Abstract:
This study introduces a computational tool to analyze how a population of decision makers communicates and learns to coordinate to attain an equilibrium or a social convention in a two-sided matching game with incomplete information. Genetic algorithms are used in an environment where agents are heterogeneous and have private information. In the contexts of centralized and decentralized entry-level labor markets, evolution and adjustment paths of "unraveling" are explored using this tool. The situation of the Kagel and Roth (1997) laboratory experiment is generalized under a variety of markets and institutions. Evolution paths of unraveling are investigated, particularly for the historic entry-level British medical labor markets. As one result, it is demonstrated that "stability" need not be required for the success of a matching-mechanism under incomplete information in the long run. Evolutionary evidence is found to support the field success of unstable linear programming mechanisms used in Britain.
More papers in Computing in Economics and Finance 1999 from Society for Computational Economics Address: CEF99, Boston College, Department of Economics, Chestnut Hill MA 02467 USA Contact information at EDIRC. Series data maintained by Christopher F. Baum ().
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