Backward Unraveling over Time: The Evolution of Strategic Behavior in the Entry-Level British Medical Labor Markets

This paper studies an adaptive artificial agent model using a genetic algorithm to analyze how a population of decision-makers learns to coordinate on the selection of an equilibrium or a social convention in a two-sided matching game. In the contexts of centralized and decentralized entry-level labor markets, evolution and adjustment paths of unraveling are explored using this model in an environment inspired by the Kagel and Roth (Quarterly Journal of Economics, 2000) experimental study. As an interesting result, it is demonstrated that stability need not be required for the success of a matching mechanism under incomplete information in the long run.