Simulating in silos – The Economist discovers ABMs

An article in the Economist for 11 July 2010 argues that economic forecasting should make use of agent-based modelling (ABM). That it does not yet seem to have used ABM is a good example of the way simulation takes place in silos.

Traditional economic forecasting models are being blamed for failing to spot the recent economic crisis. Professor Joe Stiglitz argued in a recent letter that: “It is hard for non-economists to understand how peculiar the predominant macroeconomic models were. Many assumed demand had to equal supply – and that meant there could be no unemployment…. Many used “representative agent models” – all individuals were assumed to be identical, and this meant there could be no meaningful financial markets (who would be lending money to whom?). Information asymmetries, the cornerstone of modern economics, also had no place: they could arise only if individuals suffered from acute schizophrenia, an assumption incompatible with another of the favoured assumptions, full rationality.” (According to Wikipedia, “…Before the advent of models of imperfect and asymmetric information, the traditional neoclassical economics literature had assumed that markets are efficient except for some limited and well defined market failures. More recent work by Stiglitz and others reversed that presumption, to assert that it is only under exceptional circumstances that markets are efficient. Stiglitz has shown (together with Bruce Greenwald) that “whenever markets are incomplete and/or information is imperfect (which are true in virtually all economies), even competitive market allocation is not constrained Pareto efficient…”. ”

According to the Economist, there was ” a workshop in Virginia at the end of June….. funded by America’s National Science Foundation and attended by a diverse bunch that included economists from the Fed and the Bank of England, policy advisers and computer scientists….. to explore the potential of “agent-based models” (ABMs) of the economy to help learn the lessons of this crisis and, perhaps, to develop an early-warning system for the next one.”

I cant find any internet reference to this workshop. However, according to the Economist summary, it was argued that “Agent-based modelling does not assume that the economy can achieve a settled equilibrium. No order or design is imposed on the economy from the top down. Unlike many models, ABMs are not populated with “representative agents”: identical traders, firms or households whose individual behaviour mirrors the economy as a whole. Rather, an ABM uses a bottom-up approach which assigns particular behavioural rules to each agent. For example, some may believe that prices reflect fundamentals whereas others may rely on empirical observations of past price trends….Crucially, agents’ behaviour may be determined (and altered) by direct interactions between them, whereas in conventional models interaction happens only indirectly through pricing. This feature of ABMs enables, for example, the copycat behaviour that leads to “herding” among investors. The agents may learn from experience or switch their strategies according to majority opinion. They can aggregate into institutional structures such as banks and firms. These things are very hard, sometimes impossible, to build into conventional models. But in an agent-based model you simply run a computer simulation to see what emerges, free from any top-down assumptions….Although DSGE models are also based on microeconomic foundations, they accept the traditional view that there exists some ideal equilibrium towards which all prices are drawn. That this is often approximately true is why DSGE models perform well enough in a business-as-usual economy. They do badly in a crisis, however, because their “dynamic stochastic” element only amounts to minor fluctuations around a state of equilibrium, and there is no equilibrium during crashes….ABMs, in contrast, make no assumptions about the existence of efficient markets or general equilibrium. The markets that they generate are more like a turbulent river or the weather system, subject to constant storms and seizures of all sizes. Big fluctuations and even crashes are an inherent feature. That is because ABMs contain feedback mechanisms that can amplify small effects, such as the herding and panic that generate bubbles and crashes. In mathematical terms the models are “non-linear”, meaning that effects need not be proportional to their causes.”

Some work has been done already on ABMs, eg a paper by Professor Andrew Lo of MIT, in which “We construct a computer simulation of a repeated double-auction market, designed to match those in experimental-market settings with human subjects, to model complex interactions among artificially-intelligent traders endowed with varying degrees of learning capabilities. In the course of six different experimental designs, we investigate a number of features of our agent-based model: the price efficiency of the market, the speed at which prices converge to the rational expectations equilibrium price, the dynamics of the distribution of wealth among the different types of AI-agents, trading volume, bid/ask spreads, and other aspects of market dynamics. We are able to replicate several findings of human-based experimental markets, however, we also find intriguing differences between agent-based and human-based experiments.”

I argued in December 2005 that “simulation takes place in many different silos, and that people working in one field may not be aware of what is going on in others”. It seems incredible that economists are only starting to use ABMs, when you consuder that other social scientists (eg virtually anyone who writes for JASSS) uses little else.

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