Recently I blogged about getting my hands on a copy of Debunking Economics by Steve Keen. At that point I did not really know what to expect but it seemed to be a good read for challenging my conventional economic training in university. My undergraduate classes – both Macroeconomics and Microeconomics – have so far been dominated by the Neoclassical and New Keynesian school of thought. An exception to this monopoly on the undergraduate economics curriculum is probably Behavioural Economics, especially Game Theory, which I really enjoyed last semester! However, it is normally the concepts of rational expectations, utility maximising firms and individuals (constrained optimisation) and equilibrium that dominate the lectures. I am certain that it is not just me, but most undergraduates are tortured with abstract supply and demand analyses and comparative statics.
As Richard Thaler (2015, p.6) in his book Misbehaving: The Making of Behavioural Economics points out, the core premise of economic theory can simplistically be summarised as follows:
Optimization + Equilibrium = Economics
These are the very basics of economic theory and almost never challenged in the undergraduate curriculum. I am honest about this, so far I have followed the standard Economics curriculum sheepishly because you are hardly encouraged to question the core premises. However, my main takeaway from Keen’s book is really to not take Equilibrium and Optimising Behaviour for granted. These should not be core premises of economic theory, because – in practice – they just do not hold. We cannot overlook this and work on the premise that our assumptions of optimisation and equilibrium do not matter as revealed by Milton Friedman’s “paradoxical statement that ‘the more significant the theory, the more unrealistic the assumptions’” (Keen, 2011, p.159). This is because most of the assumptions we make in Economics are not neglibility assumptions but either domain assumptions or heuristic assumptions. In short, assumptions do matter and therefore modeling economic systems based on the flawed premises of optimisation and equilibrium (and a range of other unreasonable assumptions) must also be flawed.
Criticising conventional economic theory is one side of the coin, putting forward promising alternatives is the flipside. This is why Keen devotes his very last chapter of the 2011 Edition to the main alternative schools of thought. In particular, he gives a brief overview on Austrian economics, Post-Keynesian economics, Marxian economics, Sraffian economics, Complexity theory and Econophysics, and Evolutionary economics. However, it should be pointed out that all of them have their own weaknesses and I very much appreciate that Keen discusses both their strengths and weaknesses in the chapter.
According to Keen, one of the promising alternatives is Econophysics – the merger of Economics and Physics – due to its contribution to complexity in economics. The Econophysics approach is empirical, dynamic rather than static, and devoid of equilibrium conditions. This motivated me to devote today’s blog post to Econophysics, giving a short introduction to what Econophysics is about. I am also going to highlight the main areas of application at the moment as well as some interesting articles and books to get started with this multidisciplinary approach.
First, let’s look at what Physics and Economics have in common: They both make use of dense mathematics. Physicists even more so than Economists, because naturally their background is far more mathematical. And this is also where they depart and where Physics can greatly enhance the current state of the Economics profession. Physicists have the tools to investigate complex systems. Econophysics recognises that statistical physics concepts, such as stochastic dynamics, short- and long-range correlations, self-similarity and scaling, can be applied to to understand the global behaviour of economic systems (Mantegna and Stanley, 2000). For Economists this means that they can turn to empirical analysis methods without imposing a priori assumptions. Adopting theoretical tools of Physics allows Economists to model systems with interacting subsystems and this is exactly what we need in order to model the Macro-economy. Rather than building Macroeconomics from Microeconomics this merger of Economics and Physics allows Macroeconomists to model the Macro-economy as something that is more than the sum of its parts. It allows Macroeconomists to abandon representative agent models of the economy which in the past have failed to accurately describe the real economy anyway.
There are two approaches in Physics that Economics can greatly benefit from: complexity theory and chaos theory. While the former is the study of non-deterministic systems the latter are deterministic systems, which might seem a bit counterintuitive.
Chaotic systems are non-linear and dynamic. For example, when we take two variables which are influenced by each other they give constant feedback. Chaotic systems are sensitive to initial conditions, meaning that even a small change in the initial conditions leads to a completely different outcome in the long run and the so-called Butterfly effect (Jacobs, 2006). Therefore, in chaotic systems uncertainty arises because we cannot determine the chaotic system’s initial conditions (Fisher, 2012).
In comparison, complex systems are characterised by emergent behaviour. They are made of agents that interact with and adapt to another. Complex systems can be robust to small shocks at one point in time but fragile at another. Because complex systems are non-deterministic the outcome of the interaction of its agents is unpredictable. This gives rise to uncertainty, because even if we knew the initial conditions of the complex system we could not predict the future (Fisher, 2012).
What are the main areas of application? In the past Econophysics was centred on financial markets due to the availability of high frequency data thanks to electronic trading and financial markets being active 24 hours around the world. Financial markets create a vast amount of data which is needed for modeling. One of the most striking application is risk management which greatly benefits from Econophysics as a multidisciplinary approach making use of financial mathematics, probability theory, physics and economics (Mantegna and Stanley, 2000). However, the discipline is now moving on to explain more general economic phenomena. Starting out as what Keen calls ‘Finaphysics’ the discipline is now becoming more of ‘Econophysics’. One example is the Economic Complexity Index developed by Hidalgo and Hausman which shows “that countries tend to converge to the level of income dictated by the complexity of their productive structures” and which sees the emergence of complexity as a main factor for generating sustained growth and prosperity (Hidalgo and Hausmann, 2009, p. 10570). The Economic Complexity Index has proven to be more accurate in predicting income growth relative to the World Bank’s traditional governance measures. In particular, in the Atlas of Economic Complexity Hausman, Hidalgo et al. conclude that “the Economic Complexity Index captures significantly more growth-relevant information than the 6 World Governance Indicators” (2011, p.33).
Some interesting articles are listed on the website of the Economics: The Open-Access, Open-Assessment E-Journal. For example, in one of the papers Paul Ormerod applied random matrix theory to the analysis of macro-economic time series data. He examined “the evolution of the convergence of the business cycle between capitalist economies from the late 19th century to 2006” (Omerod, 2008, p.1). With the help of random matrix theory Ormerod distinguished true information from noise and showed that there is now a strong level of synchronisation of business cycles which makes it possible to speak of an international business cycle. In another paper Chen, Chang and Wen (2014) examined the effect of social networks on macroeconomic stability. They made use of an agent-based network-based DSGE and showed that both the non-linear and combined effects of network characteristics and the shape of the degree distribution are significant in determining the effect on economic stability. In another paper Challet, Solomon and Yaari deployed a three-parameter equation to model how GDP evolved during recessions and recoveries and argued that their equation is “the response function of the economy to isolated shocks” (2009, p.1) which therefore can help detecting shocks and has predictive power. The last interesting paper I want to point out was only published recently by Solferino and Solferino (2016). They applied the geometrical model of the Möbius strip to a Corporate Social Responsibility context to allow for complex interactions that characterise social and economic relationships today. As discussed before in the paragraph on complex and chaotic systems, this paper makes deliberate use of complexity and nonlinearity and acknowledges that feedback loops make systems interdependent and interacting with their environment and which, according to Solferino and Solferino, is also at the core of the models of Corporate Social Responsibility. For people interested in a comprehensive introduction to the field beyond the Econophysics papers published in journals, Mantegna and Stanley published the book An Introduction to Econophysics: Correlations and Complexity in Finance which might be worthwhile to read!
I hope you enjoyed today’s post. Thanks for reading!
Challet, D., Solomon, S., and Yaari, G. (2009). The Universal Shape of Economic Recession and Recovery after a Shock. Economics: The Open-Access, Open-Assessment E-Journal, 3(2009-36), 1-24. http://dx.doi.org/10.5018/economics-ejournal.ja.2009-36
Chen, S.H., Chang, C.L., and Wen, M.C. (2014). Social Networks and Macroeconomic Stability. Economics: The Open-Access, Open-Assessment E-Journal, 8(2014-16), 1-40. http://dx.doi.org/10.5018/economics-ejournal.ja.2014-16
Fisher, G. (2012, July 14). Chaos Versus Complexity. Retrieved from http://www.synthesisips.net/blog/chaos-versus-complexity/
Hausmann, R., Hidalgo, C.A., Bustos, S., Coscia, M., Chung, S., Jimenez, J., Simoes, A., and Yildirim, M.A. (2011). The Atlas of Economic Complexity: Mapping Paths to Prosperity. Retrieved from http://atlas.media.mit.edu/static/pdf/atlas/AtlasOfEconomicComplexity.pdf
Hidalgo, C.A., and Hausmann, R. (2009). The building blocks of economic complexity. PNAS, 106(26), 10570-10575.
Jacobs, J. (2006, May 7). Chaos theory, game theory and complexity theory. Retrieved from http://joannejacobs.net/cgi-bin/archives/000348.html
Keen, S. (2011). Debunking Economics: The Naked Emperor dethroned? London: Zed Books.
Omerod, P. (2008). Random Matrix Theory and Macro-Economic Time-Series: An Illustration Using the Evolution of Business Cycle Synchronisation, 1886–2006. Economics: The Open-Access, Open-Assessment E-Journal, 2(2008-26), 1-10. http://dx.doi.org/10.5018/economics-ejournal.ja.2008-26
Mantegna, R.N., and Stanley, H.E. (2000). An Introduction to Econophysics: Correlations and Complexity in Finance. Cambridge, UK: Cambridge University Press.
Solferino, N., and Solferino, V. (2016). The Corporate Social Responsibility Is just a Twist in a Möbius Strip. Economics Discussion Papers, No 2016-12, Kiel Institute for the World Economy. http://www.economics-ejournal.org/economics/discussionpapers/2016-12
Thaler, R. (2015). Misbehaving: The Making of Behavioural Economis. London, UK: Penguin Books.