Equilibrium Selection by Co-evolution for Bargaining Problems under Incomplete Information about Time Preferences

The main purpose of this work is to measure the impact of players' information completeness on the outcomes in dynamic strategic games. We apply co-evolutionary algorithms to solve four incomplete information bargaining problems and investigate the experimental outcomes on players' shares from agreements, the efficiency of agreements and the evolutionary time for convergence. Empirical analyses indicate that in the absence of complete information on the counterpart(s)' preferences, co-evolving populations are still able to select equilibriums which are Pareto-efficient and stationary. This property of the co-evolutionary algorithm supports its future applications on complex dynamic games.