Equilibrium Selection in Alternating-Offers Bargaining Models: The EvolutionaryComputing Approach. Software Engineering

摘要

A systematic validation of evolutionary techniques in the field of bargaining ispresented. For this purpose, the dynamic and equilibrium-selecting behavior of a multi-agent system consisting of adaptive bargaining agents is investigated. The agents' bargaining strategies are updated by an evolutionary algorithm (EA), an innovative computational method to simulate collective learning in societies of boundedly-rational agents. Negotiations between the agents are governed by the well-known 'alternating-offers' protocol. Using this protocol, the influence of various important factors (like the finite length of the game, time preferences, exogenous breakdown, and risk aversiveness) is investigated. We show that game theory can be used successfully to interpret the equilibrium-selecting behavior observed in computational experiments with adaptive bargaining agents. Agreement between theory and experiment is especially good when the agents experience an intermediate time pressure. Deviations from classical game theory are, however, observed in several experiments. Violent nonlinear oscillations may for instance occur in the single-stage ultimatum game. We demonstrate that the specific evolutionary model governing agent selection is an important factor under these conditions.