Genetic Algorithms in Resource Economic Models

The paper describes the application of Genetic Algorithms to a Resource Economics problem; the decision about the intensity of exploitation of a renewable resource. Genetic Algorithms, developed by HOLLAND (1975), are a model of biological evolution, that captures some important features of evolution in general: selection, recombination and arbitrary mistakes. They have therefore also been used already to model economic evolution and learning processes. The model will be based on two main assumptions. First, the agents using the resource are not informed about its reproduction dynamics. And second, although profits are their only concern, they are not able to calculate the optimal extraction rate that would maximize present value of all present and future benefits, like in neoclassical Resource Economics. This is caused by restrictions on the informational as well as the "intellectual" level, all referred to as "bounded rationality". The paper explains the model and its results. It then discusses problems of the application of a biologically motivated prodecure to social evolution and makes some suggestions for a better adaptation of Genetic Algorithms for the purpose of economic modeling.

[1]  D. Pearce,et al.  Capital theory and the measurement of sustainable development: an indicator of “weak” sustainability , 1993 .

[2]  Herbert Dawid,et al.  On the convergence of genetic learning in a double auction market , 1999 .

[3]  W. Arthur,et al.  Complexity in Economic and Financial Markets , 1995 .

[4]  S. Faucheux,et al.  Decision-making under environmental uncertainty , 1995 .

[5]  Colin W. Clark,et al.  Mathematical Bioeconomics: The Optimal Management of Renewable Resources. , 1993 .

[6]  J. Holland,et al.  Artificial Adaptive Agents in Economic Theory , 1991 .

[7]  R. Selten.  Evolution, learning, and economic behavior , 1991 .

[8]  S. Winter,et al.  An Evolutionary Theory of Economic Change.by Richard R. Nelson; Sidney G. Winter , 1987 .

[9]  W. Arthur.  Complexity in economic theory: inductive reasoning and bounded rationality , 1994 .

[10]  The concept of sustainable yield in multi-species fisheries , 1991 .

[11]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[12]  Thomas Brenner,et al.  Can evolutionary algorithms describe learning processes? , 1998 .

[13]  Frank Beckenbach,et al.  Learning by Genetic Algorithms in Economics , 1999 .

[14]  N. Myers.  Ecological economics: The science and management of sustainability , 1992 .

[15]  Gérard Weisbuch,et al.  Information contagion and the economics of pollution , 1996 .

[16]  Edmund Chattoe,et al.  Just How (Un)realistic are Evolutionary Algorithms as Representations of Social Processes? , 1998, J. Artif. Soc. Soc. Simul..

[17]  Ronald A. Heiner,et al.  The origin of predictable dynamic behavior , 1989 .

[18]  David A. Lane,et al.  Artificial worlds and economics, part I , 1993 .

[19]  Herbert Dawid,et al.  Adaptive Learning by Genetic Algorithms, Analytical Results and Applications to Economic Models, 2nd extended and revised edition , 1999 .

[20]  R. Heiner.  The Origin of Predictable Behavior , 1983 .

[21]  David A. Lane.  Artificial worlds and economics, part II , 1993 .

[22]  W. Brian Arthur,et al.  On designing economic agents that behave like human agents , 1993 .

[23]  Herbert Dawid,et al.  Learning of cycles and sunspot equilibria by Genetic Algorithms , 1996 .

[24]  John H. Miller,et al.  Auctions with Artificial Adaptive Agents , 1995 .

[25]  Edmund Chattoe-Brown.  Just How (Un)realistic Are Evolutionary Algorithms As Representations of Social Processes , 1998 .

[26]  Wolfgang Weidlich,et al.  The master equation approach to nonlinear economics , 1992 .

[27]  R. Turner.  Sustainable environmental economics and management : principles and practice , 1993 .