The Evolution of Algorithmic Learning Rules: A Global Stability Result

Luca Anderlini and Hamid Sabourian

Economics Working Papers from European University Institute

Abstract: This paper considers the dynamic evolution of algorithmic (recursive) learning rules in a normal form game. It is shown that the system - the population frequencies - is globally stable for any arbitrary N-player normal form game, if the evolutionary process is algorithmic and the "birth process" guarantees that an appropriate set of "smart" rules is present in the population.

Keywords: ECONOMETRICS; LEARNING (search for similar items in EconPapers)
JEL-codes: C70 C72 C79 D83 (search for similar items in EconPapers)
Date: 1996

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Working Paper: The Evolution of Algorithmic Learning Rules: A Global Stability Result (1995)
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Persistent link: http://EconPapers.repec.org/RePEc:eui:euiwps:eco96/05

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