Fusing non-conservative kinetic market models and evolutionary computing

Evandro Luquini, Guido Montagna and Nizam Omar

Physica A: Statistical Mechanics and its Applications, 2020, vol. 537, issue C

Abstract: This research establishes an identity between kinetic market models of econophysics and evolutionary algorithms of computer science. The fusion between the two approaches motivated a new market model with two basic operations: sampling and selection of states. The result is a non-conservative market that depends on the size of the sample set and the approach used to approximate the principle of energy conservation. This market exhibits complex dynamics with random walks for the sum of all the agents’ money and a scaling behavior for the money distribution in the population. Moreover, the fusion demonstrates how to add an evolutionary context to the kinetic market models and suggests a quasi-equilibrium version of those models. As a by-product, the work reveals a practical application as a new replacement rule for family competition evolutionary algorithms, which outperforms traditional ones in challenging combinatorial optimization problems.

Keywords: Money distribution; Non-conservative kinetic market models; Combinatorial optimization; Evolutionary algorithm; Random walk; Population diversity (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:537:y:2020:i:c:s0378437119314906

DOI: 10.1016/j.physa.2019.122606

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