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Learning to respect property by refashioning theft into trade

Published online by Cambridge University Press:  14 March 2025

Erik O. Kimbrough
Erik O. Kimbrough*
Affiliation:
Department of Economics (AE1), School of Business and Economics, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands
*
e-mail: [email protected]
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Abstract

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Agent-based simulations and human-subject experiments explore the emergence of respect for property in a specialization and exchange economy with costless theft. Software agents, driven by reciprocity and hill-climbing heuristics and parameterized to replicate humans when property is exogenously protected, are employed to predict human behavior when property can be freely appropriated. Agents do not predict human behavior in a new set of experiments because subjects innovate, constructing a property convention of “mutual taking” in 5 out of the 6 experimental sessions that allows exchange to crowd out theft. When the same convention is made available to agents, they adopt it and again replicate human behavior. Property emerges as a social convention that exploits the capacity for reciprocity to sustain trade.

Keywords

Property rightsConventionsExperimental economicsAgent-based modeling

JEL classification

C63: Computational Techniques • Simulation Modeling D51: Exchange and Production Economies F10: General C92: Laboratory, Group Behavior D83: Search • Learning • Information and Knowledge • Communication • Belief • Unawareness B41: Economic Methodology
Type
Research Article
Information
Experimental Economics , Volume 14 , Issue 1 , March 2011 , pp. 84 - 109
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NC
This is an open access article distributed under the terms of the Creative Commons Attribution Noncommercial License (https://creativecommons.org/licenses/by-nc/2.0), which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
Copyright
Copyright © The Author(s) 2010

Footnotes

Data, simulation code, and CSW experiment instructions available upon request. All simulations, data analysis, and figures were performed or created in R: A language and Environment for Statistical Computing (R Development Core Team 2010) with assistance from various contributed packages including: Bengtsson (2003), Berkelaar et al. (2008), Henningsen (2008), Neuwirth (2007), and Warnes et al. (2008).

Electronic supplementary material The online version of this article (doi:10.1007/s10683-010-9258-0) contains supplementary material, which is available to authorized users.

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