Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/174581
Authors: 
Dosi, Giovanni
Napoletano, Mauro
Roventini, Andrea
Stiglitz, Joseph E.
Treibich, Tania
Year of Publication: 
2017
Series/Report no.: 
LEM Working Paper Series 2017/31
Abstract: 
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 näive 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".
Subjects: 
complexity
expectations
heterogeneity
heuristics
learning
agent-based model
computational economics
JEL: 
C63
E32
E6
G01
G21
O4
Document Type: 
Working Paper

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