Economics Has Failed on the Climate Crisis. This Complexity Scientist Has a Mind-Blowing Plan to Fix That
•February 12, 2026
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Why It Matters
Accurate, granular economic forecasts would let policymakers and investors pre‑empt financial crashes and design cost‑effective climate transitions, potentially saving trillions. The tool could finally align economic planning with the urgency of the climate crisis.
Key Takeaways
- •$100 m model could cut 1% of $10 tn losses.
- •Simulates 30,000 energy firms, 160,000 assets.
- •Uses simple rule‑based agents, not perfect rationality.
- •Addresses equilibrium assumption, generating endogenous cycles.
- •Aims to guide climate policy, saving trillions.
Pulse Analysis
The shortcomings of conventional macro‑models have become starkly visible after the 2008 financial crash and the accelerating climate emergency. Traditional frameworks rely on a handful of representative agents and assume perfect information and market equilibrium, which forces analysts to simplify reality to the point of uselessness. Doyne Farmer, a veteran of chaos theory and algorithmic trading, argues that the explosion in computing power and the availability of massive firm‑level datasets now make it feasible to simulate economies at unprecedented resolution.
Farmer’s approach builds an agent‑based model where each company follows simple decision rules—imitation, trial‑and‑error, or adaptive learning—rather than the omniscient rationality imposed by classic theory. By calibrating these rules on 25 years of energy‑sector data, the model can reproduce observed boom‑bust cycles without injecting external shocks. Early back‑tests on US real‑estate transactions and the UK’s Covid‑era policy response demonstrated that the system could surface policy‑relevant signals that traditional models missed, offering a more realistic picture of how shocks propagate through interconnected markets.
If scaled to the entire global economy, the simulator could become a decision‑support platform for governments, central banks, and corporations facing climate‑related risks. A modest 1% reduction in the $10 trillion losses from the last crisis would already justify the $100 million investment many times over, while more accurate climate‑policy forecasts could steer trillions toward clean‑energy pathways. The main challenges lie in data integration, computational scaling, and securing public‑private funding, but Farmer’s ten‑year horizon reflects both the urgency and the growing appetite for tools that can translate complex economic dynamics into actionable insight.
Economics has failed on the climate crisis. This complexity scientist has a mind-blowing plan to fix that
I t’s a mind‑blowing idea: an economic model of the world in which every company is individually represented, making realistic decisions that change as the economy changes. From this astonishing complexity would emerge forecasts of unprecedented clarity. These would be transformative: no more flying blind into global financial crashes, no more climate policies that fail to shift the dial.
This super simulator could be built for what Prof Doyne Farmer calls the bargain price of $100 m, thanks to advances in complexity science and computing power.
If you are thinking this sounds crazily far‑fetched, then you’re betting against a man who, with friends, beat the casino at roulette in the 1970s using the first wearable digital computer and beat Wall Street in the 1990s with an automated rapid‑trading computer company that was later sold to the bank UBS.
Farmer, now at Oxford University, is a softly spoken polymath, whose academic adventures have taken him from cosmology to chaos theory to theoretical biology. Now, his 50‑year career has brought him to his biggest challenge yet:
“We want to do for economic planning what Google maps did for traffic planning, so we can give anybody who has an economic question an intelligent and useful answer.”
Traditional economic models are either too simple to give useful forecasts or too complex for even today’s computers to handle. Complexity economics offers a path through, says Farmer, and wouldn’t even need to be that much better to be a good investment.
The global financial crash in 2008, sparked by a real‑estate collapse in the US, cost the world about $10 tn.
“If in 2006 the US central bank had the model we could build now, they would have said: ‘Wow, this is really going to be a disaster – we’ve got to act now and save the world a lot of pain’.”
If the $100 m model had given enough foresight to cut just 1 % of the losses, it would have paid back the investment 1,000 times over.
This isn’t just talk: Farmer and colleagues built a retrospective model of US real‑estate transactions based on a vast data trove that would have given crucial insights. More recently, they built a complex model of how the UK economy reacted to the Covid pandemic, which they say would have indicated the best compromise between protecting health and protecting the economy.
Farmer’s book Making Sense of Chaos: A Better Economics for a Better World. Photograph: Penguin
But Farmer, who is 73, has now set his sights on the climate crisis:
“In my old age, I want to do good things for the world and I think this is the biggest problem we’re facing, maybe along with political polarisation, which unfortunately is itself making [the climate crisis] even harder to deal with. The world is going to experience a lot of pain due to not coping with climate change.
“Secondly, it’s an area where the failure of economic models is seen most dramatically,” he says. “I think the models we have are completely inadequate and even misleading. For example, the track record for these models in saying what renewable energy was going to do is genuinely terrible. They consistently predicted that it would be very slow to roll out and the cost would come down very slowly.” In reality, costs have plunged and the rollout has been rapid.
Driven by this, Farmer’s team’s first step towards a complexity model of the entire world economy is tackling the energy sector. The model encompasses all 30,000 companies and their 160,000 oil rigs, power stations and other assets, based on a rich, 25‑year‑long dataset of how they have operated.
“We’re literally modelling the decision‑making of all the energy companies in the world,” he says, each represented by a separate digital agent in the model. “We can simulate the whole energy system of the world to see how much energy each company delivers and at what price.”
The model is still in development, but should be much better at laying out the best path to a green‑energy future than today’s economic models. That could be transformative – a data‑led study by Farmer and colleagues in 2022 found that a rapid transition to clean energy could save the world trillions of dollars.
‘A complicated beast’
The new complexity models solve two fundamental problems with mainstream economic models.
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Assumption of perfect rationality. Existing models assume agents know everything about the system and about every other agent. With only a handful of agents this is barely possible; with thousands or millions it becomes an impossible computing task. “So the models are necessarily kept simple, which means that you can’t model the real world very well, as the economy is a pretty complicated beast,” says Farmer.
The new models let agents make decisions based on simple rules—e.g., imitating the best, or trial‑and‑error. This better reflects reality, because people don’t make perfect decisions, as behavioural economics has long shown. The simplification hugely reduces the computing power required.
“Whereas the normal way of doing things is limited to five or at most 10 different agents, and that would be a lot, we can do millions of them,” says Farmer.
Distilling the simple rules the agents use from large amounts of real‑world data makes the models even more realistic and useful. There’s also the Lucas critique—people may change the way they decide as the world changes. Complexity science solves this by using machine learning to let agents evolve their strategies.
Farmer’s model encompasses all 30,000 energy companies and their assets. Photograph: Kang‑Chun Cheng/AFP/Getty Images
“There are already some studies showing this in really simple settings,” says Farmer. “We’re going to be able to do that in more complicated settings – that’s a frontier problem we’re working on right now.”
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Assumption of equilibrium. Mainstream models assume supply and demand are balanced, or that every agent is acting perfectly. The real world is far from equilibrium—otherwise there would be no crashes. To explain crashes, mainstream economists have to introduce external shocks (“kicking the rocking horse”). In contrast, complexity models generate cycles internally because agents continuously adapt their strategies, producing fads, booms and busts without any exogenous shock.
‘Deep academic rut’
So how did we end up with today’s ineffective economic models? Farmer points to several reasons.
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Historical lock‑in. “We got stuck in a very deep academic rut back in the 1960s,” he says, when the debate was won by those who treated agents as perfect rational actors. “So we’ve been doing it that way ever since. The academic establishment has let itself become too close‑minded and has been very resistant to different ways of doing things.”
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Computing limits. “Back then, computers were a billionth as powerful as they are now, and the [economic] data wasn’t there, so it was much harder to do things the way we’re doing it now,” he says.
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Disciplinary bias. “Economics was colonised by mathematicians, not by physicists. Physicists have a much more practical viewpoint. Mathematician economists like to prove theorems and have highfalutin models. Physicists go, ‘Oh, this isn’t right, let’s just roll up our sleeves and simulate the world.’”
The target of developing the complexity model of the global economy has become an urgent task for Farmer:
“I really want to realise this goal within 10 years – hopefully we can even get there in five years. I’m old enough that I feel a certain urgency. I’d like to see it happen before I die or I go senile.”
He believes it would be a revolutionary tool, enabling politicians and business leaders to forecast the impact of their decisions far more confidently, grabbing opportunities and avoiding pitfalls. In the case of the climate crisis, that could accelerate the world down a cheaper path to ending emissions.
“It’s a very exciting endeavour – stay tuned,” he says. “Of course, if somebody wants to give us millions of dollars to help build these models, we’ve got our hat out.”
Doyne Farmer’s book, Making Sense of Chaos: A Better Economics for a Better World, was published in 2024.
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