Convergence Rates in Resource Allocation Games

Daniel Stephenson ()
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Daniel Stephenson: Department of Economics, VCU School of Business

No 2304, Working Papers from VCU School of Business, Department of Economics

Abstract: If Nash equilibrium corresponds to the long run outcome of a dy- namic process, its usefulness as a predictive tool may depend on the rate of convergence to equilibrium. This paper experimentally tests theoretical predictions about the rate of convergence to equilibrium in settings where agents simultaneously allocate resources between contests with complementary prizes. More responsive contest suc- cess functions give agents a stronger incentive to best respond, but learning models predict slower convergence to equilibrium under more responsive success functions because of the incentives agents face out of equilibrium. Consistent with learning model predictions, we observe slower convergence under more responsive success functions, suggest- ing that disequilibrium incentives contain useful information about the rate of convergence to equilibrium in empirical settings.

Keywords: convergence; equilibrium; allocation; contest; learning (search for similar items in EconPapers)
JEL-codes: C73 C92 D74 Q34 (search for similar items in EconPapers)
Pages: 21 pages
Date: 2023-01
New Economics Papers: this item is included in nep-gth
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