Predicting human cooperation in the Prisoner's Dilemma using case-based decision theory

Authored by Todd Guilfoos, Andreas Duus Pape

Date Published: 2016

DOI: 10.1007/s11238-015-9495-y

Sponsors: United States Department of Agriculture (USDA)

Platforms: No platforms listed

Model Documentation: Other Narrative Mathematical description

Model Code URLs: Model code not found

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.

Tags

behavior Optimization Equilibria Similarity Reinforcement learning-models Extensive-form games Incomplete information Cognitive constraints Rational cooperation Supergames