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Computing the Perfect Model: Why Do Economists Shun Simulation?

Published online by Cambridge University Press:  01 January 2022

Abstract

Like other mathematically intensive sciences, economics is becoming increasingly computerized. Despite the extent of the computation, however, there is very little true simulation. Simple computation is a form of theory articulation, whereas true simulation is analogous to an experimental procedure. Successful computation is faithful to an underlying mathematical model, whereas successful simulation directly mimics a process or a system. The computer is seen as a legitimate tool in economics only when traditional analytical solutions cannot be derived, i.e., only as a purely computational aid. We argue that true simulation is seldom practiced because it does not fit the conception of understanding inherent in mainstream economics. According to this conception, understanding is constituted by analytical derivation from a set of fundamental economic axioms. We articulate this conception using the concept of economists' perfect model. Since the deductive links between the assumptions and the consequences are not transparent in ‘bottom-up’ generative microsimulations, microsimulations cannot correspond to the perfect model and economists do not therefore consider them viable candidates for generating theories that enhance economic understanding.

Type
Research Article
Copyright
Copyright © The Philosophy of Science Association

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Footnotes

Previous versions of this paper have been presented at Philosophical Perspectives on Scientific Understanding in Amsterdam and at ECAP 05 in Lisbon. The authors would like to thank Tarja Knuuttila, Erika Mattila, Jani Raerinne, and Petri Ylikoski for helpful comments and Joan Nordlund for correcting the language. Jaakko Kuorikoski would also like to thank the Finnish Cultural Foundation for support of this research.

References

Axelrod, Robert (1997), “Advancing the Art of Simulation in the Social Sciences”, in Conte, Rosaria, Hegselmann, Rainer, and Terna, Pietro (eds.), Simulating Social Phenomena. Heidelberg: Springer, 2140.CrossRefGoogle Scholar
Axtell, Robert (2000), “Why Agents? On the Varied Motivations for Agent Computing in the Social Sciences”, Center on Social and Economic Dynamics, working paper number 17.Google Scholar
Backhouse, Roger E. (1998), “If Mathematics Is Informal, Then Perhaps We Should Accept That Economics Must Be Informal Too”, If Mathematics Is Informal, Then Perhaps We Should Accept That Economics Must Be Informal Too 108:18481858.Google Scholar
Bona, Jerry L., and Santos, Manuel S. (1997), “On the Role of Computation in Economic Theory”, On the Role of Computation in Economic Theory 72:241281.Google Scholar
Boyd, Robert, and Richerson, Peter (1987), “Simple Models of Complex Phenomena: The Case of Cultural Evolution”, in Dupré, John (ed.), The Latest on the Best. Cambridge, MA: MIT Press, 2752.Google Scholar
Canova, Fabio (1995), “Sensitivity Analysis and Model Evaluation in Simulated Dynamic General Equilibrium Economies”, Sensitivity Analysis and Model Evaluation in Simulated Dynamic General Equilibrium Economies 36:477501.Google Scholar
Clarkson, Geoffrey P. E., and Simon, Herbert A. (1960), “Simulation of Individual and Group Behavior”, Simulation of Individual and Group Behavior 50:920932.Google Scholar
Cloutier, Martin L., and Rowley, Robin (2000), “The Emergence of Simulation in Economic Theorizing and Challenges to Methodological Standards”, Centre de Recherche en Gestion, document 20-2000.Google Scholar
DeMeyer, Frank, and Plott, Charles R. (1970), “The Probability of a Cyclical Majority”, The Probability of a Cyclical Majority 38:345354.Google Scholar
DeMillo, Richard A., Lipton, Richard J., and Perlis, Alan J. (1979), “Social Processes and Proofs of Theorems and Programs”, Social Processes and Proofs of Theorems and Programs 22:271280.Google Scholar
De Regt, Henk W., and Dieks, Dennis (2005), “A Contextual Approach to Scientific Understanding”, A Contextual Approach to Scientific Understanding 144:137170.Google Scholar
Dion, Douglas (1992), “The Robustness of the Structure-Induced Equilibrium”, The Robustness of the Structure-Induced Equilibrium 36:462483.Google Scholar
Dowling, Deborah (1999), “Experimenting on Theories”, Experimenting on Theories 12:261273.Google Scholar
Epstein, Joshua M. (2006), “Remarks on the Foundations of Agent-Based Generative Social Science”, in Tesfatsion, Leigh S. and Judd, Kenneth L. (eds.), Handbook of Computational Economics, Vol. 2. Dordrecht: Elsevier, 15851604.Google Scholar
Epstein, Joshua M., and Axtell, Robert (1997), Growing Artificial Societies: Social Science from the Bottom Up. Washington, DC: Brookings Institution Press.Google Scholar
Fetzer, James H. (1988), “Program Verification: The Very Idea”, Program Verification: The Very Idea 31:10481063.Google Scholar
Fetzer, James H. (1991), “Philosophical Aspects of Program Verification”, Philosophical Aspects of Program Verification 1:197216.Google Scholar
Fishburn, Peter C., and Gehrlein, William V. (1976), “An Analysis of Simple Two-Stage Voting Systems”, An Analysis of Simple Two-Stage Voting Systems 21:112.Google Scholar
Friedman, Milton (1953), “The Methodology of Positive Economics”, in Essays in Positive Economics. Chicago: University of Chicago Press, 343.Google Scholar
Galison, Peter (1996), “Computer Simulations and the Trading Zone”, in Galison, Peter and Stump, David J. (eds.), The Disunity of Science. Stanford, CA: Stanford University Press, 118157.Google Scholar
Gehrlein, William V. (1983), “Condorcet’s Paradox”, Condorcet’s Paradox 15:161197.Google Scholar
Gehrlein, William V. (2002), “Condorcet’s Paradox and the Likelihood of Its Occurrence: Different Perspectives on Balanced Preferences”, Condorcet’s Paradox and the Likelihood of Its Occurrence: Different Perspectives on Balanced Preferences 52:171199.Google Scholar
Gilbert, Nigel, and Terna, Pietro (2000), “How to Build and Use Agent-Based Models in Social Science”, How to Build and Use Agent-Based Models in Social Science 1:127.Google Scholar
Gilbert, Nigel, and Troitzsch, Klaus G. (1999), Simulation for the Social Scientist. Buckingham, Philadelphia: Open University Press.Google Scholar
Giocoli, Nicola (2003), Modeling Rational Agents: From Interwar Economics to Early Modern Game Theory. Cheltenham, UK: Edward Elgar.Google Scholar
Hansen, Lars P., and Heckman, James J. (1996), “The Empirical Foundations of Calibration”, The Empirical Foundations of Calibration 10:87104.Google Scholar
Hartmann, Stephan (1996), “The World as a Process: Simulations in the Natural and Social Sciences”, in Hegselmann, Rainer, Mueller, Ulrich, and Troitzsch, Karl (eds.), Modelling and Simulation in the Social Sciences from the Philosophy of Science Point of View. Dordrecht: Kluwer, 77100.CrossRefGoogle Scholar
Hausman, Daniel M. (1992), The Inexact and Separate Science of Economics. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Hindriks, Frank A. (2005), “Unobservability, Tractability and the Battle of Assumptions”, Unobservability, Tractability and the Battle of Assumptions 12:383406.Google Scholar
Hughes, R. I. G. (1999), “The Ising Model, Computer Simulation, and Universal Physics”, in Morgan, Mary S. and Morrison, Margaret (eds.), Models as Mediators: Perspectives on Natural and Social Science. Cambridge: Cambridge University Press, 97145.CrossRefGoogle Scholar
Humphreys, Paul (1991), “Computer Simulations”, in Fine, Arthur, Forbes, Micky, and Wessels, Linda (eds.), PSA 1990: Proceedings of the 1990 Biennial Meeting of the Philosophy of Science Association, Vol. 1. East Lansing, MI: Philosophy of Science Association, 497506.Google Scholar
Humphreys, Paul (2004), Extending Ourselves: Computational Science, Empiricism, and Scientific Method. Oxford: Oxford University Press.CrossRefGoogle Scholar
Johnson, Paul E. (1999), “Simulation Modeling in Political Science”, Simulation Modeling in Political Science 42:15091530.Google Scholar
Jones, Bradford, Radcliff, Benjamin, Taber, Charles, and Timpone, Richard (1995), “Condorcet Winners and the Paradox of Voting: Probability Calculations for Weak Preference Orders”, Condorcet Winners and the Paradox of Voting: Probability Calculations for Weak Preference Orders 89:137144.Google Scholar
Judd, Kenneth L. (1997), “Computational Economics and Economic Theory: Substitutes or Complements?”, Computational Economics and Economic Theory: Substitutes or Complements? 21:907942.Google Scholar
Judd, Kenneth L. (2001), “Computation and Economic Theory: Introduction”, Computation and Economic Theory: Introduction 18:16.CrossRefGoogle Scholar
Kitcher, Philip (1989), “Explanatory Unification and the Causal Structure of the World”, in Kitcher, Philip and Salmon, Wesley C. (eds.), Scientific Explanation, Minnesota Studies in the Philosophy of Science. Minneapolis: University of Minnesota Press, 410505.Google Scholar
Kitcher, Philip (1993), The Advancement of Science: Science without Legend, Objectivity without Illusions. New York: Oxford University Press.Google Scholar
Klahr, David (1966), “A Computer Simulation of the Paradox of Voting”, A Computer Simulation of the Paradox of Voting 60:384390.Google Scholar
Kydland, Finn E., and Prescott, Edward C. (1996), “The Computational Experiment: An Econometric Tool”, The Computational Experiment: An Econometric Tool 10:6985.Google Scholar
LeBaron, Blake, Arthur, W. B., and Palmer, Richard (1999), “Time Series Properties of an Artificial Stock Market”, Time Series Properties of an Artificial Stock Market 23:14871516.Google Scholar
Leombruni, Roberto, and Richiardi, Matteo (2005), “Why Are Economists Sceptical about Agent-Based Simulations?”, Why Are Economists Sceptical about Agent-Based Simulations? 355:103109.Google Scholar
Levins, Richard (1966), “The Strategy of Models Building in Population Biology”, The Strategy of Models Building in Population Biology 54:421431.Google Scholar
MacKenzie, Donald A. (2001), Mechanizing Proof: Computing, Risk, and Trust. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Mäki, Uskali (1992), “On the Method of Isolation in Economics”, in Dilworth, C. (ed.), Intelligibility in Science, Vol. 26. Amsterdam: Rodopi, 319354.Google Scholar
Mäki, Uskali (1994), “Isolation, Idealization and Truth in Economics”, in Hamminga, Bert and De Marchi, Neil B. (eds.), Idealization VI: Idealization in Economics. Amsterdam: Rodopi, 147168.Google Scholar
Mäki, Uskali (2002), “Explanatory Ecumenism and Economics Imperialism”, Explanatory Ecumenism and Economics Imperialism 18:237259.Google Scholar
Mayer, Thomas (1993), Truth versus Precision in Economics. Aldershot, UK: Edward Elgar.Google Scholar
Mayer, Thomas (1999), “The Domain of Hypotheses and the Realism of Assumptions”, The Domain of Hypotheses and the Realism of Assumptions 6:319330.Google Scholar
Mirowski, Philip (1989), More Heat than Light. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Mirowski, Philip (2002), Machine Dreams: Economics Becomes a Cyborg Science. Cambridge: Cambridge University Press.Google Scholar
Morgan, Mary S. (2003), “Experiments without Material Intervention: Model Experiments, Virtual Experiments and Virtually Experiments”, in Radder, Hans (ed.), The Philosophy of Scientific Experimentation. Pittsburgh: University of Pittsburgh Press, 236254.Google Scholar
Morton, Rebecca B. (1999), Methods and Models: A Guide to the Empirical Analysis of Formal Models in Political Science. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Novales, Alfonso (2000), “The Role of Simulation Methods in Macroeconomics”, The Role of Simulation Methods in Macroeconomics 2:155181.Google Scholar
Oreskes, Naomi, Shrader-Frechette, Kristin, and Belitz, Kenneth (1994), “Verification, Validation, and Confirmation of Numerical Models in the Earth Sciences”, Verification, Validation, and Confirmation of Numerical Models in the Earth Sciences 263:641646.Google ScholarPubMed
Ostrom, Thomas M. (1988), “Computer Simulation: The Third Symbol System”, Computer Simulation: The Third Symbol System 24:381392.Google Scholar
Peck, Steven L. (2004), “Simulation as Experiment: A Philosophical Reassessment for Biological Modeling”, Simulation as Experiment: A Philosophical Reassessment for Biological Modeling 19:530534.Google ScholarPubMed
Petersen, Arthur C. (2000), “Philosophy of Climate Science”, Philosophy of Climate Science 81:265271.Google Scholar
Regenwetter, Michel, Grofman, Bernard, Marley, A. A., and Tsetlin, Ilia (2006), Behavioral Social Choice: Probabilistic Models, Statistical Inference, and Applications. Cambridge: Cambridge University Press.Google Scholar
Sugden, Robert (2001), “The Evolutionary Turn in Game Theory”, The Evolutionary Turn in Game Theory 8:113130.Google Scholar
Teller, Paul (2001), “Twilight of the Perfect Model Model”, Twilight of the Perfect Model Model 55:393415.Google Scholar
Tesfatsion, Leigh S. (2003), “Agent-Based Computational Economics”, ISU Economics, working paper number 1.CrossRefGoogle Scholar
Tesfatsion, Leigh S. (2006), “Agent-Based Computational Economics: A Constructive Approach to Economic Theory”, in Tesfatsion, Leigh S. and Judd, Kenneth L. (eds.), Handbook of Computational Economics, Vol. 2. Dordrecht: Elsevier, 831880.Google Scholar
Trout, J. D. (2002), “Scientific Explanation and the Sense of Understanding”, Scientific Explanation and the Sense of Understanding 69:212233.Google Scholar
Tymoczko, Thomas (1979), “The Four-Color Problem and Its Philosophical Significance”, The Four-Color Problem and Its Philosophical Significance 76:5783.Google Scholar
Van Deemen, Adrian (1999), “The Probability of the Paradox of Voting for Weak Preference Orderings”, The Probability of the Paradox of Voting for Weak Preference Orderings 16:171182.Google Scholar
Varian, Hal R. (1990), Intermediate Microeconomics: A Modern Approach, 2nd Edition. New York: Norton.Google Scholar
Wimsatt, William C. (1981), “Robustness, Reliability and Overdetermination”, in Brewer, Marilynn B. and Collins, Barry E. (eds.), Scientific Inquiry and the Social Sciences. San Francisco: Jossey-Bass, 124163.Google Scholar
Winsberg, Eric (2001), “Simulations, Models, and Theories: Complex Physical Systems and Their Representations”, Simulations, Models, and Theories: Complex Physical Systems and Their Representations 68:442454.Google Scholar
Winsberg, Eric (2003), “Simulated Experiments: Methodology for a Virtual World”, Simulated Experiments: Methodology for a Virtual World 70:105125.Google Scholar