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Abstract
Introduction
The Abms Contribution to the Analysis of Markets
Conclusion
References

Agent-based modeling and economic theory: where do we stand?

Profile image of Gérard BallotGérard Ballot

2014, Journal of Economic Interaction and Coordination

https://doi.org/10.1007/S11403-014-0132-6
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Abstract

This paper provides a survey of the development and contributions of agentbased modeling to economic analysis, without any claim to be exhaustive. Our perspective is particularly shaped by the papers presented at the 17th Wehia conference, a selection of which are published in this special issue. The paper shows how agentbased models have developed, and how they have improved our understanding not only of macroeconomic disequilibria but also of the possibilities of the emergence of equilibrium in such realistic systems. It also reviews the progress made in our understanding of real markets, and lists some of the improvements needed to establish these models as alternative tools to the present orthodox models.

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References (100)

  1. Aghion P, Howitt P (2009) The economics of growth. The MIT Press, Cambridge Aoki M, Yoshikawa H (2011) Reconstructing macroeconomics: a perspective from statistical physics and combinatorial stochastic processes. Cambridge University Press, Cambridge
  2. Ashraf Q, Gershman B, Howitt P (2011) Banks, market organization, and macroeconomic performance: an agent-based computational analysis. Discussion paper w17102, National Bureau of Economic Research
  3. Assenza T, Delli Gatti D (2013) E pluribus unum: macroeconomic modelling for multi-agent economies. J Econ Dyn Control Elsevier 37(8):1659-1682
  4. Ballot G (1987) Rapport sur la modélisation microanalytique. Commissariat Général du Plan, France Ballot, G. (1988) Changement de Régulation dans un modèle du Marché du Travail dans D. KESSLER (sous la direction de), Economie Sociale, CNRS Press, Paris pp 15-35
  5. Ballot G (2002) Modeling the labour market as an evolving institution: model artemis. J Econ Behav Organ 49(1):51-77
  6. Ballot G, Taymaz E (1997) The dynamics of firms in a micro-macro model with training. learning and innovation. J Evol Econ 7(4):435-457
  7. Ballot G, Taymaz E (1998) Human capital, technological lock-in and evolutionary dynamics. In: Eliasson G, Green C (eds) Microfoundations of economic growth. A Schumpeterian perspective. The University of Michigan Press, Ann Arbor
  8. Ballot G, Taymaz E (1999) Technological change, organizational learning and macroeconomic coordination: an evolutionary model. J Artif Soc Soc Simul (JASSS) 2(2)
  9. Ballot G, Goudet O and Kant J-D (2013) Modeling both sides of the French labor market with adaptive agents under bounded rationality, EAEPE 2013 Conference 7-9 November
  10. Bargigli L, Gallegati M, Riccetti L and Russo A (2014) Network analysis and calibration of the leveraged network-based financial accelerator. J Econ Behav Organ (forthcoming)
  11. Baroni E, Richiardi M (2007) Orcutt's vision, 50 years on, Laboratorio Revelli, working paper 65, October 13 Fagiolo et al. (2007) review the many problems involved.
  12. The Krieging method is a method of interpolation which predicts unknown values from data observed at known locations. This method uses variogram to express the spatial variation , and it minimizes the error of predicted values which are estimated by spatial distribution of the predicted values.
  13. Barro R, Grossman H (1971) A general disequilibrium model of income and employment. Am Econ Rev 61:82-93
  14. Battiston S, Delli Gatti D, Gallegati M, Greenwald B, Stiglitz JE (2007) Credit chains and bankruptcies avalanches in supply networks. J Econ Dyn Control 31(6):2061-2084
  15. Beeboot H (1986) Evaluating reagan administration social program changes: two aplications of math. In: Orcutt G, Merz J, Quinke H (eds) Microanalytic simulation models to support social and financial policy. North Holland, Amsterdam Benassy JP (1975) Neo-Keynesian disequilibrium theory in a monetary economy. Rev Econ Stud 42(4): 503-523
  16. Bennett R, Bergmann B (1986) A microsimulated model of the United States Economy. The John Hopkins University Press, Baltimore
  17. Bergmann B (1974) A microsimulation of the macroeconomy with explicitly represented money flows. Ann Econ Soc Measur 3:475-479
  18. Bezemer D (2010) Understanding financial crisis through accounting models. Account Organ Soc 35:676- 688
  19. Bulow J, Klemperer P (1996) Auctions versus negotiations. Am Econ Rev 86:180-194
  20. Bouchaud (2008) Economics needs a scientific revolution. Nature 455(7217):1181
  21. Chan NT, LeBaron B, Lo AW, Poggio T (1999) Agent-based models of financial markets: a comparison with experimental markets. MIT artificial markets project
  22. Cheng W, Yang X (2004) Inframarginal analysis of division of labor: a survey. J Econ Behav Organ 55(2):137-174
  23. Chiaromonte F, Dosi G (1993) Heterogeneity, competition and macroeconomic dynamics. Struct Change Econ Dyn 4:39-63
  24. Dawid H (2006) Agent-based models of innovation and technological change. In: Tesfatsion L and Judd K (eds) Handbook of computational economics 2, 1235-1272
  25. Dawid H (2007) Evolutionary game dynamics and the analysis of agent-based imitation models: the long run, the medium run and the importance of a global analysis. J Econ Dyn Control 31:2108-213
  26. Dawid H, Gemkow S, Harting P, Neugart M (2012a) Labor market integration policies and the convergence of regions: the role of skills and technology diffusion. J Evol Econ 22:79-108
  27. Dawid H, Gemkow S, Harting P, van der Hoog S, Neugart M (2012b) Agent-based makroeconomic mod- eling and policy analysis: the eurace@unibi model. In: Chen S-H, Kaboudan M (eds) Handbook of computational economics and finance. Oxford University Press, Oxford Debreu G (1974) Excess demand functions. J Math Econ 1:15-23
  28. Delli Gatti D, Gallegati M, Greenwald B, Russo A, Stiglitz JE (2010) The financial accelerator in an evolving credit network. J Econ Dyn Control 34(9):1627-1650
  29. Delli Gatti D, Guilmi CD, Gaffeo E, Giulioni G, Gallegati M, Palestrini A (2005) A new approach to business fluctuations: heterogeneous interacting agents, scaling laws and financial fragility. J Econ Behav Organ 56(4):489-512
  30. Deissenberg C, van der Hoog S, Dawid H (2008) EURACE: A massively parallel agent-based model of the European economy. Appl Math Comput 204(2):541-552
  31. De Vroey M, Malgrange P (2011) The history of macroeconomics from Keyness General Theory to the Present. IRES Discussion Papers 2011028, Université catholique de Louvain Di Guilmi C, Landini S and Gallegati M (2013) Interactive macroeconomics, microfoundation framework via a stochastically dynamic aggregation of heterogeneous agents, forthcoming
  32. Dosi G, Fagiolo G, Roventini A (2010) Schumpeter meeting keynes: a policy-friendly model of endogenous growth and business cycles. J Econ Dyn Control 34(9):1748-1767
  33. Dosi G, Fagiolo G, Napoletano M, Roventini A (2013) Income distribution, credit and fiscal policies in an agent-based keynesian model. J Econ Dyn Control 37(8):1598-1625
  34. Drèze JH (1975) Existence of equilibrium under price rigidities. Int Econ Rev 16:301-20
  35. Eliasson C (1976) Business economic planning. Wiley, New York Eliasson G (1977), Competition and market processes in a simulation model of the Swedish economy. Am Econ Rev (67)1
  36. Eliasson G (1984) Micro-heterogeneity of firms and the stability of industrial growth. J Econ Behav Organ 5:249-274
  37. Eliasson G (1991a) Modelling the experimentally organized economy. complex dynamics in an empirical micro-macro model of endogenous economic growth. J Econ Behav Organ 13:273-298
  38. Eliasson G (1991b) Deregulation, innovative entry and structural diversity as a source of stable and rapid economic growth. J Evol Econ 1:49-63
  39. Fagiolo G, Dosi G (2003) Exploitation, exploration and innovation in a model of endogenous growth with locally interacting agents. Struct Change Econ Dyn 14:237-273
  40. Fagiolo G, Moneta A, Windrum P (2007) A critical guide to empirical validation of agent-based models in economics: methodologies. Proced Open Probl Comput Econ 30:195-226
  41. Farmer DFJ, Foley D (2009) The economy needs agent-based modelling. Nature 460(6):685-686
  42. Gabaix X (1999) Zipf's law for cities: an explanation. Q J Econ 114(3):739-767
  43. Gaffeo E, Gallegati M and Gostoli U (2012) An agent-based proof of principle for Walrasian macroeconomic theory, CEEL Working Papers 1202, Department of Economics, University of Trento, Italia Gallegati M, Richiardi MG (2009) Agent based models in economics and complexity. encyclopedia of complexity and systems science. Springer, New York, pp 200-224
  44. Gallegati M, Giulioni G, Kirman A and Palestrini A (2011) What's that got to do with the price of fish? Buyer's behavior on the ancona Fish Market. J Econ Behav Organ 80(1):20-33
  45. Galler HP, Wagner G (1986) The microsimulation model of the Sfb3 for the analysos of economic and social policies. In: Orcutt G, Merz J, Quinke H (eds) Microanalytic simulation models to support social and financial policy. North Holland, Amsterdam, pp 227-247
  46. Gauvin L, Vignes A, Nadal J-P (2013) Modeling urban housing market dynamics: can the socio-spatial segregation preserve some social diversity? J Econ Dyn Control Elsevier 37(7):1300-1321
  47. Gell-Mann M (1995) The quark and the jaguar: adventures in the simple and the complex. St Martin's press, New York Gintis H (2006) The emergence of a price system from decentralized bilateral exchange. B E J Theor Econ 6:1302-1322
  48. Gintis H (2007) The dynamics of general equilibrium. Econ J 117(523):1280-1309
  49. Gode DK, Sunder S (1993) Allocative efficiency of markets with zero-intelligence traders: market as a partial substitute for individual rationality. J Polit Econ 101(1):119-137
  50. Gouriéroux C, Monfort A, Renault E (1993) Indirect inference. J Appl Econom 8(S):85-118
  51. Grandmont J-M (1987) Distributions of preferences and the law of demand. Econometrica 55:155-161
  52. Granovetter M (1973) The strength of weak ties. Am J Sociol 78(6):1
  53. Greenwald BC, Stiglitz JE (1993) Financial market imperfections and business cycles. Q J Econ 108(1):77- 114
  54. Grossman SJ and Stiglitz JE (1980) On the impossibility of informationally efficient markets. Am Econ Rev 70:393-408
  55. Gualdi S, Tarzia M, Zamponi F, Bouchaud J-P (2013) Tipping points in macroeconomic agent-based models. Eprint arXiv:1307.5319
  56. Guerrero OA, Axtell RL (2011) Using agentization for exploring firm and labor dynamics emergent results of artificial economics. Lect Notes Econ Math Syst 652:139-150
  57. Guthrie H, Orcutt G, Peabody G, Caldwell S, Sadowski G (1972) Microanalytic simulation of household behavior. Ann Econ Soc Measur 2(1):141-169
  58. Harding A (ed) (1996) Microsimulation and public policy. Contributions to Economic Analysis Series. North Holland, Amsterdam
  59. Hayek FA (1948) Individualism and economic order. University of Chicago Press, Chicago Jaeger C (2012) Shaking an invisible hand complexity. Economics 1:91-103
  60. Hildenbrand W (1994) Market demand. Princeton University Press, Princeton Hoffmann A, Jager W, Eije JV (2007) Social simulation of stock markets: taking it to the next level. J Artif Soc Soc Simul 10(2)
  61. Iori G, Porter J (2012) Agent-based modelling for financial markets working papers 12/08, Department of Economics, City University London
  62. Kranton R, Minehart D (2001) A theory of buyer-seller networks american. Econ Rev 91:485-508
  63. Kranton RE, Minehart DF (2000) Networks versus vertical integration. Rand J Econ 5(3):570-601
  64. Kirman A, Moulet S (2009) Impact de l'organisation du marchÈ: comparaison de la nÈgociation de grÈ à grÈ et des enchËres descendantes, dans SystËmes Complexes et Sciences Sociales, Vuibert
  65. Kirman A, Vignes A (1990) Price dispersion: theoretical coordination and empirical evidence from the marseilles fish market. In: Arrow AKJ (ed) Issues in contemporary economics. MacMillan, New York Kirman A (2010) Routledge (ed) complex economics: individual and collective rationality graz schumpeter lectures
  66. Leijonhufvud A (2006) Agent-based macro. In: Tesfatsion L and Judd K (eds) Handbook of computational economics. Chapter 36, pp 1625-1636
  67. Lengnick M, Sebastian K, Wohltmann H-W (2013) Money creation and financial instability: an agent-based credit network approach. Econ Open-Access Open-Assessment E J 7(32):1-44
  68. Mandel A and Botta N (2009) A note on Herbert Gintis: emergence of a price system from decentralized bilateral exchange. B E J Theor Econ 9(1):1-18
  69. Mandel A, Gintis H (2013) Stochastic stability in the scarf economy. Math Soc Sci doi:10.1016/j.mathsocsci. 2013.09.002
  70. Mandel A, Landini S, Gallegati M, Gintis H (2013) Price dynamics, financial fragility and aggregate volatility. University Paris, C.E.S discussion paper 1
  71. Mantel R (1974) On the characterization of aggregate excess demand. J Econ Theor 7:348-353
  72. Marshall A (1890) Principles of economics. Macmillan, London Milgrom (2004) Milgrom P, Press CU (eds) Putting auction theory to work. Cambridge
  73. Milgrom, P. Bewley, T. (Ed.) (1986) Advances in economic theory: fifth world congress of the econometric society auction theory Cambridge University Press, London: Mac Millan and Co, pp 11-32
  74. Mignot S, Tedeschi G, Vignes A (2012) An agent based model of switching: the case of boulogne s/mer fish market. J Artif Soc Soc Simul 15(2):3
  75. Mitra-Kahn BH (2008). Debunking the myths of computable general equilibrium models, SCEPA Working Papers 2008-1, The New School for Social Research
  76. Moreno D, Wooders J (2010) Decentralized trade mitigates. Lemons Probl Int Econ Rev 51(2):383-399
  77. Nelson R and Winter S (1974) Neoclassical vs evolutionary theory theories of economic growth: critique and prospectus, Econ J 84(336):886-905
  78. Nelson R, Winter S (1982) An evolutionary theory of economic change. Harvard University Press, Cam- bridge Orcutt GH (1957) A new type of socio-economic system. Rev Econ Stat 39(2):116-123
  79. Orcutt GH (1990a) From engineering to microsimulation: an autobiographical reflection. J Econ Behav Organ 14(1):5-27
  80. Orcutt GH (1990b) The microanalytic approach for modeling national econcomies. J EconBehav Organ 14(1):29-41
  81. Orcutt GH, Merz J, Quinke H (eds) (1986) Microanalytic simulation models to support social and financial policy. North Holland, New York
  82. Pareto V (1896) Cours d'économie politique. Lausanne F Rouge 1896-97 (2 volumes)
  83. Peyton-Young H (1993) The evolution of conventions. Econometrica 61:57-84
  84. Pingle M and Tesfatsion L (2003) Evolution of worker-employer networks and behaviors under alternative unemployment benefits: an agent-based computational study, Chapter 12 (pp 256-285) in Anna Nagurney (ed) Innovations in economic and financial networks. Edward Elgar Publishers
  85. Progrebna G (2006) Auctions, versus bilateral bargaining: evidence from a natural experiment working paper, available at SSRN
  86. Salle I, Zumpe M, Yildizoglu M and Senegas M-A (2012) Modelling social learning in an agent-based new keynesian macroeconomic model, GREThA Discussion paper 2012-20
  87. Salle I, Yildizoglu M (2013) Efficient sampling and metamodeling for computational economic models. GREThA w.p
  88. Scarf H (1960) Some examples of global instability of competitive equilibrium. Int Econ Rev 1:157-172
  89. Scarf HE (1967) On the computation of equilibrium prices. In: Fellner WJ (ed) Ten economic studies in the tradition of irving fischer. Wiley, New York
  90. Seppecher P (2012) Flexibility of wages and macroeocnomic instability in an agent-based computational model with endogenous money. Macroecon Dyn 16(S2):284-297
  91. Silverberg G, Verspagen B (1994) Collective learning, innovation and growth in a boundedly rational, evolutionary world. J Evol Econ 4:207-226
  92. Simon HA (1959) Theories of decision-making in economics and behavioral science. Am Econ Rev 59:253- 283
  93. Sonnenschein H (1973) Do walras' identity and continuity characterize the class of community excess demand functions? J Econ Theor 6:345-354
  94. Spadaro A (2007) (ed) Microsimulation as a tool for the evaluation of public policies. Fundacion BBVA, Bilbao Schumpeter JA (1934) The theory of economic development: an inquiry into profits, capital, credit, interest and the business cycle. Transl. by Opie R. Harvard University Press, Cambridge
  95. Short MB, D'Orsogna MR, Brantingham PJ, Tita G (2010) Measuring and modeling repeat and near-repeat burglary effects. J Quant Criminol 25:325-339
  96. Taymaz E (1991) MOSES on PC. IUI Almqvist and Wicksell International, Stockholm Thiriot S, Lewkovicz Z, Caillou P, Kant JD (2011) Referral hiring and labor markets: a computational study, Artificial economics: Agent-based methods in finance, game theory and their application, The Hague. Springer, pp 15-25
  97. Thornton WH (1870) On labour: its wrongful claims and rightful dues, its actual present and possible future. London, ed
  98. Van der Leij M, Goyal S (2011) Strong ties in a small world. Rev Netw Econ 10(2):1-22
  99. Vignes A, Etienne J-M (2011) Price formation on the marseille fish market: evidence from a network analysis. J Econ Behav Organ 80:50-67
  100. G, Kirman A, Herreiner D (2000) Market organization and trading relationships. Econ J 110(463):411-436

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