Gale-Shapley Matching in an Evolutionary Trade Network Game

This study investigates the performance of Gale-Shapley matching in an evolutionary market context. Computational experimental findings are reported for an evolutionary match-and-play trade network game in which resource-constrained traders repeatedly choose and refuse trade partners in accordance with Gale-Shapley matching, participate in risky trades modelled as two-person prisoner's dilemma games, and evolve their trade strategies over time. Particular attention is focused on correlations between ex ante market structure and the formation of trade networks, and between trade network formation and the types of trade behavior and social welfare outcomes that these trade networks support. The main conclusion drawn from this study is that the optimality criteria conventionally used to evaluate the performance of matching mechanisms in static market contexts -- namely, core stability and Pareto optimality -- are highly incomplete indicators of performance from an evolutionary vantage point. The static viewpoint hides the strong role played by market structure and ex ante capacity constraints in determining the types of persistent matching networks that evolve, the types of persistent interaction behaviors that these networks support, and the transactions costs and inactivity costs to agents that the achievement of these persistent networks and behaviors entails. In addition, the static viewpoint takes preference rankings over potential partners as given whereas these rankings are continuously updated on the basis of past interactions in evolutionary settings. Indeed, matching behaviors and interaction behaviors evolve conjointly. This suggests the need for more comprehensive optimality criteria that take both facets into account.