View source for Genetic algorithms in economics

{{Main|genetic algorithm}}

'''[[Genetic algorithm]]s''' have increasingly been applied to economics since the pioneering work by John H. Miller in 1986. It has been used to characterize a variety of models including the [[cobweb model]], the [[overlapping generations model]], [[game theory]], [[Genetic algorithm scheduling|schedule optimization]] and [[asset pricing]]. Specifically, it has been used as a model to represent learning, rather than as a means for fitting a model.

== Genetic algorithm in the cobweb model ==

The [[cobweb model]] is a simple supply and demand model for a good over ''t'' periods. Firms (agents) make a production quantity decision in a given period, however their output is not produced until the following period. Thus, the firms are going to have to use some sort of method to forecast what the future price will be. The GA is used as a sort of learning behaviour for the firms. Initially their quantity production decisions are random, however each period they learn a little more. The result is the agents converge within the area of the [[rational expectations]] (RATEX) equilibrium for the stable and unstable case. If the election operator is used, the GA converges exactly to the RATEX equilibrium.

There are two types of learning methods these agents can be deployed with: social learning and individual learning. In social learning, each firm is endowed with a single string which is used as its quantity production decision. It then compares this string against other firms' strings. In the individual learning case, agents are endowed with a pool of strings. These strings are then compared against other strings within the agent's population pool. This can be thought of as mutual competing ideas within a firm whereas in the social case, it can be thought of as a firm learning from more successful firms. Note that in the social case and in the individual learning case with identical cost functions, that this is a homogeneous solution, that is all agents' production decisions are identical. However, if the cost functions are not identical, this will result in a heterogeneous solution, where firms produce different quantities (note that they are still locally homogeneous, that is within the firm's own pool all the strings are identical).

After all agents have made a quantity production decision, the quantities are aggregated and plugged into a demand function to get a price. Each firm's profit is then calculated. Fitness values are then calculated as a function of profits. After the offspring pool is generated, hypothetical fitness values are calculated. These hypothetical values are based on some sort of estimation of the price level, often just by taking the previous price level.

==See also==
*{{slink|List of genetic algorithm applications #Finance and Economics}}

== References ==
* J H Miller, 'A Genetic Model of Adaptive Economic Behavior', University of Michigan working paper, 1986.
* J Arifovic, 'Learning by Genetic Algorithm in Economic Environments', PhD Thesis, University of Chicago, 1991.
* J Arifovic, 'Genetic Algorithm Learning and the Cobweb Model ', Journal of Economic Dynamics and Control, vol. 18, Issue 1, (January 1994), 3&ndash;28.
* R Hoffmann, 'The independent localisations of interaction and learning in the repeated prisoner's dilemma', Theory and Decision, vol. 47, p.&nbsp;57–72, 1999.
* R Hoffmann, 'The ecology of cooperation', Theory and Decision, vol. 50, Issue 2. p.&nbsp;101–118, 2001.

==External links==
* [https://www.sfu.ca/crabe/ Centre for Adaptive Behaviour in Economics]
* [https://www2.econ.iastate.edu/tesfatsi/getalife.htm Agent-Based Computational Economics and Artificial Life: A Brief Intro]

[[Category:Computational economics]]
[[Category:Production economics]]
[[Category:Genetic algorithms]]
[[Category:Optimization algorithms and methods]]