Effective action #4b: ‘Expertise’, prediction and noise, from the NHS killing people to Brexit

In part A I looked at extreme sports as some background to the question of true expertise and the crucial nature of fast high quality feedback.

This blog looks at studies comparing expertise in many fields over decades, including work by Tetlock and Kahneman, and problems like — why people don’t learn to use even simple tools to stop children dying unnecessarily. There is a summary of some basic lessons at the end.

The reason for writing about this is that we will only improve the performance of government (at individual, team and institutional levels) if we reflect on:

  • what expertise really is and why do some very successful fields cultivate it effectively while others, like government, do not;
  • how to select much higher quality people (it’s insane people as ignorant and limited as me can have the influence we do in the way we do — us limited duffers can help in limited ways but why do we deliberately exclude ~100% of the most intelligent, talented, relentless, high performing people from fields with genuine expertise, why do we not have people like Fields Medallist Tim Gowers or Michael Nielsen as Chief Scientist  sitting ex officio in Cabinet?);
  • how to train people effectively to develop true expertise in skills relevant to government: it needs different intellectual content (PPE/economics are NOT good introductory degrees) and practice in practical skills (project management, making predictions and in general ‘thinking rationally’) with lots of fast, accurate feedback;
  • how to give them effective tools: e.g the Cabinet Room is worse in this respect than it was in July 1914 — at least then the clock and fireplace worked, and Lord Salisbury in the 1890s would walk round the Cabinet table gathering papers to burn in the grate — while today No10 is decades behind the state-of-the-art in old technologies like TV, doesn’t understand simple tools like checklists, and is nowhere with advanced technologies;
  • and how to ‘program’ institutions differently so that 1) people are more incentivised to optimise things we want them to optimise, like error-correction and predictive accuracy, and less incentivised to optimise bureaucratic process, prestige, and signalling as our institutions now do to a dangerous extent, and, connected, so that 2) institutions are much better at building high performance teams rather than continue normal rules that make this practically illegal, and so that 3) we have ‘immune systems’ to minimise the inevitable failures of even the best people and teams .

In SW1 now, those at the apex of power practically never think in a serious way about the reasons for the endemic dysfunctional decision-making that constitutes most of their daily experience or how to change it. What looks like omnishambles to the public and high performers in technology or business is seen by Insiders, always implicitly and often explicitly, as ‘normal performance’. ‘Crises’ such as the collapse of Carillion or our farcical multi-decade multi-billion ‘aircraft carrier’ project occasionally provoke a few days of headlines but it’s very rare anything important changes in the underlying structures and there is no real reflection on system failure.

This fact is why, for example, a startup created in a few months could win a referendum that should have been unwinnable. It was the systemic and consistent dysfunction of Establishment decision-making systems over a long period, with very poor mechanisms for good accurate feedback from reality, that created the space for a guerrilla operation to exploit.

This makes it particularly ironic that even after Westminster and Whitehall have allowed their internal consensus about UK national strategy to be shattered by the referendum, there is essentially no serious reflection on this system failure. It is much more psychologically appealing for Insiders to blame ‘lies’ (Blair and Osborne really say this without blushing), devilish use of technology to twist minds and so on. Perhaps the most profound aspect of broken systems is they cannot reflect on the reasons why they’re broken  — never mind take effective action. Instead of serious thought, we have high status Insiders like Campbell reduced to bathos with whining on social media about Brexit ‘impacting mental health’. This lack of reflection is why Remain-dominated Insiders lurched from failure over the referendum to failure over negotiations. OODA loops across SW1 are broken and this is very hard to fix — if you can’t orient to reality how do you even see your problem well? (NB. It should go without saying that there is a faction of pro-Brexit MPs, ‘campaigners’ and ‘pro-Brexit economists’ who are at least as disconnected from reality, often more, as the May/Hammond bunker.)

Screenshot 2018-06-05 10.05.19

In the commercial world, big companies mostly die within a few decades because they cannot maintain an internal system to keep them aligned to reality plus startups pop up. These two factors create learning at a system level — there is lots of micro failure but macro productivity/learning in which useful information is compressed and abstracted. In the political world, big established failing systems control the rules, suck in more and more resources rather than go bust, make it almost impossible for startups to contribute and so on. Even failures on the scale of the 2008 Crash or the 2016 referendum do not necessarily make broken systems face reality, at least quickly. Watching Parliament’s obsession with trivia in the face of the Cabinet’s and Whitehall’s contemptible failure to protect the interests of millions in the farcical Brexit negotiations is like watching the secretary to the Singapore Golf Club objecting to guns being placed on the links as the Japanese troops advanced.

Neither of the main parties has internalised the reality of these two crises. The Tories won’t face reality on things like corporate looting and the NHS, Labour won’t face reality on things like immigration and the limits of bureaucratic centralism. Neither can cope with the complexity of Brexit and both just look like I would look like in the ring with a professional fighter — baffled, terrified and desperate for a way to escape. There are so many simple ways to improve performance — and their own popularity! — but the system is stuck in such a closed loop it wilfully avoids seeing even the most obvious things and suppresses Insiders who want to do things differently…

But… there is a network of almost entirely younger people inside or close to the system thinking ‘we could do so much better than this’. Few senior Insiders are interested in these questions but that’s OK — few of them listened before the referendum either. It’s not the people now in power and running the parties and Whitehall who will determine whether we make Brexit a platform to contribute usefully to humanity’s biggest challenges but those that take over.

Doing better requires reflecting on what we know about real expertise…

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How to distinguish between fields dominated by real expertise and those dominated by confident ‘experts’ who make bad predictions?

We know a lot about the distinction between fields in which there is real expertise and fields dominated by bogus expertise. Daniel Kahneman, who has published some of the most important research about expertise and prediction, summarises the two fundamental tests to ask about a field: 1) is there enough informational structure in the environment to allow good predictions, and 2) is there timely and effective feedback that enables error-correction and learning.

‘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.)

In fields where these two elements are present there is genuine expertise and people build new knowledge on the reliable foundations of previous knowledge. Some fields make a transition from stories (e.g Icarus) and authority (e.g ‘witch doctor’) to quantitative models (e.g modern aircraft) and evidence/experiment (e.g some parts of modern medicine/surgery). As scientists have said since Newton, they stand on the shoulders of giants.

How do we assess predictions / judgement about the future?

‘Good judgment is often gauged against two gold standards – coherence and correspondence. Judgments are coherent if they demonstrate consistency with the axioms of probability theory or propositional logic. Judgments are correspondent if they agree with ground truth. When gold standards are unavailable, silver standards such as consistency and discrimination can be used to evaluate judgment quality. Individuals are consistent if they assign similar judgments to comparable stimuli, and they discriminate if they assign different judgments to dissimilar stimuli.

‘Coherence violations range from base rate neglect and confirmation bias to overconfidence and framing effects (Gilovich, Griffith & Kahneman, 2002; Kahneman, Slovic & Tversky, 1982). Experts are not immune. Statisticians (Christensen-Szalanski & Bushyhead, 1981), doctors (Eddy, 1982), and nurses (Bennett, 1980) neglect base rates. Physicians and intelligence professionals are susceptible to framing effects and financial investors are prone to overconfidence.

‘Research on correspondence tells a similar story. Numerous studies show that human predictions are frequently inaccurate and worse than simple linear models in many domains (e.g. Meehl, 1954; Dawes, Faust & Meehl, 1989). Once again, expertise doesn’t necessarily help. Inaccurate predictions have been found in parole officers, court judges, investment managers in the US and Taiwan, and politicians. However, expert predictions are better when the forecasting environment provides regular, clear feedback and there are repeated opportunities to learn (Kahneman & Klein, 2009; Shanteau, 1992). Examples include meteorologists, professional bridge players, and bookmakers at the racetrack, all of whom are well-calibrated in their own domains.‘ (Tetlock, How generalizable is good judgment?, 2017.)

In another 2017 piece Tetlock explored the studies furtherIn the 1920s researchers built simple models based on expert assessments of 500 ears of corn and the price they would fetch in the market. They found that ‘to everyone’s surprise, the models that mimicked the judges’ strategies nearly always performed better than the judges themselves’ (Tetlock, cf. ‘What Is in the Corn Judge’s Mind?’, Journal of American Society for Agronomy, 1923). Banks found the same when they introduced models for credit decisions.

‘In other fields, from predicting the performance of newly hired salespeople to the bankruptcy risks of companies to the life expectancies of terminally ill cancer patients, the experience has been essentially the same. Even though experts usually possess deep knowledge, they often do not make good predictions

When humans make predictions, wisdom gets mixed with “random noise.”… Bootstrapping, which incorporates expert judgment into a decision-making model, eliminates such inconsistencies while preserving the expert’s insights. But this does not occur when human judgment is employed on its own…

In fields ranging from medicine to finance, scores of studies have shown that replacing experts with models of experts produces superior judgments. In most cases, the bootstrapping model performed better than experts on their own. Nonetheless, bootstrapping models tend to be rather rudimentary in that human experts are usually needed to identify the factors that matter most in making predictions. Humans are also instrumental in assigning scores to the predictor variables (such as judging the strength of recommendation letters for college applications or the overall health of patients in medical cases). What’s more, humans are good at spotting when the model is getting out of date and needs updating…

Human experts typically provide signal, noise, and bias in unknown proportions, which makes it difficult to disentangle these three components in field settings. Whether humans or computers have the upper hand depends on many factors, including whether the tasks being undertaken are familiar or unique. When tasks are familiar and much data is available, computers will likely beat humans by being data-driven and highly consistent from one case to the next. But when tasks are unique (where creativity may matter more) and when data overload is not a problem for humans, humans will likely have an advantage…

One might think that humans have an advantage over models in understanding dynamically complex domains, with feedback loops, delays, and instability. But psychologists have examined how people learn about complex relationships in simulated dynamic environments (for example, a computer game modeling an airline’s strategic decisions or those of an electronics company managing a new product). Even after receiving extensive feedback after each round of play, the human subjects improved only slowly over time and failed to beat simple computer models. This raises questions about how much human expertise is desirable when building models for complex dynamic environments. The best way to find out is to compare how well humans and models do in specific domains and perhaps develop hybrid models that integrate different approaches.‘ (Tetlock)

Kahneman also recently published new work relevant to this.

Research has confirmed that in many tasks, experts’ decisions are highly variable: valuing stocks, appraising real estate, sentencing criminals, evaluating job performance, auditing financial statements, and more. The unavoidable conclusion is that professionals often make decisions that deviate significantly from those of their peers, from their own prior decisions, and from rules that they themselves claim to follow.’

In general organisations spend almost no effort figuring out how noisy the predictions made by senior staff are and how much this costs. Kahneman has done some ‘noise audits’ and shown companies that management make MUCH more variable predictions than people realise.

‘What prevents companies from recognizing that the judgments of their employees are noisy? The answer lies in two familiar phenomena: Experienced professionals tend to have high confidence in the accuracy of their own judgments, and they also have high regard for their colleagues’ intelligence. This combination inevitably leads to an overestimation of agreement. When asked about what their colleagues would say, professionals expect others’ judgments to be much closer to their own than they actually are. Most of the time, of course, experienced professionals are completely unconcerned with what others might think and simply assume that theirs is the best answer. One reason the problem of noise is invisible is that people do not go through life imagining plausible alternatives to every judgment they make.

‘High skill develops in chess and driving through years of practice in a predictable environment, in which actions are followed by feedback that is both immediate and clear. Unfortunately, few professionals operate in such a world. In most jobs people learn to make judgments by hearing managers and colleagues explain and criticize—a much less reliable source of knowledge than learning from one’s mistakes. Long experience on a job always increases people’s confidence in their judgments, but in the absence of rapid feedback, confidence is no guarantee of either accuracy or consensus.’

Reviewing the point that Tetlock makes about simple models beating experts in many fields, Kahneman summarises the evidence:

‘People have competed against algorithms in several hundred contests of accuracy over the past 60 years, in tasks ranging from predicting the life expectancy of cancer patients to predicting the success of graduate students. Algorithms were more accurate than human professionals in about half the studies, and approximately tied with the humans in the others. The ties should also count as victories for the algorithms, which are more cost-effective…

‘The common assumption is that algorithms require statistical analysis of large amounts of data. For example, most people we talk to believe that data on thousands of loan applications and their outcomes is needed to develop an equation that predicts commercial loan defaults. Very few know that adequate algorithms can be developed without any outcome data at all — and with input information on only a small number of cases. We call predictive formulas that are built without outcome data “reasoned rules,” because they draw on commonsense reasoning.

‘The construction of a reasoned rule starts with the selection of a few (perhaps six to eight) variables that are incontrovertibly related to the outcome being predicted. If the outcome is loan default, for example, assets and liabilities will surely be included in the list. The next step is to assign these variables equal weight in the prediction formula, setting their sign in the obvious direction (positive for assets, negative for liabilities). The rule can then be constructed by a few simple calculations.

The surprising result of much research is that in many contexts reasoned rules are about as accurate as statistical models built with outcome data. Standard statistical models combine a set of predictive variables, which are assigned weights based on their relationship to the predicted outcomes and to one another. In many situations, however, these weights are both statistically unstable and practically unimportant. A simple rule that assigns equal weights to the selected variables is likely to be just as valid. Algorithms that weight variables equally and don’t rely on outcome data have proved successful in personnel selection, election forecasting, predictions about football games, and other applications.

‘The bottom line here is that if you plan to use an algorithm to reduce noise, you need not wait for outcome data. You can reap most of the benefits by using common sense to select variables and the simplest possible rule to combine them…

‘Uncomfortable as people may be with the idea, studies have shown that while humans can provide useful input to formulas, algorithms do better in the role of final decision maker. If the avoidance of errors is the only criterion, managers should be strongly advised to overrule the algorithm only in exceptional circumstances.

Jim Simons is a mathematician and founder of the world’s most successful ‘quant fund’, Renaissance Technologies. While market prices appear close to random and are therefore extremely hard to predict, they are not quite random and the right models/technology can exploit these small and fleeting opportunities. One of the lessons he learned early was: Don’t turn off the model and go with your gut. At Renaissance, they trust models over instincts. The Bridgewater hedge fund led by Ray Dalio is similar. After near destruction early in his career, Dalio explicitly turned towards explicit model building as the basis for decisions combined with radical attempts to create an internal system that incentivises the optimisation of error-correction. It works.

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People fail to learn from even the great examples of success and the simplest lessons

One of the most interesting meta-lessons of studying high performance, though, is that simply demonstrating extreme success does NOT lead to much learning. For example:

  • ARPA and PARC created the internet and PC. The PARC research team was an extraordinary collection of about two dozen people who were managed in a very unusual way that created super-productive processes extremely different to normal bureaucracies. XEROX, which owned PARC, had the entire future of the computer industry in its own hands, paid for by its own budgets, and it simultaneously let Bill Gates and Steve Jobs steal everything and XEROX then shut down the research team that did it. And then, as Silicon Valley grew on the back of these efforts, almost nobody, including most of the billionaires who got rich from the dynamics created by ARPA-PARC, studied the nature of the organisation and processes and copied it. Even today, those trying to do edge-of-the-art research in a similar way to PARC right at the heart of the Valley ecosystem are struggling for long-term patient funding. As Alan Kay, one of the PARC team, said, ‘The most interesting thing has been the contrast between appreciation/exploitation of the inventions/contributions [of PARC] versus the almost complete lack of curiosity and interest in the processes that produced them. ARPA survived being abolished in the 1970s but it was significantly changed and is no longer the freewheeling place that it was in the 1960s when it funded the internet. In many ways DARPA’s approach now is explicitly different to the old ARPA (the addition of the ‘D’ was a sign of internal bureaucratic changes).

Screenshot 2018-06-05 14.55.00

  • ‘Systems management’ was invented in the 1950s and 1960s (partly based on wartime experience of large complex projects) to deal with the classified ICBM project and Apollo. It put man on the moon then NASA largely abandoned the approach and reverted to being (relative to 1963-9) a normal bureaucracy. Most of Washington has ignored the lessons ever since — look for example at the collapse of ObamaCare’s rollout, after which Insiders said ‘oh, looks like it was a system failure, wonder how we deal with this’, mostly unaware that America had developed a successful approach to such projects half a century earlier. This is particularly interesting given that China also studied Mueller’s approach to systems management in Apollo and as we speak is copying it in projects across China. The EU’s bureaucracy is, like Whitehall, an anti-checklist to high level systems management — i.e they violate almost every principle of effective action.
  • Buffett and Munger are the most successful investment partnership in world history. Every year for half a century they have explained some basic principles, particularly concerning incentives, behind organisational success. Practically no public companies take their advice and all around us in Britain we see vast corporate looting and politicians of all parties failing to act — they don’t even read the Buffett/Munger lessons and think about them. Even when given these lessons to read, they won’t read them (I know this because I’ve tried).

Perhaps you’re thinking — well, learning from these brilliant examples might be intrinsically really hard, much harder than Cummings thinks. I don’t think this is quite right. Why? Partly because millions of well-educated and normally-ethical people don’t learn even from much simpler things.

I will explore this separately soon but I’ll give just one example. The world of healthcare unnecessarily kills and injures people on a vast scale. Two aspects of this are 1) a deep resistance to learning from the success of very simple tools like checklists and 2) a deep resistance to face the fact that most medical experts do not understand statistics properly and their routine misjudgements cause vast suffering, plus warped incentives encourage widespread lies about statistics and irrational management. E.g People are constantly told things like ‘you’ve tested positive for X therefore you have X’ and they then kill themselves. We KNOW how to practically eliminate certain sorts of medical injury/death. We KNOW how to teach and communicate statistics better. (Cf. Professor Gigerenzer for details. He was the motivation for including things like conditional probabilities in the new National Curriculum.) These are MUCH simpler than building ICBMs, putting man on the moon, creating the internet and PC, or being great investors. Yet our societies don’t do them.

Why?

Because we do not incentivise error-correction and predictive accuracy. People are not incentivised to consider the cost of their noisy judgements. Where incentives and culture are changed, performance magically changes. It is the nature of the systems, not (mostly) the nature of the people, that is the crucial ingredient in learning from proven simple success. In healthcare like in government generally, people are incentivised to engage in wasteful/dangerous signalling to a terrifying degree — not rigorous thinking and not solving problems.

I have experienced the problem with checklists first hand in the Department for Education when trying to get the social worker bureaucracy to think about checklists in the context of avoiding child killings like Baby P. Professionals tend to see them as undermining their status and bureaucracies fight against learning, even when some great officials try really hard (as some in the DfE did such as Pamela Dow and Victoria Woodcock). ‘Social work is not the same as an airline Dominic’. No shit. Airlines can handle millions of people without killing one of them because they align incentives with predictive accuracy and error-correction.

Some appalling killings are inevitable but the social work bureaucracy will keep allowing unnecessary killings because they will not align incentives with error-correction. Undoing flawed incentives threatens the system so they’ll keep killing children instead — and they’re not particularly bad people, they’re normal people in a normal bureaucracy. The pilot dies with the passengers. The ‘CEO’ on over £150,000 a year presiding over another unnecessary death despite constantly increasing taxpayers money pouring in? Issue a statement that ‘this must never happen again’, tell the lawyers to redact embarrassing cockups on the grounds of ‘protecting someone’s anonymity’ (the ECHR is a great tool to cover up death by incompetence), fuck off to the golf course, and wait for the media circus to move on.

Why do so many things go wrong? Because usually nobody is incentivised to work relentlessly to suppress entropy, never mind come up with something new.

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We can see some reasonably clear conclusions from decades of study on expertise and prediction in many fields.

  • Some fields are like extreme sport or physics: genuine expertise emerges because of fast effective feedback on errors.
  • Abstracting human wisdom into models often works better than relying on human experts as models are often more consistent and less noisy.
  • Models are also often cheaper and simpler to use.
  • Models do not have to be complex to be highly effective — quite the opposite, often simpler models outperform more sophisticated and expensive ones.
  • In many fields (which I’ve explored before but won’t go into again here) low tech very simple checklists have been extremely effective: e.g flying aircraft or surgery.
  • Successful individuals like Warren Buffett and Ray Dalio also create cognitive checklists to trap and correct normal cognitive biases that degrade individual and team performance.
  • Fields make progress towards genuine expertise when they make a transition from stories (e.g Icarus) and authority (e.g ‘witch doctor’) to quantitative models (e.g modern aircraft) and evidence/experiment (e.g some parts of modern medicine/surgery).
  • In the intellectual realm, maths and physics are fields dominated by genuine expertise and provide a useful benchmark to compare others against. They are also hierarchical. Social sciences have little in common with this.
  • Even when we have great examples of learning and progress, and we can see the principles behind them are relatively simple and do not require high intelligence to understand, they are so psychologically hard and run so counter to the dynamics of normal big organisations, that almost nobody learns from them. Extreme success is ‘easy to learn from’ in one sense and ‘the hardest thing in the world to learn from’ in another sense.

It is fascinating how remarkably little interest there is in the world of politics/government, and social sciences analysing politics/government, about all this evidence. This is partly because politics/government is an anti-learning and anti-expertise field, partly because the social sciences are swamped by what Feynman called ‘cargo cult science’ with very noisy predictions, little good feedback and learning, and a lot of chippiness at criticism whether it’s from statistics experts or the ‘ignorant masses’. Fields like ‘education research’ and ‘political science’ are particularly dreadful and packed with charlatans but much of economics is not much better (much pro- and anti-Brexit mainstream economics is classic ‘cargo cult’).

I have found there is overwhelmingly more interest in high technology circles than in government circles, but in high technology circles there is also a lot of incredulity and naivety about how government works — many assume politicians are trying and failing to achieve high performance and don’t realise that in fact nobody is actually trying. This illusion extends to many well-connected businessmen who just can’t internalise the reality of the apex of power. I find that uneducated people on 20k living hundreds of miles from SW1 generally have a more accurate picture of daily No10 work than extremely well-connected billionaires.

This is all sobering and is another reason to be pessimistic about the chances of changing government from ‘normal’ to ‘high performance’ — but, pessimism of the intellect, optimism of the will…

If you are in Whitehall now watching the Brexit farce or abroad looking at similar, you will see from page 26 HERE a checklist for how to manage complex government projects at world class levels (if you find this interesting then read the whole paper). I will elaborate on this. I am also thinking about a project to look at the intersection of (roughly) five fields in order to make large improvements in the quality of people, ideas, tools, and institutions that determine political/government decisions and performance:

  • the science of prediction across different fields (e.g early warning systems, the Tetlock/IARPA project showing dramatic performance improvements),
  • what we know about high performance (individual/team/organisation) in different fields (e.g China’s application of ‘systems management’ to government),
  • technology and tools (e.g Bret Victor’s work, Michael Nielsen’s work on cognitive technologies, work on human-AI ‘minotaur’ teams),
  • political/government decision making affecting millions of people and trillions of dollars (e.g WMD, health), and
  • communication (e.g crisis management, applied psychology).

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.

How to jump from the Idea to Reality? More soon…


Ps. Just as I was about to hit publish on this, the DCMS Select Committee released their report on me. The sentence about the Singapore golf club at the top comes to mind.

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.

On the referendum #23, a year after victory: ‘a change of perspective is worth 80 IQ points’ & ‘how to capture the heavens’

‘Just like all British governments, they will act more or less in a hand to mouth way on the spur of the moment, but they will not think out and adopt a steady policy.’ Earl Cromer, 1896.

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 management and head of the Apollo programme to put man on the moon.

Traditional cultures, those that all humans lived in until quite recently and which still survive in pockets, don’t realise that they are living inside a particular perspective. They think that what they see is ‘reality’. It is, obviously, not their fault. It is not because they are stupid. It is a historical accident that they did/do not have access to mental models that help more accurate thinking about reality.

Westminster and the other political cultures dotted around the world are similar to these traditional cultures. They think they they are living in ‘reality’. The MPs and pundits get up, read each other, tweet at each other, give speeches, send press releases, have dinner, attack, fuck or fight each other, do the same tomorrow and think ‘this is reality’. Like traditional cultures they are wrong. They are living inside a particular perspective that enormously distorts reality. 

They are trapped in thinking about today and their careers. They are trapped in thinking about incremental improvements. Almost nobody has ever been part of a high performance team responsible for a complex project. The speciality is a hot take to explain post facto what one cannot predict. They mostly don’t know what they don’t know. They don’t understand the decentralised information processing that allows markets to enable complex coordination. They don’t understand how scientific research works and they don’t value it. Their daily activity is massively constrained by the party and state bureaucracies that incentivise behaviour very different to what humanity needs to create long-term value. As Michael Nielsen (author of Reinventing Science) writes:

‘[M]uch of our intellectual elite who think they have “the solutions” have actually cut themselves off from understanding the basis for much of the most important human progress.’

Unlike traditional cultures, our modern political cultures don’t have the excuse of our hunter-gatherer ancestors. We could do better. But it is very very hard to escape the core imperatives that make big bureaucracies — public companies as well as state bureaucracies — so bad at learning. Warren Buffet explained decades ago how institutions actively fight against learning and fight to stay in a closed and vicious feedback loop:

‘My most surprising discovery: the overwhelming importance in business of an unseen force that we might call “the institutional imperative”. In business school, I was given no hint of the imperative’s existence and I did not intuitively understand it when I entered the business world. I thought then that decent, intelligence, and experienced managers would automatically make rational business decisions. But I learned the hard way that isn’t so. Instead rationality frequently wilts when the institutional imperative comes into play.

‘For example, 1) As if governed by Newton’s First Law, any institution will resist any change in its current direction. 2) … Corporate projects will materialise to soak up available funds. 3) Any business craving of the leader, however foolish, will quickly be supported by … his troops. 4) The behaviour of peer companies … will be mindlessly imitated.’

Almost nobody really learns from the world’s most successful investor about investing and how to run a successful business with good corporate governance. (People read what he writes but almost no investors choose to operate long-term like him, I think it is still true that not a single public company has copied his innovations with corporate governance like ‘no pay for company directors’, and governments have consistently rejected his and Munger’s advice about controlling the looting of public companies by management.) Almost nobody really learns how to do things better from the experience of dealing with this ‘institutional imperative’. We fail over and over again in the same way, trusting in institutions that are programmed to fail.

It is very very hard for humans to lift our eyes from today and to go out into the future and think about what could be done to bring the future back to the present. Like ants crawling around on the leaf, we political people only know our leaf.

Science has shown us a different way. Newton looked up from his leaf, looked far away from today, and created a new perspective — a new model of reality. It took an extreme genius to discover something like calculus but once discovered billions of people who are far from being geniuses can use this new perspective. Science advances by turning new ideas into standard ideas so each generation builds on the last.

Politics does the equivalent of constantly trying to reinvent children’s arithmetic and botching it. It does not build reliable foundations of knowledge. Archimedes is no longer cutting edge. Thucydides and Sun Tzu are still cutting edge. Even though Tetlock and others have shown how to start making similar progress with politics, our political cultures fiercely resist learning and fight ferociously to stay in closed and failing feedback loops.

In many ways our political culture has regressed as it has become more and more audio-visual and less and less literate. (Only 31% of US college graduates can read at a basic level. I’d guess it’s similar here. See end.) I’ve experimented with the way Jeff Bezos runs meetings at Amazon: i.e start the meeting with giving people a 5-10 page memo to read. Impossible in Westminster, nobody will sit and read like that! Officials have tried and failed for a year to get senior ministers to engage with complex written material about the EU negotiations. TV news dominates politics and is extremely low-bandwidth: it contains a few hundred words and rarely uses graphics properly. Evan Davis illustrates a comment about ‘going down the plughole’ with a picture of water down a plughole and Nick Robinson illustrates a comment about ‘the economy taking off’ with a picture of a plane taking off. The constant flow of bullshit from the likes of Robert Peston and Jon Snow dominates the medium because competition has been impossible until recently. BUT, although technology is making these charlatans less relevant (good) it also creates new problems and will not necessarily improve the culture.

Watching political news makes you dumber — switch it off and read books! If you work in it, either QUIT or go on holiday and come back determined to subvert it. How? Start with a previous blog which has some ideas, like tracking properly which people have a record of getting things right and wrong. Every editor I’ve suggested this to winces and says ‘impossible’. Insiders fear accountability and competition.

Today, the anniversary of the referendum, is a good day to forget the babble in the bubble and think about lessons from another project that changed the world, the famous ARPA/PARC team of the 1960s and 1970s.

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ARPA/PARC and ‘capturing the heavens’: The best way to predict the future is to invent it

The panic over Sputnik brought many good things such as a huge increase in science funding. America also created the Advanced Research Projects Agency (ARPA, which later added ‘Defense’ and became DARPA). Its job was to fund high risk / high payoff technology development. In the 1960s and 1970s, a combination of unusual people and unusually wise funding from ARPA created a community that in turn invented the internet, or ‘the intergalactic network’ as Licklider originally called it, and the personal computer. One of the elements of this community was PARC, a research centre working for Xerox. As Bill Gates said, he and Steve Jobs essentially broke into PARC, stole their ideas, and created Microsoft and Apple.

The ARPA/PARC project has created over 35 TRILLION DOLLARS of value for society and counting.

The whole story is fascinating in many ways. I won’t go into the technological aspects. I just want to say something about the process.

What does a process that produces ideas that change the world look like?

One of the central figures was Alan Kay. One of the most interesting things about the project is that not only has almost nobody tried to repeat this sort of research but the business world has even gone out of its way to spread mis-information about it because it was seen as so threatening to business-as-usual.

I will sketch a few lessons from one of Kay’s pieces but I urge you to read the whole thing.

‘This is what I call “The power of the context” or “Point of view is worth 80 IQ points”. Science and engineering themselves are famous examples, but there are even more striking processes within these large disciplines. One of the greatest works of art from that fruitful period of ARPA/PARC research in the 60s and 70s was the almost invisible context and community that catalysed so many researchers to be incredibly better dreamers and thinkers. That it was a great work of art is confirmed by the world-changing results that appeared so swiftly, and almost easily. That it was almost invisible, in spite of its tremendous success, is revealed by the disheartening fact today that, as far as I’m aware, no governments and no companies do edge-of-the-art research using these principles.’

‘[W]hen I think of ARPA/PARC, I think first of good will, even before brilliant people… Good will and great interest in graduate students as “world-class researchers who didn’t have PhDs yet” was the general rule across the ARPA community.

‘[I]t is no exaggeration to say that ARPA/PARC had “visions rather than goals” and “funded people, not projects”. The vision was “interactive computing as a complementary intellectual partner for people pervasively networked world-wide”. By not trying to derive specific goals from this at the funding side, ARPA/PARC was able to fund rather different and sometimes opposing points of view.

‘The pursuit of Art always sets off plans and goals, but plans and goals don’t always give rise to Art. If “visions not goals” opens the heavens, it is important to find artistic people to conceive the projects.

‘Thus the “people not projects” principle was the other cornerstone of ARPA/PARC’s success. Because of the normal distribution of talents and drive in the world, a depressingly large percentage of organizational processes have been designed to deal with people of moderate ability, motivation, and trust. We can easily see this in most walks of life today, but also astoundingly in corporate, university, and government research. ARPA/PARC had two main thresholds: self-motivation and ability. They cultivated people who “had to do, paid or not” and “whose doings were likely to be highly interesting and important”. Thus conventional oversight was not only not needed, but was not really possible. “Peer review” wasn’t easily done even with actual peers. The situation was “out of control”, yet extremely productive and not at all anarchic.

‘”Out of control” because artists have to do what they have to do. “Extremely productive” because a great vision acts like a magnetic field from the future that aligns all the little iron particle artists to point to “North” without having to see it. They then make their own paths to the future. Xerox often was shocked at the PARC process and declared it out of control, but they didn’t understand that the context was so powerful and compelling and the good will so abundant, that the artists worked happily at their version of the vision. The results were an enormous collection of breakthroughs.

‘Our game is more like art and sports than accounting, in that high percentages of failure are quite OK as long as enough larger processes succeed… [I]n most processes today — and sadly in most important areas of technology research — the administrators seem to prefer to be completely in control of mediocre processes to being “out of control” with superproductive processes. They are trying to “avoid failure” rather than trying to “capture the heavens”.

‘All of these principles came together a little over 30 years ago to eventually give rise to 1500 Altos, Ethernetworked to: each other, Laserprinters, file servers and the ARPAnet, distributed to many kinds of end-users to be heavily used in real situations. This anticipated the commercial availability of this genre by 10-15 years. The best way to predict the future is to invent it.

‘[W]e should realize that many of the most important ARPA/PARC ideas haven’t yet been adopted by the mainstream. For example, it is amazing to me that most of Doug Engelbart’s big ideas about “augmenting the collective intelligence of groups working together” have still not taken hold in commercial systems. What looked like a real revolution twice for end-users, first with spreadsheets and then with Hypercard, didn’t evolve into what will be commonplace 25 years from now, even though it could have. Most things done by most people today are still “automating paper, records and film” rather than “simulating the future”. More discouraging is that most computing is still aimed at adults in business, and that aimed at nonbusiness and children is mainly for entertainment and apes the worst of television. We see almost no use in education of what is great and unique about computer modeling and computer thinking. These are not technological problems but a lack of perspective. Must we hope that the open-source software movements will put things right?

‘The ARPA/PARC history shows that a combination of vision, a modest amount of funding, with a felicitous context and process can almost magically give rise to new technologies that not only amplify civilization, but also produce tremendous wealth for the society. Isn’t it time to do this again by Reason, even with no Cold War to use as an excuse? How about helping children of the world grow up to think much better than most adults do today? This would truly create “The Power of the Context”.’

Note how this story runs contrary to how free market think tanks and pundits describe technological development. The impetus for most of this development came from government funding, not markets.

Also note that every attempt since the 1950s to copy ARPA and JASON (the semi-classified group that partly gave ARPA its direction) in the UK has been blocked by Whitehall. The latest attempt was in 2014 when the Cabinet Office swatted aside the idea. Hilariously its argument was ‘DARPA has had a lot of failures’ thus demonstrating extreme ignorance about the basic idea — the whole point is you must have failures and if you don’t have lots of failures then you are failing!

People later claimed that while PARC may have changed the world it never made any money for XEROX. This is ‘absolute bullshit’ (Kay). It made billions from the laser printer alone and overall Xerox made 250 times what they invested in PARC before they went bust. In 1983 they fired Bob Taylor, the manager of PARC and the guy who made it all happen.

‘They hated [Taylor] for the very reason that most companies hate people who are doing something different, because it makes middle and upper management extremely uncomfortable. The last thing they want to do is make trillions, they want to make a few millions in a comfortable way’ (Kay).

Someone finally listened to Kay recently. ‘YC Research’, the research arm of the world’s most successful (by far) technology incubator, is starting to fund people in this way. I am not aware of any similar UK projects though I know that a small network of people are thinking again about how something like this could be done here. If you can help them, take a risk and help them! Someone talk to science minister Jo Johnson but be prepared for the Treasury’s usual ignorant bullshit — ‘what are we buying for our money, and how can we put in place appropriate oversight and compliance?’ they will say!

Why is this relevant to the referendum?

As we ponder the future of the UK-EU relationship shaped amid the farce of modern Whitehall, we should think hard about the ARPA/PARC example: how a small group of people can make a huge breakthrough with little money but the right structure, the right ways of thinking, and the right motives.

Those of us outside the political system thinking ‘we know we can do so much better than this but HOW can we break through the bullshit?’ need to change our perspective and gain 80 IQ points.

This real picture is a metaphor for the political culture: ad hoc solutions that are either bad or don’t scale.

Screenshot 2017-06-14 16.58.14.png

ARPA said ‘Let’s get rid of all the wires’. How do we ‘get rid of all the wires’ and build something different that breaks open the closed and failing political cultures? Winning the referendum was just one step that helps clear away dead wood but we now need to build new things.

The ARPA vision that aligned the artists ‘like little iron filings’ was:

‘Computers are destined to become interactive intellectual amplifiers for everyone in the world universally networked worldwide’ (Licklider).

We need a motivating vision aimed not at tomorrow but at changing the basic wiring of  the whole system, a vision that can align ‘the little iron filings’, and then start building for the long-term.

I will go into what I think this vision could be and how to do it another day. I think it is possible to create something new that could scale very fast and enable us to do politics and government extremely differently, as different to today as the internet and PC were to the post-war mainframes. This would enable us to build huge long-term value for humanity in a relatively short time (less than 20 years). To create it we need a process as well suited to the goal as the ARPA/PARC project was and incorporating many of its principles.

We must try to escape the current system with its periodic meltdowns and international crises. These crises move 500-1,000 times faster than that of summer 1914. Our destructive potential is at least a million-fold greater than it was in 1914. Yet we have essentially the same hierarchical command-and-control decision-making systems in place now that could not even cope with 1914 technology and pace. We have dodged nuclear wars by fluke because individuals made snap judgements in minutes. Nobody who reads the history of these episodes can think that this is viable long-term, and we will soon have another wave of innovation to worry about with autonomous robots and genetic engineering. Technology gives us no option but to try to overcome evolved instincts like destroying out-group competitors.

In a previous blog I outlined how the ‘systems management’ approach used to put man on the moon provides principles for a new approach.

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Ironically, one of the very few people in politics who understood the sort of thinking needed was … Jean Monnet, the architect of the EEC/EU! Monnet understood how to step back from today and build institutions. He worked operationally to prepare the future:

‘If there was stiff competition round the centres of power, there was practically none in the area where I wanted to work – preparing the future.’

Monnet was one of the few people in modern politics who really deserve the label ‘genius’. The story of how he wangled the creation of his institutions through the daily chaos of post-war politics is a lesson to anybody who wants to get things done.

But the institutions he created are in many ways the opposite of what the world needs. Their core operating principle is perpetual centralisation of power in the hands of an all powerful bureaucracy (Commission) and Court (ECJ). Nothing that works well in the world works like this!

Thanks to the prominence of Farage the dominant story among educated people is that those who got us out of the EU want to take us back to the pre-1914 era of hostile competing nation states. Nothing could be further from the truth. The key people in Vote Leave wanted and want not just what is best for Britain but what is best for all humanity. We want more international cooperation, not less. The problem with the EU is not that it is about international cooperation but that it is so bad at it and actually undermines it.

Britain leaving forces those with power to ask: how can all European countries trade freely and cooperate without subscribing to Monnet’s bureaucratic centralism? This will help Europe in the long-term. To those who favour this bureaucratic centralism and uniformity, reflect on the different trajectories of Europe and China post-Renaissance. In Europe, regulatory competition (so Columbus could chase funding in Spain after rejection in Portugal) brought immense gains. In China, centrally directed uniformity led to centuries of stagnation. America’s model of competitive federalism created by the founding fathers has been a far more effective engine of civilisation, growth, and new knowledge than the Monnet-Delors Single Market model.

If Britain were to focus on science and education with huge resources and a new-found seriousness, then this regulatory diversity would help not just Britain but all Europe and the global science community. We could make Britain the best place in the world to be for those who can invent the future. Like Alan Kay and his colleagues, we could create whole new industries. We could call Jeff Bezos and say, ‘Ok Jeff, you want a permanent international manned moon base, let’s talk about who does what, but not with that old rocket technology.’ No country on earth funds science as well as we already know how it could be done — that is something for Britain to do that would create real long-term value for humanity, instead of the ‘punching above our weight’ and ‘special relationship’ bullshit that passes for strategy in London. How we change our domestic institutions is within our power and will have much much greater influence on our long-term future than whatever deal is botched together with Brussels. We have the resources. But can we break the system open? If we don’t then we’re likely to go down the path we were already going down inside the EU, like the deluded Norma Desmond in Sunset Boulevard claiming ‘I am big, it’s the pictures that got small.’

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Vote Leave and ‘good will’

Although Vote Leave was enmeshed in a sort of collective lunacy we managed, barely, to fend it off from the inner working of the campaign. Much of my job (sadly) was just trying to maintain a cordon around the core team so they could deliver the campaign with as little disruption as possible. We managed this because among the core people we had great good will. The stories of the campaign focus on the lunacy, but the people who really made it work remember the goodwill.

A year ago tonight I was sitting alone in a room thinking ‘we’ve won, now…’ when the walls started rumbling. At first I couldn’t make it out then, as Tim Shipman tells the story in his definitive book on the campaign, I heard ‘Dom, Dom, DOM’ — the team had declared victory. I went next door…

Thanks to everybody who sacrificed something. As I said that night and as I said in my long blog on the campaign, I’ve been given credit I don’t deserve and which rightly belongs to others — Cleo Watson, Richard ‘Ricardo’ Howell, Brother Starkie, Oliver Lewis, Lord Suart et al. Now, let’s think about what should come next…

 

Watch Alan Kay explain how to invent the future HERE and HERE.


Ps. Kay also points out that the real computer revolution won’t happen until people fulfil the original vision of enabling children to use this powerful way of thinking:

‘The real printing revolution was a qualitative change in thought and argument that lagged the hardware inventions by almost two centuries. The special quality of computers is their ability to rapidly simulate arbitrary descriptions, and the real computer revolution won’t happen until children can learn to read, write, argue and think in this powerful new way. We should all try to make this happen much sooner than 200 or even 20 more years!’

Almost nobody in education policy is aware of the educational context for the ARPA/PARC project which also speaks volumes about the abysmal field of ‘education research/policy’.

* Re the US literacy statistic, cf. A First Look at the Literacy of America’s Adults in the 21st Century, National Assessment of Adult Literacy, U.S. Dept of Education, NCES 2006.