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An introduction to ABED: Agent-based simulation of evolutionary game dynamics

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  • Izquierdo, Luis R.
  • Izquierdo, Segismundo S.
  • Sandholm, William H.

Abstract

ABED is free and open-source software for simulating evolutionary game dynamics in finite populations. We explain how ABED can be used to simulate a wide range of dynamics considered in the literature and many novel dynamics. In doing so, we introduce a general model of revisions for dynamic evolutionary models, one that decomposes strategy updates into selection of candidate strategies, payoff determination, and choice among candidates. Using examples, we explore ways in which simulations can complement theory in increasing our understanding of strategic interactions in finite populations.

Suggested Citation

  • Izquierdo, Luis R. & Izquierdo, Segismundo S. & Sandholm, William H., 2019. "An introduction to ABED: Agent-based simulation of evolutionary game dynamics," Games and Economic Behavior, Elsevier, vol. 118(C), pages 434-462.
  • Handle: RePEc:eee:gamebe:v:118:y:2019:i:c:p:434-462
    DOI: 10.1016/j.geb.2019.09.014
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    Cited by:

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    3. Sandholm, William H. & Izquierdo, Segismundo S. & Izquierdo, Luis R., 2019. "Best experienced payoff dynamics and cooperation in the Centipede game," Theoretical Economics, Econometric Society, vol. 14(4), November.
    4. Runtian Zhang & Jinye Li, 2020. "Impact of incentive and selection strength on green technology innovation in Moran process," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-15, June.
    5. Mertikopoulos, Panayotis & Sandholm, William H., 2018. "Riemannian game dynamics," Journal of Economic Theory, Elsevier, vol. 177(C), pages 315-364.
    6. Jiabin Wu & Hanzhe Zhang, 2022. "Polarization, antipathy, and political activism," Economic Inquiry, Western Economic Association International, vol. 60(3), pages 1005-1017, July.
    7. Yanping Xu & Lilong Zhu, 2022. "Pharmaceutical Enterprises’ R&D Innovation Cooperation Moran Strategy When Considering Tax Incentives," IJERPH, MDPI, vol. 19(22), pages 1-13, November.
    8. Mantas Radzvilas & Francesco De Pretis & William Peden & Daniele Tortoli & Barbara Osimani, 2020. "Double blind vs. open review: an evolutionary game logit-simulating the behavior of authors and reviewers," Papers 2011.07797, arXiv.org.
    9. Srinivas Arigapudi & Yuval Heller & Amnon Schreiber, 2021. "Sampling dynamics and stable mixing in hawk-dove games," Papers 2107.08423, arXiv.org, revised Jun 2022.
    10. Roland Mühlenbernd & Sławomir Wacewicz & Przemysław Żywiczyński, 2022. "The Evolution of Ambiguity in Sender—Receiver Signaling Games," Games, MDPI, vol. 13(2), pages 1-19, February.
    11. Jonathan Newton, 2018. "Evolutionary Game Theory: A Renaissance," Games, MDPI, vol. 9(2), pages 1-67, May.
    12. Arigapudi, Srinivas & Heller, Yuval & Schreiber, Amnon, 2021. "Sampling Dynamics and Stable Mixing in Hawk–Dove Games," MPRA Paper 108819, University Library of Munich, Germany.
    13. Zhijian Wang & Shujie Zhou & Qinmei Yao & Yijia Wang, 2022. "Dynamic Structure in Four-strategy Game: Theory and Experiment," Papers 2203.14669, arXiv.org.

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