Can genetic algorithms explain experimental anomalies? An application to common property resources



provided by Research Papers in Economics

CAN GENETIC ALGORITHMS

EXPLAIN EXPERIMENTAL ANOMALIES?

AN APPLICATION TO COMMON PROPERTY RESOURCES

Marco Casari1

Universitat Autdnoma de Barcelona

First draft: October 2002
This version: April 2003

UFAE and IAE Working Paper number 542.02

Abstract. It is common to find in experimental data persistent oscillations in the aggregate
outcomes and high levels of heterogeneity in individual behavior. Furthermore, it is not
unusual to find significant deviations from aggregate Nash equilibrium predictions. In this
paper, we employ an evolutionary model with boundedly rational agents to explain these
findings. We use data from common property resource experiments (Casari and Plott, 2003).
Instead of positing individual-specific utility functions, we model decision makers as selfish
and identical. Agent interaction is simulated using an individual learning genetic algorithm,
where agents have constraints in their working memory, a limited ability to maximize, and
experiment with new strategies. We show that the model replicates most of the patterns that
can be found in common property resource experiments.

Keywords: Bounded rationality, Experiments, Common-pool resources, Genetic algorithms
JEL Classification numbers: C72, C63, C91, Q2

1 Correspondence address: Marco Casari, Departament d'Economia i d'Historia Economica, CODE, Edifici B,
Universitat Autdnoma de Barcelona, 08193 Bellaterra, Spain, email:
[email protected], tel: ++34.93.581
4068, fax: ++34.93.581 2461.

The paper has benefited from comments of Jasmina Arifovic, Simon Wilkie, Charles Plott, Nelson Mark,
Sean Gailmard, Guillaume Frechette, Ben Klemens and of participants at seminars at Ohio State University, the
5th Workshop in Experimental Economics in Siena, Italy, at the ESA meeting in San Diego, CA, USA,
University of Guelph, Canada, and the Simposio de Analisi Economica in Salamanca, Spain. Sharyn Slavin
Miller, Maria Satterwhite, and Eloisa Imel from Caltech provided technical support. Financial support from the
Division of the Humanities and Social Sciences at Caltech and the EU Marie Curie Fellowship is gratefully
acknowledged.



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