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Learning, Memory, and Inertia

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Abstract

This paper explores the impact of memory in standard models of learning and evolution in games where a fininite number of agents display imitation behavior, focusing on coordination games (as in Kandori et al (1993)) and N-player games where spiteful behavior allows to discard Nash equilibria. It is shown that the way inertia is modeled in such examples actually entails a strong "no-memory" assumption. Once inertia is removed (or modeled otherwise), the addition of bounded memory changes the predictions dramatically. The analysis highlights the stability properties of Nash outcomes in purely evolutionary contexts with a finite population of agents.

Suggested Citation

  • Carlos Alós-Ferrer, 2000. "Learning, Memory, and Inertia," Vienna Economics Papers vie0003, University of Vienna, Department of Economics.
    • Handle: RePEc:vie:viennp:vie0003
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    References listed on IDEAS

    as
    1. Glenn Ellison, 2000. "Basins of Attraction, Long-Run Stochastic Stability, and the Speed of Step-by-Step Evolution," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 67(1), pages 17-45.
    2. Young, H Peyton, 1993. "The Evolution of Conventions," Econometrica, Econometric Society, vol. 61(1), pages 57-84, January.
    3. Fernando Vega-Redondo, 1997. "The Evolution of Walrasian Behavior," Econometrica, Econometric Society, vol. 65(2), pages 375-384, March.
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    More about this item

    JEL classification:

    • E62 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - Fiscal Policy; Modern Monetary Theory
    • H54 - Public Economics - - National Government Expenditures and Related Policies - - - Infrastructures
    • F43 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Economic Growth of Open Economies

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