A new paradigm for understanding macroeconomic phenomena
The great discovery of the nineteenth century was that the equations of Nature are linear, and the great discovery of the twentieth century is that they are not. - Körner, T.W. (1988)
The concept of reductionism relies on the simple idea that any system can be broken down into its basic components, thereby allowing for a more ‘fundamental’ understanding of its whole. Over the past century, the reductionist framework has gained significant ground in the natural sciences with, to paraphrase the Nobel laureate in Physics, Stephen Weinberg ‘the explanatory arrows always pointing downwards’, from the macroscopic to the microscopic level, genes to DNA, thermodynamics to statistical mechanics etc. However, the great limitation of reductionism is that it fails to consider the emergent properties that can arise at each hierarchical level or, as P.W Anderson, another Nobel laureate in Physics put it: "At each stage, entirely new laws, concepts and generalizations are necessary, requiring inspiration and creativity to just as great a degree as in the previous one” (Anderson, 1972). This key characteristic is at core of “complex systems”, i.e. systems in which large collections of components interact in nonlinear waysand seems to characterize numerous phenomena in biology, physics, epidemiology or social sciences. As Körner (1988) mentions, to study such a system where the outputs are not directly proportional to its inputs, we need to go beyond traditional linear analysis where individual components can be treated independently from the whole system since the “whole is more than the sum of its part” to paraphrase Aristotle in his Metaphysics.
Of course, the reductionist mindset is hardly new to economics. In macroeconomics, the approach is at core of its research agenda on its microfoundations which arose in response to the now-famous Lucas critique. In a nutshell, this states that any macroeconomic model that does not allow individual agents to adjust their behaviour could not be used for policy since, when applying such models, said agents would simply alter their behaviour until the model no longer worked. Spearheaded by the Real-Business Cycle (RBC) model of (Kydland and Prescott, 1982), researchers combined the microfounded approach with Lucas’s ‘Rational Expectations Hypothesis’ to create the class of macromodels now referred to as DSGE (Dynamic Stochastic General Equilibrium).
This begs the question, just how reductionist (i.e. microfounded) can a model be if it writes off the notion of agent heterogeneity by assuming a representative agent who makes all production/consumption decisions in the model economy? The answer is, not a whole lot. Not only does it preclude the notion of emergent behaviour at the agent-level (since agent interactions are ignored), it assumes that agents possess a level of knowledge of the market (thereby allowing them to make optimal plans) that is inconsistent with real-world observations.
A truly reductionist approach would model agents individually and see what phenomena emerge endogenously at the macro level due to their interactions. This ‘bottoms-up’ approach is known as agent-based modelling and has recently been proposed as an alternative to mainstream DSGE models by Farmer and Foley (2009), who advocate that such models are inherently equipped to handle nonlinear behaviours due to their purely computational (as opposed to analytical, as in DSGE models) nature. The use of heuristics at the agent-level rather than optimization mechanisms as drivers of macroeconomic fluctuations is a central theme in De Grauwe (2012) who attributes ‘limited cognitive capabilities’ to agents in order to show how macroeconomic booms and busts can be attributed to self-fulfilling waves of agents optimism and pessimism.
Interestingly, considering behavioural heuristics based on ‘psychology experiments’ (as Farmer and Foley suggest) ends up leading us further down the reductionist rabbit-hole into the realm of prospect theory, developed by Kahnemann and Tversky (1979) who advocate the important role of human psychology in determining economic behaviour. By zooming in on the psychology of agents, the theory is able to explain why ‘We have an irrational tendency to be less willing to gamble with profits than with losses.’
Proponents of the agent-based modeling paradigm claim that current ‘microfounded’ macromodels do not truly reduce the system to the agent-level and moreover, over-rely on the rational expectations assumption (leading to the representative agent framework), which does not allow for the presence of endogenous emergent phenomena. Economist Kevin Hoover has humorously referred to such models as ‘nothing else but an aggregate in microeconomic drag’ (Hoover, 2006). However, this does not spell the end for locating the fundamentals of economics. Agent-based models and behavioural economics show that, by using computational models combined with simple heuristics, one can produce aggregate variables that emerge endogenously due to the repeated interactions of the agents comprising the system. The challenge is now to provide an understanding of what macroeconomic conditions (i.e individual characteristics coupled to the structure of interactions between them) can generate (in)stability of the system in order to guide policy-makers to formulate regulatory framework and monitor it accurately.
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Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica: Journal of the Econometric Society, 263-291.
Körner, T.W. (1988), Nonlinear oscillator, in ‘Fourier Analysis’, Cambridge University Press.
Kydland, F. E., & Prescott, E. C. (1982). Time to build and aggregate fluctuations. Econometrica: Journal of the Econometric Society, 1345-1370.
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