Abstract
Interaction among autonomous decision-makers is usually modelled in economics in game-theoretic terms or within the framework of General Equilibrium. Game-theoretic and General Equilibrium models deal almost exclusively with the existence of equilibria and do not analyse the processes which might lead to them. Even when existence proofs can be given, two questions are still open. The first concerns the possibility of multiple equilibria, which game theory has shown to be the case even in very simple models and which makes the outcome of interaction unpredictable. The second relates to the computability and complexity of the decision procedures which agents should adopt and questions the possibility of reaching an equilibrium by means of an algorithmically implementable strategy. Some theorems have recently proved that in many economically relevant problems equilibria are not computable. A different approach to the problem of strategic interaction is a “constructivist” one. Such a perspective, instead of being based upon an axiomatic view of human behaviour grounded on the principle of optimisation, focuses on algorithmically implementable “satisfycing” decision procedures. Once the axiomatic approach has been abandoned, decision procedures cannot be deduced from rationality assumptions, but must be the evolving outcome of a process of learning and adaptation to the particular environment in which the decision must be made. This paper considers one of the most recently proposed adaptive learning models: Genetic Programming and applies it to one the mostly studied and still controversial economic interaction environment, that of oligopolistic markets. Genetic Programming evolves decision procedures, represented by elements in the space of functions, balancing the exploitation of knowledge previously obtained with the search of more productive procedures. The results obtained are consistent with the evidence from the observation of the behaviour of real economic agents.
Support to the research at different stages has been provided by the International Institute of Applied Systems Analysis (IIASA), Laxenburg, Austria, the Italian Ministry of University and Research (Murst 40%), the Italian Research Council (CNR, Progetto Strategico “Cambiamento Tecnologico e Sviluppo Economico”) and the Center for Research in Management, University of California, Berkeley. Comments by an anonymous referee and by the participants at seminars at the Cerisy Association (Cerisy, France), the Santa Fe Institute (Santa Fe, New Mexico), and in particular Kenneth Arrow, are gratefully acknowledged. This work was awarded the “International A. Kapp Prize” for 1994 by the European Association of Political and Evolutionary Economics.
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References
Arifovic J (1994) Genetic algorithm learning and the cobweb model. Journal of Economic Dynamics and Control 18: 3–25
Arrow K (1987) Oral history: an interview. In: Feiwel GR (ed) Arrow and the ascent of modern economic theory. MacMillan, London
Arthur WB (1992) On learning and adaptation in the economy. Santa Fe NM, Santa Fe Institute, working paper 92–07–038
Axelrod R (1984) The evolution of cooperation. Basic Books, New York
Casti JL (1992) Reality rules. Wiley, New York
Cohen D (1987) Computability and logic. Ellis Horwood, Chichester
Cohen M, Burkhart R, Dosi G, Egidi M, Marengo L, Warglien M, Winter S, Coriat B (1995) Routines and other recurring action patterns of organizations: Contemporary Research Issues. Santa Fe, Santa Fe Institute, WP 95–11–101, Industrial and Corporate Change (forthcoming)
Cutland NJ (1980) Computability: an introduction to recursive function theory. Cambridge University Press, Cambridge
Curzon-Price T (1997) Using co-evolutionary programming to simulate strategic behaviour in markets. Journal of Evolutionary Economics 7: 219–254
Dosi G, Egidi M (1991) Substantive and procedural uncertainty. An exploration of economic behaviours in complex and changing environments. Journal of Evolutionary Economics 1: 145–168
Dosi G, Freeman C, Nelson R, Silverberg G, Soete L (eds) (1988) Technical change and economic theory. Pinter, London
Dosi G, Marengo L (1994) Some elements of an evolutionary theory of organizational competences. In England RW (ed) Evolutionary concepts in contemporary economics, pp 157–78. University of Michigan Press, Ann Arbor
Dosi G, Marengo L, Fagiolo G (1996) Learning in evolutionary environments. Laxen-burg, Austria, International Institute for Applied Systems Analysis, Working Paper
Dosi G, Metcalfe JS (1991) On some notions of Irreversibility in Economics. In Saviotti PP, Metcalfe JS (eds) Evolutionary theories of economic and technological change. Harwood Academic Press, Chur
Elster J (1986) The multiple self. Cambridge University Press, Cambridge
Fontana W (1992) Algorithmic chemistry. In: Langton C, Farmer JD, Rasmussen S (eds) Artificial life. Addison Wesley, Redwood City, CA
Fontana W, Buss LW (1994) What would be conserved if “the tape were played twice”? Proceedings of the National Academy of Sciences USA, vol 91, pp 757–761
Friedman M (1953) Essays in positive economics. University of Chicago Press, Chicago
Goldberg DE (1989) Genetic algorithms in search. Optimization and learning. Addison Wesley, Reading, MA
Heiner RA (1983) The origin of predictable behavior. American Economic Review 73: 560–595
Heiner RA (1988) Imperfect decisions in organizations: toward a theory of internal structure. Journal of Economic Behavior and Organization 9: 25–44
Herrnstein RJ, Prelec D (1991) Melioration: a theory of distributed choice. Journal of Economic Perspectives 5: 137–156
Hodgson G (1988) Economics and institutions. Polity Press, London
Hogart RM, Reder MW (eds) (1986) Rational choice. Chicago University Press, Chicago
Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor
Holland JH, Holyoak KJ, Nisbett RE, Thagard PR (1986) Induction: processes of inference, learning and discovery. MIT Press, Cambridge, MA
Kahneman D, Slovic P, Tversky A (eds) (1982) Judgment under uncertainty: heuristics and biases. Cambridge University Press, Cambridge, MA
Kahneman D, Tversky A (1979) Prospect theory: an analysis of decision under risk. Econometrica 47: 263–291
Koford KJ, Miller JB (eds) (1991) Social norms and economic institutions. University of Michigan Press, Ann Arbor
Koza JR (1992) The genetic programming paradigm: genetically breeding populations of computer programs to solve problems. In: Soucek B (ed) Dynamic, genetic and chaotic programming Wiley, New York
Koza JR (1993) Genetic programming. MIT Press, Cambridge, MA
Lewis A (1985a) On effectively computable realization of choice functions. Mathematical Social Sciences 10: 43–80
Lewis A (1985b) The minimum degree of recursively representable choice functions. Mathematical Social Sciences 10: 179–188
Lewis A (1986) Structure and complexity. The use of recursion theory in the foundations of neoclassical mathematical economics and the theory of games. Cornell University, Department of Mathematics, Ithaca, mimeo
Lewis A (1987) On turing degrees of walrasian models and a general impossibility result in the theory of decision-making. Technical report n. 512, Institute for Mathematical Studies in the Social Sciences, Stanford University
Lindgren K (1991) Evolutionary phenomena in simple dynamics. In: Langton CG et al. (eds) Artificial life II. Addison Wesley, Redwood City, CA
Lippi M (1988) On the dynamics of aggregate macro equations: from simple micro behaviours to complex macro relationships. In: Dosi G et al. (eds) Technical change and economic theory, pp 170–196. Pinter, New York
Luhmann N (1979) Trust and power. Wiley, Chichester
March JG (1988) Decisions and organizations. Blackwell, Oxford
March JG (1994) A primer on decision making. Free Press, New York
Marengo L (1996) Structure, competences and learning in an adaptive model of the firm. In: Dosi G, Malerba F (eds) Organization and strategy in the evolution of the enterprise. MacMillan, London
Margolis H (1987) Patterns, thinking and cognition: A theory of judgement. Chicago University Press, Chicago
Miller JH (1988) The evolution of automata in the repeated prisoner’s dilemma. Santa Fe Institute, working paper
Nelson RR, Winter SG (1982) An evolutionary theory of economic change. Harvard University Press, Cambridge MA
Newell A, Simon H (1972) Human problem solving. Prentice-Hall, Englewood Cliffs, NJ
NJ Post E (1943) Formal reductions of the general combinatorial decision problem. American Journal of Mathematics 65: 197–215
Rabin MO (1957) Effective computability of winning strategies: contributions to the theory of games III. Annals of Mathematics Studies 39: 147–157
Rustem B, Velupillai K (1990) Rationality, computability and complexity. Journal of Economics Dynamics and Control 14: 419–432
Sen A (1977) Rational fools: a critique of the behavioral foundations of economic theory. Philosophy and Public Affairs 6: 317–344
Simon HA (1976) From substantive to procedural rationality. In: Latsis SJ (ed) Method and appraisal in economics, pp 129–148. Cambridge University Press, Cambridge
Simon HA (1981) The sciences of the artificial. MIT Press, Cambridge MA
Simon HA (1986) Rationality in psychology and economics. Journal of Business 59: supplement
Thrakhtenbrot DA (1963) Algorithms and automatic computing machines. Heath, Boston, MA
Winter SG (1971) Satisficing, selection and innovating remnant. Quarterly Journal of Economics 85: 237–261
Winter SG (1986) Adaptive behaviour and economic rationality: comments on Arrow and Lucas. Journal of Business 59: supplement
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Dosi, G., Marengo, L., Bassanini, A., Valente, M. (2002). Norms as emergent properties of adaptive learning: The case of economic routines. In: Cantner, U., Hanusch, H., Klepper, S. (eds) Economic Evolution, Learning, and Complexity. Physica, Heidelberg. https://doi.org/10.1007/978-3-642-57646-1_2
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DOI: https://doi.org/10.1007/978-3-642-57646-1_2
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