political Science Association’s members unanimously predicted a Gore victory. Beyond such examples, we have reliable general data on this problem thanks to a remarkable study by Philip Tetlock. He charted political predictions made by supposed ‘experts’ (e.g will the Soviet Union collapse, will the euro collapse) for fifteen years from 1987 and published them in 2005 (‘Expert Political Judgement’). He found that overall, ‘expert’ predictions were about as accurate as monkeys throwing darts at a board. Experts were very overconfident: ~15 percent of events that experts claimed had no chance of occurring did happen, and ~25 percent of those that they said they were sure would happen did not happen. Further, the more media interviews an expert did, the less likely they were to be right. Specific expertise in a particular field was generally of no value; experts on Canada were about as accurate on the Soviet Union as experts on the Soviet Union were.

However, some did better than others. He identified two broad categories of predictor. The first he called ‘hedgehogs’ – fans of Big Ideas like Marxism, less likely to admit errors. The second he called ‘foxes’ – not fans of Big Ideas, more likely to admit errors and change predictions because of new evidence. (‘The fox knows many little things, but the hedgehog knows one big thing,’ Archilochus.) Foxes tended to make better predictions. They are more self-critical, adaptable, cautious, empirical, and multidisciplinary. Hedgehogs get worse as they acquire more credentials while foxes get better with experience. The former distort facts to suit their theories; the latter adjust theories to account for new facts.

Tetlock believes that the media values characteristics (such as Big Ideas, aggressive confidence, tenacity in combat and so on) that are the opposite of those prized in science (updating in response to new data, admitting errors, tenacity in pursuing the truth and so on). This means that ‘hedgehog’ qualities are more in demand than ‘fox’ qualities, so the political/media market encourages qualities that make duff predictions more likely. ‘There are some academics who are quite content to be relatively anonymous. But there are other people who aspire to be public intellectuals, to be pretty bold and to attach non-negligible probabilities to fairly dramatic change. That’s much more likely to bring you attention’ (Tetlock).

Tetlock’s book ought to be much-studied in Westminster particularly given 1) he has found reliable ways of identifying a small number of people who are very good forecasters and 2)  IARPA (the intelligence community’s DARPA twin) is working with Tetlock to develop training programmes to improve forecasting skills. [See Section 6.] Tetolock says, ‘We now have a significant amount of evidence on this, and the evidence is that people can learn to become better. It’s a slow process. It requires a lot of hard work, but some of our forecasters have really risen to the challenge in a remarkable way and are generating forecasts that are far more accurate than I would have ever supposed possible from past research in this area.’ (This is part of IARPA’s ACE programme to develop aggregated forecast systems and crowdsourced prediction software. IARPA also has the SHARP programme to find ways to improve problem-solving skills for high-performing adults.)

His main advice? ‘If I had to bet on the best long-term predictor of good judgement among the observers in this book, it would be their commitment – their soul-searching Socratic commitment – to thinking about how they think’ (Tetlock). His new training programmes help people develop this ‘Socratic commitment’ and correct their mistakes in quite reliable ways.

NB. The extremely low quality of political forecasting is what allowed an outsider like Nate Silver to transform the field simply by applying some well-known basic maths.

The failure of prediction in economics

‘… the evidence from more than fifty years of research is conclusive: for a large majority of fund managers, the selection of stocks is more like rolling dice than like playing poker. Typically at least two out of every three mutual funds underperform the overall market in any given year. More important, the year-to-year correlation between the outcomes of mutual funds is very small, barely higher than zero. The successful funds in any given year are mostly lucky; they have a good roll the dice.’ Daniel Kahneman, winner of the economics ‘Nobel’ (not the same as the Nobel for physical sciences).

‘I importune students to read narrowly within economics, but widely in science…The economic literature is not the best place to find new inspiration beyond these traditional technical methods of modelling’ Vernon Smith, winner of the economics ‘Nobel’. 

I will give a few examples of problems with economic forecasting.

In the 1961 edition of his famous standard textbook used by millions of students, one of the 20th Century’s most respected economists, Paul Samuelson, predicted that respective growth rates in America and the Soviet Union meant the latter would overtake the USA between 1984-1997. By 1980, he had delayed the date to be in 2002-2012. Even in 1989, he wrote, ‘The Soviet economy is proof that, contrary to what many skeptics had earlier believed, a socialist command economy can function and even thrive.’

Chart: Samuelson’s prediction for the Soviet economy 

samuelson

The recent financial crisis also demonstrated many failed predictions. Various people, including physicists Steve Hsu and Eric Weinstein, published clear explanations of the extreme dangers in the financial markets and parallels with previous crashes such as Japan’s. However, they were almost totally ignored by politicians, officials, central banks and so on. Many of those involved were delusional. Perhaps most famously, Joe Cassano of AIG Financial said in a conference call (8/2007): ‘It’s hard for us – without being flippant – to even see a scenario within any kind of realm of reason that would see us losing one dollar in any of those transactions… We see no issues at all emerging.’

Nate Silver recently summarised some of the arguments over the crash and its aftermath. In December 2007, economists in the Wall Street Journal forecasting panel predicted only a 38 percent chance of recession in 2008. The Survey of Professional Forecasters is a survey of economists’ predictions done by the Federal Reserve Bank that includes uncertainty measurements. In November 2007, the Survey showed a net prediction by economists that the economy would grow by 2.4% in 2008, with a less than 3% chance of any recession and a 1-in-500 chance of it shrinking by more than 2%.

Chart: the 90% ‘prediction intervals’ for the Survey of Professional Forecasters net forecast of GDP growth 1993-2010

Prediction econ

If the economists’ predictions were accurate, the 90% prediction interval should be right nine years out of ten, and 18 out of 20. Instead, the actual growth was outside the 90% prediction interval six times out of 18, often by a lot. (The record back to 1968 is worse.) The data would later reveal that the economy was already in recession in the last quarter of 2007 and, of course, the ‘1-in-500’ event of the economy shrinking by more than 2% is exactly what happened.**

Although the total volume of home sales in 2007 was only ~$2 trillion, Wall Street’s total volume of trades in mortgage-backed securities was ~$80 trillion because of the creation of ‘derivative’ financial instruments. Most people did not understand 1) how likely a house price fall was, 2) how risky mortgage-backed securities were, 3) how widespread leverage could turn a US housing crash into a major financial crash, and 4) how deep the effects of a major financial crash were likely to be.  ‘The actual default rates for CDOs were more than two hundred times higher than S&P had predicted’ (Silver). In the name of ‘transparency’, S&P provided the issuers with copies of their ratings software allowing CDO issuers to experiment on how much junk they could add without losing a AAA rating. S&P even modelled a potential housing crash of 20% in 2005 and concluded its highly rated securities could ‘weather a housing downturn without suffering a credit rating downgrade.’

Unsurprisingly, Government unemployment forecasts were also wrong. Historically, the uncertainty in an unemployment rate forecast made during a recession had been about plus or minus 2 percent but Obama’s team, and economists in general, ignored this record and made much more specific predictions. In January 2009, Obama’s team argued for a large stimulus and said that, without it, unemployment, which had been 7.3% in December 2008, would peak at ~9% in early 2010, but with the stimulus it would never rise above 8% and would fall from summer 2009. However, the unemployment numbers after the stimulus was passed proved to be even worse than the ‘no stimulus’ prediction. Similarly, the UK Treasury’s forecasts about growth, debt, and unemployment from 2007 were horribly wrong but that has not stopped it making the same sort of forecasts.

Paul Krugman concluded from this episode: the stimulus was too small. Others concluded it had been a waste of money. Academic studies vary widely in predicting the ‘return’ from each $1 of stimulus. Since economists cannot even accurately predict a recession when the economy is already in recession, it seems unlikely that there will be academic consensus soon on such issues. Economics often seems like a sort of voodoo for those in power – spurious precision and delusions that there are sound mathematical foundations for the subject without a proper understanding of the conditions under which mathematics can help (cf. Von Neumann on maths and prediction in economics HERE).

Fields which do better at prediction

Daniel Kahneman, who has published some of the most important research about why humans make bad predictions, summarises the fundamental issues about when you can trust expert predictions:

‘To know whether you can trust a particular intuitive judgment, there are two questions you should ask: Is the environment in which the judgment is made sufficiently regular to enable predictions from the available evidence? The answer is yes for diagnosticians, no for stock pickers. Do the professionals have an adequate opportunity to learn the cues and the regularities? The answer here depends on the professionals’ experience and on the quality and speed with which they discover their mistakes. Anesthesiologists have a better chance to develop intuitions than radiologists do. Many of the professionals we encounter easily pass both tests, and their off-the-cuff judgments deserve to be taken seriously. In general, however, you should not take assertive and confident people at their own evaluation unless you have independent reason to believe that they know what they are talking about.’ (Emphasis added.)

It is obvious that politics fulfils neither of his two criteria – it does not even have hard data and clear criteria for success, like stock picking.

I will explore some of the fields that do well at prediction in a future blog.

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The consequences of the failure of politicians and other senior decision-makers and their institutions

‘When superior intellect and a psychopathic temperament coalesce …, we have the best possible conditions for the kind of effective genius that gets into the biographical dictionaries’ (William James). 

‘We’re lucky [the Unabomber] was a mathematician, not a molecular biologist’ (Bill Joy, Silicon Valley legend, author of ‘Why the future doesn’t need us’).

While our ancestor chiefs understood bows, horses, and agriculture, our contemporary chiefs (and those in the media responsible for scrutiny of decisions) generally do not understand their equivalents, and are often less experienced in managing complex organisations than their predecessors.

The consequences are increasingly dangerous as markets, science and technology disrupt all existing institutions and traditions, and enhance the dangerous potential of our evolved nature to inflict huge physical destruction and to manipulate the feelings and ideas of many people (including, sometimes particularly, the best educated) through ‘information operations’. Our fragile civilisation is vulnerable to large shocks and a continuation of traditional human politics as it was during 6 million years of hominid evolution – an attempt to secure in-group cohesion, prosperity and strength in order to dominate or destroy nearby out-groups in competition for scarce resources – could kill billions. We need big changes to schools, universities, and political and other institutions for their own sake and to help us limit harm done by those who pursue dreams of military glory, ‘that attractive rainbow that rises in showers of blood’ (Lincoln).

The global population of people with an IQ four standard deviations above the average (i.e. >160) is ~250k. About 1% of the population are psychopaths so there are perhaps ~2-3,000 with IQ ≈ Nobel/Fields winner. The psychopathic +3SD IQ (>145; average science PhD ~130) population is 30 times bigger. A subset will also be practically competent. Some of them may think, ‘Flectere si nequeo superos, / Acheronta movebo’ (‘If Heav’n thou can’st not bend, Hell thou shalt move’, the Aeneid). Board et al (2005) showed that high-level business executives are more likely than inmates of Broadmoor to have one of three personality disorders (PDs): histrionic PD, narcissistic PD, and obsessive-compulsive PD. Mullins-Sweatt et al (2010) showed that successful psychopaths are more conscientious than the unsuccessful.

A brilliant essay (here) by one of the 20th Century’s best mathematicians, John von Neumann, describes these issues connecting science, technology, and how institutions make decisions.

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Some conclusions

When we consider why institutions are failing and how to improve them, we should consider the general issues discussed above. How to adapt quickly to new information? Does the institution’s structure incentivise effective adaptation or does it incentivise ‘fooling oneself’ and others? Is it possible to enable distributed information processing to find a ‘good enough’ solution in a vast search space? If your problem is similar to that of the immune system or ant colony, why are you trying to solve it with a centralised bureaucracy?

Further, some other obvious conclusions suggest themselves.

We could change our society profoundly by dropping the assumption that less than a tenth of the population is suitable to be taught basic concepts in maths and physics that have very wide application to our culture, such as normal distributions and conditional probability. This requires improving basic maths 5-16 and it also requires new courses in schools.

One of the things that we did in the DfE to do this was work with Fields Medallist Tim Gowers on a sort of ‘Maths for Presidents’ course. Professor Gowers wrote a fascinating blog on this course which you can read HERE. The DfE funded MEI to develop the blog into a real course. This has happened and the course is now being developed in schools. Physics for Future Presidents already exists and is often voted the most popular course at UC Berkeley (Cf. HERE). School-age pupils, arts graduates, MPs, and many Whitehall decision-makers would greatly benefit from these two courses.

We also need new inter-disciplinary courses in universities. For example, Oxford could atone for PPE by offering Ancient and Modern History, Physics for Future Presidents, and How to Run a Start Up. Such courses should connect to the work of Tetlock on The Good Judgement Project, as described above (I will return to this subject).

Other countries have innovated successfully in elite education. For example, after the shock of the Yom Kippur War, Israel established the ‘Talpiot’ programme which  ‘aims to provide the IDF and the defense establishment with exceptional practitioners of research and development who have a combined understanding in the fields of security, the military, science, and technology. Its participants are taught to be mission-oriented problem-solvers. Each year, 50 qualified individuals are selected to participate in the program out of a pool of over 7,000 candidates. Criteria for acceptance include excellence in physical science and mathematics as well as an outstanding demonstration of leadership and character. The program’s training lasts three years, which count towards the soldiers’ three mandatory years of service. The educational period combines rigorous academic study in physics, computer science, and mathematics alongside intensive military training… During the breaks in the academic calendar, cadets undergo advanced military training… In addition to the three years of training, Talpiot cadets are required to serve an additional six years as a professional soldier. Throughout this period, they are placed in assorted elite technological units throughout the defense establishment and serve in central roles in the fields of research and development’ (IDF, 2012). The programme has also helped the Israeli hi-tech economy.****

If politicians had some basic training in mathematical reasoning, they could make better decisions amid complexity. If politicians had more exposure to the skills of a Bill Gates or Peter Thiel, they would be much better able to get things done.

I will explore the issue of training for politicians in a future blog.

Please leave corrections and comments below.


* It is very important to realise when the system one is examining is well approximated by a normal distribution and when by a power law. For example… When David Viniar (Goldman Sachs CFO) said of the 2008 financial crisis, ‘We were seeing things that were 25-standard-deviation events, several days in a row,’ he was discussing financial prices as if they can be accurately modelled by a normal distribution, and implying that events that should happen once every 10135 years (the Universe is only ~1.4×1010 years old) were occurring ‘several days in a row’. He was either ignorant of basic statistics (unlikely) or taking advantage of the statistical ignorance of his audience. Actually, we have known for a long time that financial prices are not well modelled using normal distributions because they greatly underestimate the likelihood of bubbles and crashes. If politicians don’t know what ‘standard deviation’ means, it is obviously impossible for them to contribute much to detailed ideas on how to improve bank regulation. It is not hard to understand standard deviation and there is no excuse for this situation to continue for another generation.

** However, there is also a danger in the use of statistical models based on ‘big data’ analysis – ‘overfitting’ models and wrongly inferring a ‘signal’ from what is actually ‘noise’. We usually a) have a noisy data set and b) an inadequate theoretical understanding of the system, so we do not know how accurately the data represents some underlying structure (if there is such a structure). We have to infer a structure despite these two problems. It is easy in these circumstances to ‘overfit’ a model – to make it twist and turn to fit more of the data than we should, but then we are fitting it not to the signal but to the noise. ‘Overfit’ models can seem to explain more of the variance in the data – but they do this by fitting noise rather than signal (Silver, op. cit).

This error is seen repeatedly in forecasting, and can afflict even famous scientists. For example, Freeman Dyson tells a short tale about how, in 1953, he trekked to Chicago to show Fermi the results of a new physics model for the strong nuclear force. Fermi dismissed his idea immediately as having neither ‘a clear physical picture of the process that you are calculating’ nor ‘a precise and self-consistent mathematical formalism’. When Dyson pointed to the success of his model, Fermi quoted von Neumann,  ‘With four parameters I can fit an elephant, and with five I can make him wiggle his trunk’, thus saving Dyson from wasting years on a wrong theory (A meeting with Enrico Fermi, by Freeman Dyson). Imagine how often people who think they have a useful model in areas not nearly as well-understood as nuclear physics lack a Fermi to examine it carefully.

There have been eleven recessions since 1945 but people track millions of statistics. Inevitably, people will ‘overfit’ many of these statistics to model historical recessions then ‘predict’ future ones.  A famous example is the Superbowl factor. For 28 years out of 31, the winner of the Superbowl correctly ‘predicted’ whether the stock exchange rose or fell. A standard statistical test ‘would have implied that there was only about a 1-in-4,700,000 possibility that the relationship had emerged from chance alone.’ Just as someone will win the lottery, some arbitrary statistics will correlate with the thing you are trying to predict just by chance (Silver)

*** Many of these wrong forecasts were because the events were ‘out of sample’. What does this mean? Imagine you’ve taken thousands of car journeys and never had a crash. You want to make a prediction about your next journey. However, in the past you have never driven drunk. This time you are drunk. Your prediction is therefore out of sample. Predictions of US housing data were based on past data but there was no example of such huge leveraged price rises in the historical data. Forecasters who looked at Japan’s experience in the 1980’s better realised the danger. (Silver)

**** The old Technical Faculty of the KGB Higher School (rebaptised after 1991) ran similar courses; one of its alumni is Yevgeny Kaspersky, whose company first publicly warned of the cyberweapons Stuxnet and Flame (and who still works closely with his old colleagues). It would be interesting to collect information on elite intelligence and special forces training programmes. E.g. Post-9/11, US special forces (acknowledged and covert) have greatly altered including adding intelligence roles that were previously others’ responsibility or regarded as illegal for DOD employees. How does what is regarded as ‘core training’ for such teams vary, how is it changing, and why are some better than others at decisions under pressure and surviving disaster?