Abstract
The process of gradually finding an economic equilibrium, the so called tâtonnement process, is investigated in this paper. In constrast to classical general equilibrium modelling, where a central institution with perfect information about consumer preferences and production technologies (“Walrasian auctioneer”) organizes the economy, we simulate this process with learning consumer and producer agents, but no auctioneer. These agents lack perfect information on consumption preferences and are unable to explicitly optimize utility and profits. Rather, consumers base their consumption decision on past experience – formalized by reinforcement learning – whereas producers do regression learning to estimate aggregate consumer demand for profit maximization. Our results suggest that, even without perfect information or explicit optimization, it is possible for the economy to converge towards the analytically optimal state.
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References
Bowling, M., Veloso, M.: Multiagent learning using a variable learning rate. Artif. Intell. 136(2), 215–250 (2002)
Busoniu, L., Babuska, R., De Schutter, B.: A comprehensive survey of multiagent reinforcement learning. IEEE T. Syst. Man. Cy. C. 38(2), 156–172 (2008)
Colander, D., Rothschild, C.: Sins of the sons of samuelson: Vision, pedagogy, and the zig-zag windings of complex dynamics. J. Econ. Behav. Organ. 74(3), 277–290 (2010)
Erev, I., Roth, A.E.: Predicting how people play games: Reinforcement learning in experimental games with unique, mixed strategy equilibria. Am. Econ. Rev. 88(4), 848–881 (1998)
Gintis, H.: The dynamics of general equilibrium. Econ. J. 117(523), 1280–1309 (2007)
Gintis, H.: The dynamics of generalized market exchange. Santa Fe Institute Working Paper (2011)
Hsieh, C.Y., Mangum, S.L.: A search for synthesis in economic theory. M.E. Sharpe, New York (1986)
Kirman, A., Vriend, N.: Evolving market structure: An ace model of price dispersion and loyalty. J. Econ. Dyn. Control 25(3-4), 459–502 (2001)
Rieskamp, J., Busemeyer, J., Laine, T.: How do people learn to allocate resources? comparing two learning theories. J. Exp. Psychol. Learn. 29(6), 1066–1081 (2003)
Rouchier, J.: Agent-based simulation as a useful tool for the study of markets. Technical Report 8, GREQAM (2008)
Shoham, Y., Powers, R., Grenager, T.: If multi-agent learning is the answer, what is the question? Artif. Intell. 171(7), 365–377 (2007)
Spall, J.: Introduction to stochastic search and optimization: estimation, simulation, and control. Wiley Interscience, Hoboken (2003)
Tesfatsion, L., Judd, K.L.: Handbook of Computational Economics, vol. 2: Agent-Based Computational Economics. North-Holland, Amsterdam (2006)
Walras, L.: Elements of pure economics, or, The theory of social wealth. Allen and Unwin, London (1874/1954)
Wang, X., Sandholm, T.: Reinforcement learning to play an optimal nash equilibrium in team markov games. In: Becker, S., Thrun, S., Obermayer, K. (eds.) Advances in Neural Information Processing Systems, vol. 15, pp. 1571–1578. MIT Press, Cambridge (2003)
Weisbuch, G., Kirman, A., Herreiner, D.: Market organisation and trading relationships. Econ. J. 110(463), 411–436 (2000)
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Reich, G. (2011). Market Self-organization under Limited Information. In: Lozano, J.A., Gámez, J.A., Moreno, J.A. (eds) Advances in Artificial Intelligence. CAEPIA 2011. Lecture Notes in Computer Science(), vol 7023. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25274-7_4
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DOI: https://doi.org/10.1007/978-3-642-25274-7_4
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