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Modeling Economic Systems as Locally-Constructive Sequential Games

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  • Tesfatsion, Leigh

Abstract

Real-world economies are open-ended dynamic systems consisting of heterogeneous interacting participants. Human participants are decision-makers who strategically take into account the past actions and potential future actions of other participants. All participants are forced to be locally constructive, meaning their actions at any given time must be based on their local states; and participant actions at any given time affect future local states. Taken together, these properties imply real-world economies are locally-constructive sequential games. This study discusses a modeling approach, agent-based computational economics (ACE), that permits researchers to study economic systems from this point of view. ACE modeling principles and objectives are first concisely presented. The remainder of the study then highlights challenging issues and edgier explorations that ACE researchers are currently pursuing.

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  • Tesfatsion, Leigh, 2017. "Modeling Economic Systems as Locally-Constructive Sequential Games," ISU General Staff Papers 201707110700001022, Iowa State University, Department of Economics.
  • Handle: RePEc:isu:genstf:201707110700001022
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    1. Leigh Tesfatsion, 2017. "Elements of Dynamic Economic Modeling: Presentation and Analysis," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 43(2), pages 192-216, March.
    2. Paul L. Borrill & Leigh Tesfatsion, 2011. "Agent-based Modeling: The Right Mathematics for the Social Sciences?," Chapters, in: John B. Davis & D. Wade Hands (ed.), The Elgar Companion to Recent Economic Methodology, chapter 11, Edward Elgar Publishing.
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    5. Koppl, Roger & Kauffman, Stuart & Felin, Teppo & Longo, Giuseppe, 2015. "Economics for a creative world," Journal of Institutional Economics, Cambridge University Press, vol. 11(1), pages 1-31, March.
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    7. Tesfatsion, Leigh & Judd, Kenneth L., 2006. "Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics," Staff General Research Papers Archive 10368, Iowa State University, Department of Economics.
    8. Alvin Roth, 2008. "Deferred acceptance algorithms: history, theory, practice, and open questions," International Journal of Game Theory, Springer;Game Theory Society, vol. 36(3), pages 537-569, March.
    9. Tesfatsion, Leigh, 2006. "Agent-Based Computational Economics: A Constructive Approach to Economic Theory," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 16, pages 831-880, Elsevier.
    10. Claudius Gräbner & Jakob Kapeller, 2015. "New Perspectives on Institutionalist Pattern Modeling: Systemism, Complexity, and Agent-Based Modeling," Journal of Economic Issues, Taylor & Francis Journals, vol. 49(2), pages 433-440, April.
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    13. Roger Koppl & Stuart Kauffman & Giuseppe Longo & Teppo ­­­felin, 2015. "Economics for a creative world," Post-Print hal-01415131, HAL.
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    4. Eric Innocenti & Corinne Idda & Dominique Prunetti & Pierre-Régis Gonsolin, 2022. "Agent-based modelling of a small-scale fishery in Corsica," Post-Print hal-03886619, HAL.
    5. Koen de Koning & Tatiana Filatova & Okmyung Bin, 2019. "Capitalization of Flood Insurance and Risk Perceptions in Housing Prices: An Empirical Agent‐Based Model Approach," Southern Economic Journal, John Wiley & Sons, vol. 85(4), pages 1159-1179, April.
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    7. An, Li & Grimm, Volker & Sullivan, Abigail & Turner II, B.L. & Malleson, Nicolas & Heppenstall, Alison & Vincenot, Christian & Robinson, Derek & Ye, Xinyue & Liu, Jianguo & Lindkvist, Emilie & Tang, W, 2021. "Challenges, tasks, and opportunities in modeling agent-based complex systems," Ecological Modelling, Elsevier, vol. 457(C).

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