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A Pragmatic Reading of Friedman's Methodological Essay and What It Tells Us for the Discussion of ABMs

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Abstract

The issues of empirical calibration of parameter values and functional relationships describing the interactions between the various actors plays an important role in agent based modelling. Agent-based models range from purely theoretical exercises focussing on the patterns in the dynamics of interactions processes to modelling frameworks which are oriented closely at the replication of empirical cases. ABMs are classified in terms of their generality and their use of empirical data. In the literature the recommendation can be found to aim at maximizing both criteria by building so-called 'abductive models'. This is almost the direct opposite of Milton Friedman's famous and provocative methodological credo 'the more significant a theory, the more unrealistic the assumptions'. Most methodologists and philosophers of science have harshly criticised Friedman's essay as inconsistent, wrong and misleading. By presenting arguments for a pragmatic reinterpretation of Friedman's essay, we will show why most of the philosophical critique misses the point. We claim that good simulations have to rely on assumptions, which are adequate for the purpose in hand and those are not necessarily the descriptively accurate ones.

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  • Simon Deichsel & Andreas Pyka, 2009. "A Pragmatic Reading of Friedman's Methodological Essay and What It Tells Us for the Discussion of ABMs," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(4), pages 1-6.
  • Handle: RePEc:jas:jasssj:2009-61-1
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    Cited by:

    1. 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.
    2. repec:hal:spmain:info:hdl:2441/dcditnq6282sbu1u151qe5p7f is not listed on IDEAS
    3. Vermeulen, Ben & Pyka, Andreas, 2016. "Agent-based modeling for decision making in economics under uncertainty," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 10, pages 1-33.
    4. repec:spo:wpmain:info:hdl:2441/dcditnq6282sbu1u151qe5p7f is not listed on IDEAS
    5. Giorgio Fagiolo & Andrea Roventini, 2016. "Macroeconomic Policy in DGSE and Agent-Based Models Redux," Working Papers hal-03459348, HAL.
    6. Thomas Brenner & Claudia Werker, 2009. "Policy Advice Derived from Simulation Models," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(4), pages 1-2.
    7. Troost, Christian & Huber, Robert & Bell, Andrew R. & van Delden, Hedwig & Filatova, Tatiana & Le, Quang Bao & Lippe, Melvin & Niamir, Leila & Polhill, J. Gareth & Sun, Zhanli & Berger, Thomas, 2023. "How to keep it adequate: A protocol for ensuring validity in agent-based simulation," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 159, pages 1-21.

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