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The Evolution of Algorithmic Learning Rules : A Global Stability Result

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Author Info
Anderlini, L
Sabourian, H

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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.

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Publisher Info
Paper provided by European University Institute in its series Economics Working Papers with number eco96/05.

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Length: 52 pages
Date of creation: 1996
Date of revision:
Handle: RePEc:eui:euiwps:eco96/05

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Related research
Keywords: ECONOMETRICS ; LEARNING;

Other versions of this item:

Find related papers by JEL classification:
C70 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - General
C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
C79 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Other
D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search, Learning, and Information

References listed on IDEAS
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  1. Blume, Lawrence & Easley, David, 1992. "Evolution and market behavior," Journal of Economic Theory, Elsevier, vol. 58(1), pages 9-40, October. [Downloadable!] (restricted)
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This page was last updated on 2009-12-11.


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