In April 1925, Winston Churchill, then Chancellor of the Exchequer, returned Britain to the gold standard at the pre-war parity of £1 to $4.86. The model behind the decision was explicit: restoring the pre-war rate would signal stability, anchor expectations, and re-establish London as the centre of global finance.
The problem was that Britain was not what it had been. The economy was weaker, export industries were less competitive, and the real value of sterling at the old parity was roughly 10-12% too high. John Maynard Keynes said so almost immediately, publishing The Economic Consequences of Mr Churchill that summer. The pressures he predicted — deflation, wage compression, acute stress in export industries — all materialised.
What followed was six years of the British government acting on the economy to make it fit the model. The parity had to hold, so wages had to fall. Exports were uncompetitive, so costs had to be squeezed. The coal industry could not compete at the prevailing exchange rate, so miners faced wage cuts and lockouts — precipitating the General Strike of 1926. Unemployment was chronic. Deflation ground on. At every stage, the response to mounting evidence that the parity was wrong was not to revise the model but to intensify the intervention: tight money, credit restriction, wage pressure, internal devaluation. The economy was being reshaped to fit the exchange rate, rather than the exchange rate being adjusted to fit the economy.
In September 1931, Britain abandoned the gold standard. The adjustment that the model had suppressed for six years arrived all at once.
What kind of problem is this?
When the response to contradictory evidence is not to revise the model but to intervene harder to preserve it, when the cost of that preservation escalates until it breaks discontinuously, that is a specific shape of problem. Behavioural finance would call it bias. But there is a more precise framework, one that explains not just that this happens but why.
The discipline that has formalised this is the science of self-organising systems — and the framework is Karl Friston’s free energy principle, originally developed in theoretical neuroscience but applicable to any system that maintains itself over time against disorder: institutions, strategies, and markets as much as organisms and brains.
So let’s borrow.
This is the multi-model move: recognize the shape of a problem, find the discipline that has thought rigorously about that shape, and import its frameworks deliberately rather than reinventing them from scratch.
The standard account would call Churchill’s Treasury irrational — anchored to a sunk cost, trapped by confirmation bias, unable to update. These labels are real but they are descriptions, not explanations. They name the pattern without explaining why the architecture produces it so reliably. Friston’s framework says something more unsettling: the resistance to updating is not a malfunction. It is what self-organising systems are designed to do.
Two Ways to Be Less Surprised
In 2006, Friston proposed that any self-organising system that persists over time does so by minimising something he called variational free energy, a quantity from statistical physics that bounds surprise. There are two ways to reduce it. You can change the world to match your model: that is action. Or you can change your model to match the world: that is perception. Both narrow the gap between prediction and reality. The decision-making framework that follows — active inference — asks how a system chooses between possible courses of action, which Friston calls policies. The expected free energy of a policy can be read two ways.
The first reading decomposes it as:
Minimising G means maximising both. Pragmatic value captures how likely a policy is to produce outcomes the system prefers: acting on the world to make it conform to the model. Epistemic value captures how much a policy will reduce uncertainty about what is going on: seeking information that updates the model to better fit the world.
The second reading decomposes the same quantity as:
Risk is the divergence between predicted outcomes and preferred outcomes: how far the consequences of a policy fall from what the system wants. Ambiguity is uncertainty about outcomes given the state of the world: how noisy the mapping from reality to observations will be.
These are two readings of the same quantity.
There is a separate source of resistance to updating. Variational free energy, the quantity the system minimises when revising beliefs about the current state, decomposes into accuracy minus complexity. Complexity is the divergence between updated beliefs and prior beliefs. Moving your beliefs far from where they started incurs a cost, even if the new beliefs are more accurate. A system will patch an existing model before it bears the cost of replacing it.
The balance between action and perception is governed by what Friston calls precision — the inverse variance that weights how strongly prediction errors update beliefs. High precision on prior preferences means the system trusts its model and acts on the world. High precision on incoming evidence means prediction errors get amplified and the system updates readily.
A thermostat is all pragmatic value and no epistemic value. It does not revise its model of what the temperature should be; it acts on the room to match the set point. Churchill’s Treasury was a thermostat — forcing the economy to conform to a pre-war exchange rate rather than revising the rate to fit the economy.
The Precision Trap
This is where the framework bites. Both channels of resistance compound.
The gold standard commitment was not just an economic policy. It was national prestige made concrete. Abandoning the parity meant outcomes the Treasury found intolerable: political humiliation, loss of credibility, an admission that six years of policy had been wrong. And the complexity cost of revising the model was enormous — the beliefs had been held publicly, at the highest levels, for years. Both channels locked the system in place.
Each year the parity held made it harder to abandon. Not because the evidence improved — it worsened steadily — but because the cost of admitting error grew with every budget, every speech, every policy justified by the model’s persistence. The Treasury was not ignoring the evidence. It was processing it through a system whose precision settings made action cheaper than revision. Holding, averaging down, reinterpreting. These are the low-cost moves. Not from irrationality. From architecture.
This is also why crowded trades persist beyond the point where the evidence has turned. In What Makes Stable Things Break, we described how consensus views function as attractors — states the system gravitates toward, reinforced by positioning and shared belief. The free energy principle offers a mechanism: the consensus is a shared model with very precise prior preferences. Each participant is individually minimising free energy, and for each the lowest-cost path is to preserve the existing model. The crowd does not coordinate its stubbornness. The stubbornness emerges from each individual’s precision settings.
When the Update Finally Comes
But models do break. As the gap between model and reality widens, the cost of defending the model through action keeps rising. The expected free energy of the defend-the-model policy climbs. Eventually it exceeds the expected free energy of revising the model. When it does, the update arrives suddenly.
The transition is not gradual because the model was not gradually wrong. In 1925, and 1926, and every year after, the contradictory evidence was arriving — Keynes had published it, the unemployment data confirmed it, the General Strike dramatised it — but it was being met with intervention, not revision. The cost of the defend-the-parity policy was rising the entire time. When the dam broke in September 1931, all the accumulated error arrived at once. Sterling lost a quarter of its value. Export competitiveness improved almost immediately.
The pattern echoes across decades. On Black Wednesday in 1992, the British government raised interest rates twice and spent billions defending sterling’s peg to the deutschmark within the European Exchange Rate Mechanism. The model said sterling belonged in the band. The fundamentals, distorted by German reunification, said otherwise. The response was the same as in 1925: act on the world, not the model. When the defence failed that evening, the suppressed prediction error arrived all at once. Soros was not betting that the fundamentals were wrong — that was widely known. He was betting that the government’s precision on the peg was unsustainable, and that when it broke, the repricing would be violent.
The yen carry unwind of August 2024, which we examined in What Makes Stable Things Break, had the same character. The model — “the differential persists, the yen stays weak” — had been accumulating contradictory evidence. But precision was high: it had worked for years, and the cost of updating was real. When the update arrived, it was not a gentle revision but a cascade.
This is not irrationality punctuated by sudden rationality. It is the natural dynamics of a system that minimises free energy through action until action is no longer sufficient. In that earlier post, we called this a bifurcation. The free energy principle offers a mechanism for why systems hover near that threshold: the agents inside are actively working to prevent the crossing.
The Prediction Error Question
The framework applies wherever a model has accumulated institutional weight. Strategy reviews that always conclude the strategy is sound. Risk committees that hear the warning but reinterpret the data. Organisations that restructure to protect a thesis rather than test it. These are prediction-error-minimising systems doing what they are designed to do.
The practical discipline is not “be less biased” — that advice is empty. It is in monitoring both channels. How precise are your preferences over outcomes, and is that precision earned or accumulated through commitment? How large is the complexity cost of revision, and is it distorting your weighting of the evidence?
The asymmetry matters. A small, recent, lightly held position has low precision and will update easily. A large, long-held, publicly defended position has high precision and will resist until the evidence is overwhelming — at which point the update will be violent. The longer the dam holds, the larger the flood.
After Britain left the gold standard in 1931, the economy recovered faster than almost anyone expected. Exports grew, unemployment fell, industrial production rose. The adjustment the model had suppressed for six years took months once allowed to operate. The prediction error had been there all along. The system had simply been working very hard not to feel it.
The discipline is in asking: is the model holding because the evidence supports it, or because the cost of updating it is too high?
References & Further Reading
A Free Energy Principle for the Brain: Karl Friston, James Kilner & Lee Harrison
Action and Behavior: A Free-Energy Formulation: Karl Friston, Jean Daunizeau, James Kilner & Stefan Kiebel
Active Inference and Epistemic Value: Karl Friston, Francesco Rigoli, Dimitri Ognibene, Christoph Mathys, Thomas Fitzgerald & Giovanni Pezzulo
Active Inference: A Process Theory: Karl Friston, Thomas FitzGerald, Francesco Rigoli, Philipp Schwartenbeck & Giovanni Pezzulo
Surfing Uncertainty: Prediction, Action, and the Embodied Mind: Andy Clark
The Economic Consequences of Mr Churchill: John Maynard Keynes
The Alchemy of Finance: George Soros