2017/31 LEM Working Paper Series

Rational Heuristics? Expectations and Behaviors in Evolving Economies with Heterogeneous Interacting Agents

Giovanni Dosi, Mauro Napoletano, Andrea Roventini, Joseph E. Stiglitz and Tania Treibich
complexity, expectations, heterogeneity, heuristics, learning, agent-based model, computational economics

  JEL Classifications
C63, E32, E6, G01, G21, O4

We analyze the individual and macroeconomic impacts of heterogeneous expectations and action rules within an agent-based model populated by heterogeneous, interacting firms. Agents have to cope with a complex evolving economy characterized by deep uncertainty resulting from technical change, imperfect information and coordination hurdles. In these circumstances, we find that neither individual nor macroeconomic dynamics improve when agents replace myopic expectations with less naı̈ve learning rules. In fact, more sophisticated, e.g. recursive least squares (RLS) expectations produce less accurate individual forecasts and also considerably worsen the performance of the economy. Finally, we experiment with agents that adjust simply to technological shocks, and we show that individual and aggregate performances dramatically degrade. Our results suggest that fast and frugal robust heuristics are not a second-best option: rather they are “rational” in macroeconomic environments with heterogeneous, interacting agents and changing “fundamentals”.
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