Aspiration-Based and Reciprocity-Based Rules in Learning Dynamics for Symmetric Normal-Form Games

Psychologically-based rules are important in human behavior and have the potential of explaining equilibrium selection and separatrix crossings to a payoff dominant equilibrium in coordination games. We show how Rule Learning theory can easily accommodate behavioral rules like aspiration-based experimentation and reciprocity-based cooperation, and how to test for the significance of additional rules. We confront this enhanced Rule Learning model with experimental data on games with multiple equilibria and separatrix-crossing behavior. Maximum likelihood results do not support aspiration-based experimentation or anticipated reciprocity as significant explanatory factors, but does support a small propensity for nonaspiration-based experimentation by random belief and non-reciprocity-based cooperation.