Journal
PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
Volume 380, Issue 2224, Pages -Publisher
ROYAL SOC
DOI: 10.1098/rsta.2021.0165
Keywords
econophysics; wealth distribution; agent-based model
Categories
Funding
- Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior -Brasil (CAPES)
- Brazilian agency Fundacao de Amparo a Pesquisa do Estado do Rio Grande do Sul (FAPERGS)
- CAPES [1]
- Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq -Brazil)
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Genetic machine learning algorithms in the Yard-Sale model can find optimal strategies, but the more rational agents, the greater the inequality at the collective level. To address this, a taxation-redistribution mechanism is introduced, but rational agents lead to increased inequality.
Genetic machine learning (ML) algorithms to train agents in the Yard-Sale model proved very useful for finding optimal strategies that maximize their wealth. However, the main result indicates that the more significant the fraction of rational agents, the greater the inequality at the collective level. From social and economic viewpoints, this is an undesirable result since high inequality diminishes liquidity and trade. Besides, with very few exceptions, most agents end up with zero wealth, despite the inclusion of rational behaviour. To deal with this situation, here we include a taxation-redistribution mechanism in the ML algorithm. Previous results show that simple regulations can considerably reduce inequality if agents do not change their behaviour. However, when considering rational agents, different types of redistribution favour risk-averse agents, to some extent. Even so, we find that rational agents looking for optimal wealth can always arrive to an optimal risk, compatible with a particular choice of parameters, but increasing inequality.This article is part of the theme issue 'Kinetic exchange models of societies and economies'.
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