I am on deadline at the moment, so although I promised to come up with a joke, I am afraid that the joke in question is over budget and behind schedule. Instead (thanks to Trevor for suggesting something similar with respect to the Millennium Dome), I’m going to pose an exam question, in the form of an extract from my current manuscript of the book version of The Problem Factory.

The question is: “Would you have built the Sydney Opera House? Show your working”.

As well as the questions I discuss below, I think this is a very interesting on in the field of aesthetics and even moral philosophy. The Opera House was funded by a lottery, and most of the people who bought tickets don’t go to the opera. It’s an absolute paradigm case of highbrow culture funded by taxes on the working class. But it’s very popular! The question here suggests that doing something which is basically unfair can, in principle, be justified retrospectively by artistic merit. Or does it? You tell me.

We are heading into the closing chapters of the book now, where I start talking about solutions and ways to tame the problem factory. There is a lot that can be done, I think, although first I need to talk about some ideas that won’t work or might be counterproductive. We’ve demonstrated our respect for the problem, at length, and we can now stop touching gloves with our adversary and start repeatedly punching it. But before we go into this, I need you to do a bit of a gut check.

In your heart of hearts, deep down, do you think it was a good idea to build the Sydney Opera House?

Looking through old textbooks on planning problems, the Opera House often shows up as an example of a “Great Planning Disaster”. In the book of that name, the theorist Peter Hall devotes a chapter to it, detailing how its costs ended up overrunning by more than ten times. The architect, Jørn Utzon, had never designed anything bigger than a medium-sized housing project. Ove Arup, the engineer responsible for realising his vision, described the cost forecast as having been “prepared, I believe, by some unfortunate Quantity Surveyors under duress in a few hours”.

This is something we might have to sit with, potentially a bit uncomfortably; whatever solution we come up with to all the social and organisational pathologies that stop things from being built, it is unlikely to be something which regularly facilitates things like this. And everyone knew this at the time. The project was started without proper planning, leading to the demolition of millions of dollars worth of wasted work, because as Arup put it “if a fait accompli were not established while Mr Cahill was Premier, the job would probably not go forward at all”.

But a lot of people, including most Sydneysiders, think now, and thought at the time, that it was worth it. There was even substantial support for decades afterward for continuing to finance the huge overrun and the underestimated running costs out of the proceeds of a lottery, meaning that they would largely be paid for by people who didn’t watch much opera.

Conversely, though, when I ask people the same heart-of-hearts question about whether the Concorde project should have gone ahead, they’re much more reluctant to say yes, even though it’s exactly the same sort of white elephant prestige project. And almost nobody will defend NASA’s decision to change their risk tolerance rule from “cancel a launch if there is any doubt about safety” to “proceed with a launch unless there is evidence it is unsafe” ahead of the 1986 Space Shuttle disaster.

I have consciously tried to avoid using the language of “trade-offs” so far in this book. There’s a number of reasons for this. First, it’s not an accurate way to describe most of the problems. Tradeoffs exist when you are at the efficient frontier, when it’s really not possible to get more of one desired thing without giving up another. We are nowhere near that frontier in most of our decision making; we can usually have more of everything we want, simply by spending less on doing stupid things which slow us down and annoy everyone. Removing resources from the problem factory and giving them to the solution factory isn’t a tradeoff, it’s just a gain.

Secondly, I find that the language of tradeoffs is often used in a rather bullying way. If you listen to people who are objecting to something, it’s rare that they don’t understand that there are tradeoffs in policy. They just don’t think it’s worth it. Or they think that the costs are falling disproportionately on them for benefits that go somewhere else. People think that they are sounding wise when they say that “the public want nice things but don’t want to pay for them”. But that’s just what the words “nice things” and “paying” mean. Everyone wants nice things, and nobody wants to pay, they used to teach you this when you did an economics degree.

Here is a real tradeoff, though. We are studying decision making, under conditions of uncertainty. That means that mistakes are inevitable. We can push toward the efficient frontier, to make as few mistakes as possible. But there comes a point at which it is too costly to reduce the error rate any more. Given the context is often that of vitally necessary investments, we cannot even help ourselves to the proverbial wisdom that the only way to avoid mistakes is to do nothing at all, because doing nothing may itself be a big mistake.

So there is going to be a decision to make; will our errors be those of commission, or of omission? It’s not an easy question, and I certainly can’t answer it on behalf of anyone else. I just want to warn you that even if you think you have come up with a solid set of principles to say “yes” to the Sydney Opera House, “no” to Concorde and “definitely not” to the Space Shuttle disaster, circumstances may find a way to make you look a fool, or worse.

This has been weighing on my mind quite savagely since I wrote it last week, because I think (by the way, thanks for excellent comments) it might be the key to unlocking the things I’m thinking about:

The builders and blockers are a slightly different issue, because they’re intrinsically political rather than technical. They either need to be treated as problems to be solved with the same approach as other bottlenecks, or taken seriously as political stakeholders. And asking that question is the same thing as asking “has the decision to build this thing actually been taken yet?” In my view, a lot of the problems on the Abundance Agenda might have a negative answer to this as their root.

So, let’s call this the question of “the point of decision finality” – the moment when the answer to this question changes from no to yes. The thing that’s troubling me is – does this entity, the point of decision finality, does it exist? Is there any such thing? If there is, then it’s clearly an important question of institutional design to say where in the workflow the point should be placed.

But so far I’ve just raised philosophical question after philosophical conundrum for myself – so I hope the philosophers among my readers might be able to help. Clearly, there is a sense in which there is no finality to a decision to build something until the ribbon is cut and the doors opened; projects can be abandoned and often are. Does this mean that there is no such thing as a final decision?

I think the concept can be rescued. Let’s say that the “point of decision finality” is the moment before which not building the thing is correctly described as “we decided not to”, and after which not building the thing is correctly described as “we failed to build the thing”. Now the question is – will this definition support my intuition that the location in the workflow of the point of decision finality is both important and something which is itself part of the design of the system?

My answer would be “not without a bit more substance to it”. Simply changing what you call a project from “failed” to “decided not to” isn’t something that makes a difference in the real world, and it doesn’t meet the challenge. So, what does make that difference; what changes in the world and the organisation when a decision crosses the point of finality.

The answer has to be one of information architecture, because all organisational questions are. After the point of decision finality, there is a qualitative change in the kind of information which the system is allowed to pay attention to. Before some project P reaches decision finality, for example, people are allowed to suggest plans under which the organisation tries to achieve its higher level goals through a set of projects which doesn’t include P. After that point, P has been decided on; the set of alternative plans to be considered is shrunk.

That means that the analytical problem with respect to P is “how can this be achieved?” rather than “should we do this?”. There’s another conundrum here for me, though, because I am not sure that these two questions are sufficiently different – as long as the first question could potentially be answered “it can’t” (or “it can’t, except at excessive cost”), then the second is open. As I said earlier, nothing’s really “decided” until it’s finished.

Which means that there is a constraint on the placing of the point of decision finality. In order for the decision to acceptably approach the ideal of finality, we have to say that to declare a decision final is to assert that you are confident that it can be achieved within the resources allocated to it.

So there’s a limit on how early in the workflow the point of finality can be. I am going to assert that subject to this constraint, the point of decision should come as early in the workflow as possible; keeping decisions open when they ought to be closed seems to create a lot of opportunities for people to engage in strategic behaviour. (It also seems to give advisory bodies like the Planning Inspectorate delusions of being judges; when people feel like a decision is still open, they also seem to believe it’s incumbent on them to maintain the appearance of an open mind).

And that suggests another point of decision architecture. You can’t achieve decision finality until you are reasonably certain that a project can be achieved in an acceptable budget envelope (with “budget envelope” to be taken here as meaning the budget in terms of environmental cost and political goodwill as well as the financial). But of course, you can’t be anywhere near certain of that until you know what the budget actually is. So this suggests two stages of analysis; an initial broad-brush exercise establishing the order of magnitude of the benefits and costs and bringing the tradeoffs into the open, then the decision point, then a detailed planning exercise to see how much surplus can be created, in terms of doing better than the tradeoff which was accepted. Of course, if the second number is negative, then that’s a failure, but I think the idea of “the project has failed” now has more definite organisational and economic meaning.

Sorry, that was a bit dense wasn’t it, readers? I’ll try to come up with a joke by Friday.

I’m sorry for the non-appearance of the Wednesday post; I am so busy that I literally forgot it was Wednesday. Manuscript deadline for “The Problem Factory” is at the end of this month, so naturally I decided the best thing to do was take on a bunch of other commitments. I can’t promise immediate improvement but I will do my best.

Anyway, I was at a seminar this week and, as is my habit, made a silly joke which I now take seriously and think is my actual view. We were addressing the question of “Building Things”, and I proposed the following thought experiment:

What is the largest piece of public sector infrastructure that you could build, right now, if you were restricted to using no more than two contractors, getting permission from no more than two planning bodies and employing no more than two professional services firms?

The idea was to try and get a physical scale and scope for something we were certain we could achieve given the administrative and decision making resources at our disposal. My guess is that the answer might be “something really embarrassingly small, like possibly a bus shelter”. But, infrastructure projects like this have one important advantage over more serious and necessary projects, which is that we can actually do them. There’s only fifteen possible communication channels between six nodes (twenty-one if we imagine there’s a commissioning body that needs to talk to all of them), which ought to be possible for one person to keep track of with an email outbox and a telephone.

So, if we want to get building, let’s not start by being blinded with foolish pride. Declare open season for bus shelters and similar small scale projects. But while we’re building, we need to make sure we’re learning. So let’s also commission a few projects which are a bit bigger than the scale that we know we can manage – we need another thought experiment along the lines of “what’s the smallest thing that you think you couldn’t build if you were restricted to two contractors, two permissioning authorities and two professional services firms?”

Some of those projects will succeed and some will fail, because we’re not going to be able to precisely estimate our own capacity. But we can learn from the ones that fail – this will give us lessons about where the bottlenecks are. And we can learn even more from the ones which don’t exactly fail, but which take much longer than they ought to.

Now we have a, hopefully manageable, “snag list”. This will consist of communication bottlenecks, perceived legal risks and regulatory problems. It will also include some bad faith actors and “blockers” who are opposed to the aim. The first category of problems are the suitable raw material for Malcolm Sparrow’s “problem-oriented governance approach” – you recruit a problem team from all the areas the problem touches, agree on a definition, brainstorm and experiment solutions, then implement and communicate.

The builders and blockers are a slightly different issue, because they’re intrinsically political rather than technical. They either need to be treated as problems to be solved with the same approach as other bottlenecks, or taken seriously as political stakeholders. And asking that question is the same thing as asking “has the decision to build this thing actually been taken yet?” In my view, a lot of the problems on the Abundance Agenda might have a negative answer to this as their root.

And then you iterate; if the problem teams have concluded, communicated and wound up successfully, then the largest object that you can build within your administrative capacity is now a bit bigger. So, build a bunch of those, and then also commission a few more “learning” projects which feel like they’re a bit bigger than you can handle. You’ll be putting high speed trams across the Pennines before you know it.

Readers might notice that I haven’t defined “you” above, which was intentional, because I think this sort of approach can work at different levels from local authorities to departments to central government. The important thing, I think is to start with a realistic and honest assessment of current capacity. As Sun Tsu said, know the enemy and know yourself, and you need not fear the result of a hundred battles.

NEW BOOK ALERT: Long term readers will recall that I recommended Gill Kernick’s “Catastophe and Systemic Change: Learning from the Grenfell Tower Fire and other disasters”” a few years ago, saying that it was “an extremely good and important book which deals with questions of accountability, complex systems and disastrous failure in exactly the sensitive and perceptive way that I was scared I wouldn’t”.

There is now a second edition available for pre-order, which is greatly revised from the first. The conclusion of the Grenfell Inquiry has made a lot of new information available, but more importantly, has made it much less legally dangerous to be specific about companies and building products which contributed to the disaster. I was lucky enough to blag a pre-publication copy and think anyone who is interested in the general subject matter of “Why don’t we learn?” and “How can we change?” will really enjoy the new edition. If you’re engaged with YIMBY adjacent topics in construction regulation, I would say it’s absolutely required reading.

Anyway, on with the show. I will try not to let the length of this one get out of control, because it’s one of my favourite subjects – the potential for AI to replace middle managers. I am going to do people like Matthew Prince and Jack Dorsey the courtesy of taking them seriously and assuming that they mean “actually replacing middle managers” here, rather than something anodyne like “increasing the productivity of middle managers so that each manager can supervise a larger unit”. Because if all they meant was “I have changed the capital/labour ratio in a company I run” then come on, that’s a bit of a waste of our attention.

Here are a few statements I regard as axiomatic:

1. Actually running a business is more difficult than buying and selling shares on a liquid market.

2. Picking stocks to outperform the market is something which a neural network can do, we know this because human brains do it.

3. However, most human beings, including most human beings who are paid to do so, can’t.

To me, this implies, straight off the bat, that trying to beat the equity market is an at-least AGI level task, and that trying to manage an organisation is potentially super-AGI level. (I’ve made a similar argument in the past, suggesting that a computer which was really capable of handling planning applications would have to be so smart that we would long since have handed over the whole of government to it). This seems to me at least to be either a refutation of the idea that “AI can replace 20% of my managers”, or a massive self-own on the part of people implicitly admitting that their business is so simple, uncompetitive and overstaffed that current generations of Claude can handle it.

[MASSIVE RABBIT HOLE WHICH I WILL POINT TO BUT NOT GO DOWN. Of course, part of the problem is that stock-picking is an adversarial process in which people compete with one another; doubling everyone’s cognitive capacity in the market would just make it more efficient without changing the proportion of people capable of generating outperformance. But so is managing a business, in most sectors.]

Anyway, I have about 500 words left, so I will sketch out why I think that LLMs, specifically, will have a hard time even solving the easier problem of stock picking. I start from the observation that there are a lot of very successful computer systems which beat the stock market using machine learning; they just don’t look very much like large language models.

In fact, quant systems get their edge over a market dominated by human beings precisely because they don’t reproduce human reasoning; they have rules and stick to them. There are two ways to underperform the stock market – by hanging on stubbornly to losers, or by getting the collywobbles and exiting from a good trade too quickly. (The proverb is that “amateurs go broke by taking big losses, professionals go broke by taking small profits”).

So imagine that we have an LLM with appropriate training and a system prompt that says “you are a really good stock picker. Pick stocks that will go up, hold on to them and when they are about to go down, sell them. Don’t make mistakes.” And it’s picked a good stock, that’s gone up a lot, but has now started to go down a bit. Will it get the collywobbles?

Yes! Because “getting the collywobbles in a good trade” is exactly the same activity as “not hanging on to a loser”, except that you’re wrong. Symmetrically, “hanging on stubbornly to a loser” is the same thing as “not getting shaken out of a good trade”, except you’re wrong. Stock market proverbs are massively contradictory; let your winners ride, but pigs get slaughtered. Cut your losses, but in the long term it’s a weighing machine. The trend is your friend, but be contrarian. Etc etc.

The trick is to understand what situation you’re in, and to apply the right one of those rules. And, of course, coping with a high-dimensional environment in which different rules are applicable is one thing that neural nets can handle very well.

It’s just that the neural nets which handle it well are the ones which have been specifically trained to do so, like the actual quant models which people actually use. A neural net which has been pre-trained on a massive corpus of human language by a transformer network is just going to get the collywobbles and double down like a human analyst, because that’s what happens in its training data.

The only way to stop it from doing so, or to bring it up to the standard of the best human analysts, would be to create a great big tagged dataset of good and bad trades along with the reasoning that led to them, so as to get better pre-training weights. And then you’d take on the huge additional task of updating the tagged dataset, so that it didn’t go stale as the market and the economy changed. (Stale knowledge and old habits are a huge problem for human stockpickers). But why would you do that, rather than just directly training the neural net to pick stocks?

And this matters, because when you shift focus back from stockpicking to general management, there’s no equivalent of directly training the model on the data. And it’s not even clear that “the data” has a stable referent – management is very much a game of “horses for courses”, in which the curse of dimensionality is very great indeed.

So you’re left with a model that has to be trained on the corpus of human decisions, and which will therefore be likely to reproduce human mistakes. Unless you can hire an army of offshore workers to tag management decisions as “good” and “bad”, I suppose. But that sounds expensive.

I’m not exactly in summer re-runs, but the effects of the “heat dome” over Europe have me thinking about the passage at the end of a book review I wrote for Hypertext last year, where I tried to invent the concept of “degrowth Abundance”.

“…concerned not with abundance at all but rather its opposite, bankruptcy. Greece’s Odious Debt, by Jason Manolopoulos, was a polemic published in May 2011, in the very earliest days of the Greek debt crisis. While all three books discussed here so far are attempting to address the question of how we can stop our once-productive social and political institutions from becoming pathological, Manolopolous was forced to ask the same question shortly after the bill became due.

Greece’s Odious Debt contains a thoroughly unsentimental but nonetheless moving history of how the Hellenic Republic got to where it was, and importantly how the combination of financialization, political myopia and the economics of European Monetary Union caused it to spend a decade in a world of false abundance. For a few years, Greek citizens lived well beyond their means, importing Mercedes cars and enjoying a real estate boom. When the crisis happened, they were brought face to face, as few peacetime societies ever are, with two facts: Their system of governance was rotten, and their prosperity was fake.

The economy of Greece has still not recovered; GDP is about 20 percent below its 2008 peak. And its politics have by no means been transformed. So there is no solution here, but there is a lesson. Environmental debts will come due, just like financial ones. So will political and social problems that have been put off to the future because they didn’t appear to have engineering or lawyerly solutions.”

I still think this might be a serious challenge to the Abundance agenda; that achieving the goal of growth and human flourishing might occasionally involve taking a step back, and that we might be at one of those points right now. It doesn’t mean that the whole project is discredited - far from it, it implies that solving the problems which make it too difficult to construct renewable energy infrastructure is really very urgent.

But it does mean that we can’t brush off “degrowth” as easily as we might want to. Carbon dioxide emissions are very like debt; they provide a current benefit in exchange for a future liability, and the future liability might be significantly bigger than the initial benefit. It’s just that it’s a form of future obligation which doesn’t appear in the same balance sheet as financial debt.

I don’t think this is a difficult accounting problem; it’s tricky to quantify the environmental debt and to translate it into a precise number of pounds or dollars. But it’s surely not difficult to understand that it exists, or that it’s likely to be big - we’re already thinking about abandoning entire towns, for heaven’s sake. And yet, people say that Net Zero is unaffordable, who would never say the same thing about the 4.75% October 2035 Gilt.

When a country has an excessive debt burden of either kind, bad things are likely to happen, and the only solution is either a bailout or a period of austerity. I had, and have, many disagreements with Yanis Varoufakis and with Wolfgang Schaeuble, but they did, at least, start from recognising the facts. Who is going to tell us that our prosperity is fake?

And more worryingly, what is going to happen to them when they do? One of the key motivating factors behind the Abundance Agenda is that the politics of scarcity are incredibly bad right now. The main reason why we need policies capable of delivering economic growth is that without it, a lot of voters seem to develop a really unattractive and dangerous mean streak. I still think it’s possible to design some glide path which will maintain developed world consumption levels while financing investment and writing off a large part of the last twenty years’ malinvestment. Just don’t ask me to write it down right now.