Abstract
In this paper, we show that Case-based decision theory, proposed by Gilboa and Schmeidler (Q J Econ 110(3):605–639, 1995), can explain the aggregate dynamics of cooperation in the repeated Prisoner’s Dilemma, as observed in the experiments performed by Camera and Casari (Am Econ Rev 99:979–1005, 2009). Moreover, we find CBDT provides a better fit to the dynamics of cooperation than does the existing Probit model, which is the first time such a result has been found. We also find that humans aspire to a payoff above the mutual defection outcome but below the mutual cooperation outcome, which suggests they hope, but are not confident, that cooperation can be achieved. Finally, our best-fitting parameters suggest that circumstances with more details are easier to recall. We make a prediction for future experiments: if the repeated PD were run for more periods, then we would be begin to see an increase in cooperation, most dramatically in the second treatment, where history is observed but identities are not. This is the first application of Case-based decision theory to a strategic context and the first empirical test of CBDT in such a context. It is also the first application of bootstrapped standard errors to an agent-based model.
Highlights
In this paper, we use Case-based Decision Theory (Gilboa and Schmeidler, 1995) to explain experimental data of human behavior in the repeated Prisoner’s Dilemma game
Case-based Software Agent (CBSA) was introduced in Pape and Kurtz (2013), who show that CBSA explains individual human behavior in a series of classification learning experiments from Psychology starting with Shepard, Hovland, and Jenkins (1961)
We find that the best-fitting parameters suggest (3) humans aspire to payoff value above the mutual defection outcome but below the mutual cooperation outcome, which suggests they hope but are not confident that cooperation can be achieved, and (4) circumstances with more details are easier to recall
Summary
We use Case-based Decision Theory (Gilboa and Schmeidler, 1995) to explain experimental data of human behavior in the repeated Prisoner’s Dilemma game. We find that the aggregate dynamics of cooperation are predicted by this theory. We fit the parameters of the model to data and establish that all parameters are statistically significant. We establish this fact by comparing experimental data collected by Camera and Casari (2009) against simulated data generated by a computer program called the Case-based Software Agent (CBSA). We show that CBSA can explain human group behavior in a setting that is dynamic and strategic
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