Comparing Behavioral Models Using Data from Experimental Centipede Games

The centipede game posits one of the most well‐known paradoxes of backward induction in the literature of experimental game theory. Given that deviations from the unique subgame perfect Nash equilibrium generates a Pareto improvement, several theoretical models have been employed in order to rationalize this kind of behavior in this social dilemma. The available explanations range from social preferences including fairness, altruism or cooperation motives, errors in playing, inability to perform backward induction or different depths of reasoning. In the present study, we use the Blavatskyy's theoretical contribution, and relax the assumptions of Expected Utility maximization and risk‐neutral attitudes, to test an alternative explanation. We compare various probabilistic decision theory models in terms of their descriptive (in‐sample) and predictive (out‐of‐sample fit) performance, using data from experimental centipede games. We find that introducing non‐Expected Utility preferences to the Quantal Response Equilibrium model, along with a nonlinear utility function, provides a better explanation compared to alternative specifications such as the Level‐k or the Quantal Response Equilibrium model with altruistic motives