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Rapid Evolution under Inertia

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  • Thomas Norman

Abstract

This paper demonstrates that inertia driven by switching costs leads to more rapid evolution in a class of games that includes m x m pure coordination games. Under the best-response dynamic and a fixed rate of mutation, the expected waiting time to reach long-run equilibrium is of lower order in the presence of switching costs, due to the creation of new absorbing inertia states that allow Ellison`s (Review of Economic Studies 67, 2000, 17-45) step-by-step evolution to occur.

Suggested Citation

  • Thomas Norman, 2007. "Rapid Evolution under Inertia," Economics Series Working Papers 299, University of Oxford, Department of Economics.
    • Handle: RePEc:oxf:wpaper:299
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    Citations

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    Cited by:

    1. Ge Jiang & Simon Weidenholzer, 2017. "Local interactions under switching costs," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 64(3), pages 571-588, October.
    2. Cui, Zhiwei, 2023. "Linking friction, social coordination and the speed of evolution," Games and Economic Behavior, Elsevier, vol. 140(C), pages 410-430.
    3. Dai Zusai, 2018. "Distributional stability and deterministic equilibrium selection under heterogeneous evolutionary dynamics," Papers 1805.04895, arXiv.org.
    4. Ennio Bilancini & Leonardo Boncinelli, 2020. "The evolution of conventions under condition-dependent mistakes," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 69(2), pages 497-521, March.
    5. repec:esx:essedp:746 is not listed on IDEAS
    6. Eugenio Vicario, 2021. "Imitation and Local Interactions: Long Run Equilibrium Selection," Games, MDPI, vol. 12(2), pages 1-19, April.
    7. Sawa, Ryoji, 2021. "A stochastic stability analysis with observation errors in normal form games," Games and Economic Behavior, Elsevier, vol. 129(C), pages 570-589.
    8. Sawa, Ryoji, 2021. "A prospect theory Nash bargaining solution and its stochastic stability," Journal of Economic Behavior & Organization, Elsevier, vol. 184(C), pages 692-711.
    9. Daniel Stephenson, 2022. "Assignment feedback in school choice mechanisms," Experimental Economics, Springer;Economic Science Association, vol. 25(5), pages 1467-1491, November.
    10. Sawa, Ryoji & Wu, Jiabin, 2018. "Prospect dynamics and loss dominance," Games and Economic Behavior, Elsevier, vol. 112(C), pages 98-124.
    11. Jiang, Ge & Weidenholzer, Simon, 2014. "Local Interactions and Switching Costs," Economics Discussion Papers 9015, University of Essex, Department of Economics.
    12. Jonathan Newton, 2018. "Evolutionary Game Theory: A Renaissance," Games, MDPI, vol. 9(2), pages 1-67, May.
    13. Thomas Norman, 2010. "Cycles versus equilibrium in evolutionary games," Theory and Decision, Springer, vol. 69(2), pages 167-182, August.
    14. Nax, Heinrich H. & Newton, Jonathan, 2019. "Risk attitudes and risk dominance in the long run," Games and Economic Behavior, Elsevier, vol. 116(C), pages 179-184.
    15. Dai Zusai, 2018. "Tempered best response dynamics," International Journal of Game Theory, Springer;Game Theory Society, vol. 47(1), pages 1-34, March.
    16. Jiang, Ge & Weidenholzer, Simon, 2016. "Local Interactions under Switching Costs," Economics Discussion Papers 17770, University of Essex, Department of Economics.

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    More about this item

    Keywords

    Evolution; Mutations; Long-run Equilibrium; Waiting Times; Inertia; Switching Costs;
    All these keywords.

    JEL classification:

    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games

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