The smarter we are, the more complex the economy becomes, and the dumber we become.

Dynamic Stochastic General Equilibrium models assume we’re all playing a game where the goal is equilibrium. Unfortunately (or fortunately?), though we are all playing games, they’re not the same ones.

At least, that’s how I read this piece from The Economist, which is, frankly, a little beyond me, but contains the kind of phrasing that sets off pleasure nodes in my brain:

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 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.

These non-linearities were clearly on show in the credit crunch . . . These “network-based vulnerabilities” are just the kind of thing that ABMs are good at capturing.

Of course, ABMs need stacks and stacks of data. Of the real-time real-life variety, if possible. Which means the modellers are keen to plug in your phone, your Facebook, your Twitters and all that. Which I’m all for, to be honest. But we’ll see how it goes down with the tin-foil hatters.

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