The dream of predicting the future has always haunted the human mind. From the oracles of Antiquity to contemporary algorithms, this quest spans the centuries, taking constantly renewed forms. Now at Oxford University, physicist and economist J. Doyne Farmer is proposing a revolutionary approach to prediction and explaining it in his latest book, Making Sense of Chaos: A Better Economics for a Better World.
The latter is rooted in the computing power of modern systems and chaos theory, which explores how deterministic systems, subject to precise rules, can generate unpredictable and complex behaviors due to extreme sensitivity to initial conditions. Farmer’s atypical career illustrates a deep conviction: the apparent unpredictability of certain phenomena. often hides regularities that science can decipher. Paul Rand gave him the floor for the 147th episode of the American podcast Big Brain ; this is what came out of it.
From chance to physical laws: the birth of a new method
Farmer’s first steps in the world of prediction began in front of a casino roulette in the 1970s in Las Vegas. Where ordinary mortals only see a game of chance, the physicist detects a mechanical system governed by Newton’s laws. “ Wait a minute, it’s just a marble rolling on a circular track with a rotor in the center, which spins in the opposite direction. These are simple physical systems, so as physicists we should be able to predict their behavior “, explains Farmer.
By applying these fundamental laws and precisely measuring the physical forces at play, his team develops a mathematical model capable of predicting the trajectory of the ball. To exploit this discovery, they created the first concealable laptop – a remarkable innovation for its time. The device, hidden under their clothing, receives data via toe-operated switches.
When the ball passes a reference point, the operator sends a signal. The computer then calculates the speed of the ball and predicts its probable arrival zone. A second player can place his bets accordingly. This scientific method allowed them to gain a huge statistical advantage over the casino. “ We beat the casino by a good margin, around 20% margin”specifies Farmer.
This experience, beyond its anecdotal aspect, lays the foundations for a deeper reflection on the nature of chance and predictability. It demonstrates that a seemingly random phenomenon can become predictable when we understand the physical mechanisms that govern it and have the appropriate mathematical tools to model them. A lesson that Farmer would later successfully apply to a completely different sector.
Complexity as a key to understanding financial markets
Building on this success, Farmer transposed his methods to the financial markets. In the 1990s he founded a company, called Prediction Company, which was the equivalent of a shock in the world of finance. It will be one of the first companies to use computers to carry out stock trades fully automaticallyfollowing mathematical and statistical models.
Unlike casinos where winnings quickly attract the hostile attention of establishments, the financial markets offer Farmer an ideal experimental ground. “ As soon as we started winning well, the casino put pressure on us, so we slipped away. We profited well, but we can’t say we made a fortune » emphasizes Farmer.
In partnership with UBS, the company develops sophisticated trading strategies based on the analysis of complex systems. The results exceed all expectations: over 28 years of activity, the company makes profits for 27 years. Even more impressive, the return/risk ratio of their investments is six times higher than that of the market. A success that owes nothing to chance. Where classical financial theories postulate the efficiency of markets and the perfect rationality of investors, Farmer adopts a radically different approach.
His team then models markets as complex systems, comparable to the natural phenomena he studied as a physicist. Price fluctuations are no longer seen as a series of random numbers, but as the result of multiple interactions between actors with imperfect behavior. This approach makes it possible to capture subtle dynamics: cascading effects, bubble formations, panic movements. By integrating the cognitive biases of investors and their real strategies – often far from pure rationality – their models manage to anticipate market movements invisible to traditional approaches.
The silent revolution of “complexity economics”
After these successes, Farmer is now tackling an even more ambitious challenge: model the economy as a whole. Its new company, Macrocosm, is developing an economic navigation tool to visualize the complex interdependencies between actors and to simulate the impact of different decisions. Farmer compares it to a well-known tool, Google Maps: “ My ultimate dream is to revolutionize economic decision-making like Google Maps transformed traffic management “.
Named “ complexity economics “, this approach radically breaks with traditional economic models. Instead of relying on mathematical equations assuming perfectly rational agents, Farmer’s strategy simulates millions of economic actors making imperfect decisionsbased on simple rules of thumb. A business manager does not calculate an optimal strategy, he imitates his successful competitors. A consumer does not maximize an abstract utility function, he follows habits and heuristics, etc.
The effectiveness of this method was particularly evident during the COVID-19 crisis. Farmer’s models predicted a fall in UK GDP of 21.5% in the second quarter of 2020 – the reality was 22.1%. Remarkable precision which gradually convinced major institutions. Several central banks, including those of Canada and Italy, now integrate these tools into their decision-making. The potential applications are vast: from anticipating financial crises to optimizing energy transition policies, including the study of inequalities. A new way of thinking about economics is emerging, closer to the complex reality it seeks to understand.
This news complexity science could therefore make our predictions more reliable in many areas. Not by predicting the future with absolute certainty – an ambition that will always remain illusory – but by identifying patterns and dynamics which structure complex systems.
- Doyne Farmer used the mathematics of chaos to make complex phenomena predictable, from casino roulette to financial markets.
- Its models, based on real human interactions and not perfect rationality, capture often invisible dynamics.
- Today, he now aims to completely transform economic decision-making with his company Macrocosm.
