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Study of taxes, regulations and inequality using machine learning algorithms.

Julian Neñer1, Ben-Hur Francisco Cardoso2, María Fabiana Laguna3

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Genetic machine learning (ML) algorithms in economic models can maximize wealth but increase inequality. Introducing taxation-redistribution mechanisms can mitigate this, though rational agents may still drive inequality.

Keywords:
agent-based modeleconophysicswealth distribution

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Area of Science:

  • Computational Economics
  • Agent-Based Modeling
  • Machine Learning Applications

Background:

  • Genetic machine learning (ML) algorithms effectively train agents in economic models like the Yard-Sale model to optimize wealth.
  • A key finding is that increased fractions of rational agents lead to greater collective inequality, diminishing trade and liquidity.
  • Most agents, even with rational behavior, often end up with zero wealth.

Purpose of the Study:

  • To investigate the impact of a taxation-redistribution mechanism on wealth inequality within ML-driven economic models.
  • To analyze how rational agents' behavior interacts with regulatory interventions.
  • To explore strategies for mitigating inequality while maintaining agent optimization.

Main Methods:

  • Utilizing genetic machine learning (ML) algorithms to simulate agent behavior in the Yard-Sale model.
  • Integrating a taxation-redistribution mechanism into the ML algorithm.
  • Analyzing the effects of varying fractions of rational agents and redistribution parameters on wealth distribution.

Main Results:

  • The introduction of a taxation-redistribution mechanism can significantly reduce inequality, particularly when agent behavior remains constant.
  • Rational agents, while seeking optimal wealth, can select risk levels compatible with specific parameters, but this can still lead to increased inequality.
  • Even with redistribution, achieving equitable wealth distribution remains a challenge, with some agents potentially still ending with minimal wealth.

Conclusions:

  • Taxation-redistribution mechanisms are crucial for mitigating wealth inequality generated by rational agents in ML economic models.
  • Balancing agent optimization with societal equity requires careful calibration of redistribution parameters.
  • Further research is needed to fully reconcile individual wealth maximization with collective economic stability and fairness.