Dominic Cummings contrasts 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.
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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.
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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.
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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.
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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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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.



