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Macroeconomies as Constructively Rational Games

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  • Sinitskaya, Ekaterina
  • Tesfatsion, Leigh

Abstract

Real-world decision-makers are forced to be locally constructive, in the sense that their actions are constrained by the interaction networks, limited information, and computational capabilities at their disposal. This study poses the following question: Suppose utility-seeking consumers and profit-seeking firms in an otherwise standard dynamic macroeconomic model are required to be locally constructive decision-makers, unaided by the external imposition of global coordination conditions. What combinations of locally constructive decision rules result in good macroeconomic performance relative to a social planner benchmark model, and what are the game-theoretic properties of these decision-rule combinations? We begin our investigation of this question by specifying locally constructive decision rules for the consumers and firms that range from simple reinforcement learning to sophisticated adaptive dynamic programming algorithms. We then use computational experiments to explore macroeconomic performance under alternative decision-rule combinations. A key finding is that simpler rules can outperform more sophisticated rules, but that forward-looking behavior coupled with a relatively long memory permitting past observations to inform current decision-making is critical for good performance.

Suggested Citation

  • Sinitskaya, Ekaterina & Tesfatsion, Leigh, 2014. "Macroeconomies as Constructively Rational Games," Staff General Research Papers Archive 37834, Iowa State University, Department of Economics.
  • Handle: RePEc:isu:genres:37834
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Tesfatsion, Leigh, 2017. "Modeling Economic Systems as Locally-Constructive Sequential Games," ISU General Staff Papers 201702180800001022, Iowa State University, Department of Economics.
    2. Russo, Alberto, 2017. "Dopo il keynesismo: teorie economiche per una (non-) politica economica
      [After Keynesianism: Economic Theories for a (non) Economic Policy]
      ," MPRA Paper 83346, University Library of Munich, Germany.
    3. Giorgio Fagiolo & Andrea Roventini, 2017. "Macroeconomic Policy in DSGE and Agent-Based Models Redux: New Developments and Challenges Ahead," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 20(1), pages 1-1.
    4. 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.
    5. Giovanni Dosi & Mauro Napoletano & Andrea Roventini & Joseph E. Stiglitz & Tania Treibich, 2017. "Rational Heuristics? Expectations and Behaviors in Evolving Economies with Heterogeneous Interacting Agents," LEM Papers Series 2017/31, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    6. Pyo, Dong-Jin, 2015. "Animal spirits and stock market dynamics," ISU General Staff Papers 201501010800005596, Iowa State University, Department of Economics.
    7. Matteo G. Richiardi, 2015. "The future of agent-based modelling," LABORatorio R. Revelli Working Papers Series 141, LABORatorio R. Revelli, Centre for Employment Studies.
    8. repec:taf:jecmet:v:24:y:2017:i:4:p:384-409 is not listed on IDEAS
    9. Tesfatsion, Leigh, 2017. "Modeling Economic Systems as Locally-Constructive Sequential Games," ISU General Staff Papers 201704300700001022, Iowa State University, Department of Economics.
    10. Matteo G. Richiardi, 2017. "The Future of Agent-Based Modeling," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 43(2), pages 271-287, March.
    11. Tesfatsion, Leigh, 2017. "Modeling Economic Systems as Locally-Constructive Sequential Games," ISU General Staff Papers 201703280700001022, Iowa State University, Department of Economics.
    12. Leigh Tesfatsion, 2017. "Modeling economic systems as locally-constructive sequential games," Journal of Economic Methodology, Taylor & Francis Journals, vol. 24(4), pages 384-409, October.

    More about this item

    Keywords

    Learning; Macroeconomics; agent-based; game; stochastic optimization;

    JEL classification:

    • B4 - Schools of Economic Thought and Methodology - - Economic Methodology
    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • C7 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory
    • E2 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment

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