*Originally published in 2023 and preserved as part of our archive project. E-International Relations is free to read. We rely on reader support to make this possible.

David Orrell is an applied mathematician and writer. He studied at the University of Alberta and obtained his D.Phil. in mathematics from Oxford University. He has worked in diverse areas from systems biology to quantum economics. His books on science and economics have been published in twenty countries. Orrell’s work points out to the limits of mathematical models, particularly economic ones, as argued in Apollo’s Arrow: The Science of Prediction and the Future of Everything and Economyths: Ten Ways Economics Gets It Wong. As a leading proponent of quantum finance and quantum economics and co-author of The Evolution of Money, Orrell proposes a quantum theory of money and value, which states that money has dualistic properties because it combines the properties of an owned and valued thing, with those of abstract number. As of today, he is a founding editor of the new journal Quantum Economics and Finance. He lives in Toronto.

Where do you see the most exciting research/debates happening in your field? 

In economics, the most exciting project in my view involves building an alternative to mainstream theory that is based on quantum rather than classical thinking. The field of economics has been in a rut for many years. Core ideas like the law of supply and demand date back to the Victorian era, and the failure of models during the financial crisis led to little real change. Newer approaches such as behavioural economics or systems dynamics are certainly useful but have had limited impact, in part because they don’t go far enough. For example, behavioural economics adjusts classical theory without really challenging the central idea of rational utility optimisation. 

Quantum economics is a completely new approach because it is based on a different kind of logic. In the classical picture, rational and independent investors drive prices to a stable equilibrium which reflects intrinsic value. In the quantum picture, prices are inherently uncertain, investors are influenced by subjective factors, people are financially and socially entangled, and as a result markets are unstable – and a lot more interesting.

The field is in an exciting stage because quantum models are promising to offer new answers to key problems in economics and finance. For example, one of the oldest such problems is the pricing of options (those financial instruments which give the holder the right to buy or sell an asset at a future date for a set price). The classical theory of option pricing, which is based on the Black-Scholes model from 1973, has been described as the most accurate theory in economics, but the quantum model shows that for commonly-traded options and standard parameters it can be out by 40 percent. The fact that it still serves as the benchmark model is like a magic trick (see also here), where people don’t focus on the obvious flaws because they are distracted by the elegant and persuasive theory.

Advances in the field are also being driven by the development of quantum computing. Mainstream economics was shaped by the development of computers in the post-war era, and quantum computers are changing the way we think about the economy. For example, you can model the financial entanglement between a debtor and creditor as a quantum circuit, and similar circuits are used in quantum cognition to model how we make decisions, or in quantum computers to run artificial intelligence algorithms. Today a high school student can run quantum circuits to model the prisoner’s dilemma game which is new.

How has the way you understand the world changed over time, and what (or who) prompted the most significant shifts in your thinking?

There have been many changes and influences along the way, but to focus on one, my experience doing my D.Phil. on model error in weather forecasting changed the way that I thought about science and made me much more skeptical about things like incentives and the role of mathematical models. At the time there was a belief that weather models could be treated as essentially perfect so all error came from the inputs to the model – i.e. measurement of the current weather – amplified by chaos (the butterfly effect). This meant that forecasters just needed to run many forecasts from perturbed initial conditions, which of course required more computers and bigger budgets. Having worked already on some engineering projects, my view – and that of my supervisor Lenny Smith – was that the main problem wasn’t chaos or the butterfly effect, it was just that models were wrong, because you can’t build a perfect model of the weather. (It’s amusing how people are skeptical about things like quantum approaches, but unskeptical about models once they become established.)

Economics is an example of a field where the dominant ideas only make sense when you think of the incentives involved. During the Cold War, the promotion of results such as the Arrow-Debreu model of competitive equilibrium, which purported to prove the optimality of free markets, was as much about propaganda as science. The efficient market hypothesis – which states that prices adjust to new information immediately – is the economics equivalent of the perfect model hypothesis in weather forecasting because it assumes the equilibrium model is perfect. And mainstream economics, with its emphasis on efficiency, rationality, stability, and optimality, often sounds like the PR wing of the........

© E-International