In 2006, economists at the Federal Reserve Bank of New York started worrying about the overheating US housing market. Concerned that the bubble might burst, they used their best model to predict what would happen if house prices dropped by 20 per cent. Not much, was the answer it churned out. Soon after, house prices fell by almost exactly this amount, leading to probably the worst period of global economic decline in a century.

The crises that have shaken the world economy since 2008 have pushed people, especially policymakers and governments, to ask why economists have not been able for foresee them.  What is happening to economic science if it is not able to recognise an economic crisis before it “steps on it”?  How is it possible that the economic science has been caught off guard again and again?  Besides, what is the implication for the status of economics as a science if it is not able to successfully deal with real economic problems?

In a work in 1992 French physicist and economist Maurice Allais, also a Nobel Prize winner, said: “…the essential condition of any science is the existence of regularities which can be analysed and forecast. This is the case of celestial mechanics but it is true for many economic phenomena whose analysis displays the existence of regularities which are similar to those found in the physical sciences. This consideration is the basis of why economics is a science, and why this science can rest on the same general principles and methods of classical thermodynamics and in general as Physics.”

Economists had long tried to build economic models that apply the mathematical laws of hard sciences, in particular, physics. The aim was to create a real mathematical economics on those models.  Some important exponents of neoclassical economics explicitly declared their intentions of transferring to economics the concepts and the methods used in physics.  We now know what the result has been – millions of jobs disappeared, lives were ruined, and right-wing politicians exploited the crises to obtain power in many countries.

The economics profession had spent too much time on what it called a “general equilibrium model”, based on analytically tractable assumptions and observations which led them to make conclusions for a few limited economic phenomena. Economics was largely a matter of formalised thin fiction that had little to do with the wonderful richness of the facts of the real world. The assumptions made in various models were frequently made for the convenience of mathematical manipulation, not for reasons of similarity to concrete reality. 

Detractors often lambast economics for being a pseudoscience, with dense mathematical formulae that belie its subjectivity and a poor track record of making accurate predictions. Eventually, economists themselves started wondering about the approach used to analyse economic phenomena. After a lot of soul-searching, almost everybody agreed that traditional economic theory had considerable weaknesses.  But agreement on what improvements in terms of description and foresight could be obtained by different approaches, was rather elusive.

Along came chaos theory.  This new approach has attracted particular attention because of its ability to produce sequences whose characteristics resemble the fluctuations observed in the marketplace. Most economic variables, whether at the micro-level (such as prices and quantities) or at the macro-level (such as consumption, investment and employment) oscillate.

Moreover, the traditional models concentrated on regularities, not on differences.  But culture, psychology, class, group dynamics, or other variables manifestly show the heterogeneity of human behaviour.  Models built on regularities cannot easily handle significant real-world differences, nor can linear models that assume a theoretical state of equilibrium provide solutions for economies that are rarely in equilibrium.  

Enter chaos theory.  In a book titled Making Sense of Chaos: A Better Economics for a Better World, J. Doyne Farmer unravels why standard economic approaches often fail and presents a radical alternative:  complexity economics.   In this take, economies are treated as systems akin to natural ecosystems or Earth’s climate. Giant computer simulations based on these ideas offer a better representation of how billions of people interact within the global economy.

Farmer currently holds posts at the University of Oxford and the Santa Fe Institute in New Mexico, but his entry into the economics profession has been unconventional. It began when he dropped out of graduate school, built the world’s first wearable computer and used it to beat the casino at roulette. In the 1990s, he set up Prediction Company, where he applied similar principles to the stock market.

Chaos theory wasn’t invented by Farmer, but he is trying to take it into the mainstream.  He writes that in mainstream models, the economy always tends toward equilibrium, like a rocking horse that rocks when it’s whacked with a stick but eventually settles down (to use the analogy of the Swedish economist Knut Wicksell, who died in 1926).  In fact, Farmer argues, a lot of the economy’s ups and downs come from internal forces, not external ones. As a result, the economy never settles down into a quiet equilibrium. It behaves chaotically.

To Farmer chaos is normal.  What distinguishes chaos is its sensitive dependence on initial conditions, such that a tiny change in how things start off can lead to dramatic differences in what happens later.  It also has endogenous motion   ̶   that is the system never settles because it generates its own fluctuations. However, chaos is not synonymous with unpredictability:   Farmer adds that, “On one hand, chaos imposes fundamental limits to long-term prediction; on the other, it means that data that otherwise look random can be predictable in the short term.”

Chaos theory stimulates the search for a mechanism that generates the observed movements in real economic data and that minimises the role of exogenous shocks. In this sense it could represent a shift in thinking about methods to study economic activity and in the explanation of economic phenomena such as fluctuations, instability, crisis, and depressions.

Economic forecasters who ignore chaos theory err by working from the top down, trying to predict where the complex system is headed by looking at aggregates such as employment and price levels, Farmer argues. He uses a different approach, which is to build a model of the economy from the bottom up out of “agents” representing individual consumers and businesses. Then let those agents interact and see what behaviour emerges. It may be something you never could have guessed.

Farmer argues that weather forecasters do it right. They gather data from millions of probes measuring the local temperature, humidity and wind speed and feed it into a model that’s a virtual representation of the globe’s atmosphere. Weather forecasts have gotten much more accurate, while economic forecasts have not.

Initial opposition to the chaos theory was that chaotic motion in systems is neither predictable nor controllable because of the sensitive dependence on initial conditions. Small disturbances lead only to other chaotic motions and not to any stable and predictable alternative.

But scientists Edward Ott, Celso Grebogi, and James A. Yorke, who wrote Controlling Chaos, have proposed an ingenious and versatile method for controlling chaos. The key achievement of their paper was to show that control of a chaotic system can be made by a “tiny” correction of its parameters. This observation opened possibilities for changing the behaviour of natural systems without interfering with their inherent properties.

To explain the huge difference between traditional economic models and chaos models, consider that, if a system is non-chaotic, the effect of an input (say, more investment) on the output (GDP) is proportional to the latter. Vice versa when the system is chaotic, the relations between input and output are made exponential by the sensitivity to initial conditions.  This sensitivity is complicated by the fact that there are also chaotic external shocks (e.g. wars, upsets in supply logistics, weather events, disruptive technologies, pandemic) which disturb the economy.

Economists can obtain a relatively large improvement in the system performance of chaos models by using small controls originally used in engineering models.  I won’t go into the nitty-gritty of it since it is quite esoteric.  But it concerns the ability of governments to manipulate some policy parameters in order to shift the economic system from a position of chaos to a fixed-point outcome and in this way fulfil its economic goals   ̶   exiting a high inflation situation to a low and stable rate; incentivising female labour participation rates through incremental changes in the In-Work Benefit; or reducing dependence on foreign labour without breaking the economy. 

By the way, chaos theory is used in many other sectors. For example, Dr Jacqueline Żammit of the Department of Languages and Humanities in Education at the University of Malta, has used it to analyse the subject of Maltese as a second language.  The findings of her research where that a second language is a “complex, dynamic, chaotic, unpredictable, adaptive, open, self-organising and non-linear procedure.”  The research outlined various cognitive and sociocultural provocations that impact the accomplishment, proficiency and achievement of learners.  Chaos theory helped her identify various factors that could help students achieve  better learning outcomes.

The aim of the process is to bring order out of chaos. It is rather akin to the events described in Genesis.  In the beginning, there was a formless void, darkness, and a deep water abyss. Then God said “let there be light” and out of the cauldron of creation, He brought definition, order, light, form, clarity and a framework for our existence. 

The same thing happens in chaos models, where we seek to control the chaos around us and bring some order in economies.  The problem is that, unlike in Creation, we have several gods, not one   ̶   national governments, the EU, the IMF, the WTO, the FATF, the mighty American government and its buck, and other lesser gods but still as dangerous as all those mythological Greek gods.  So, before we rush to condemn the Maltese gods for not getting it right from the get-go, let us at least spare a thought for their labours.