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Tactical Exploration of Tax Compliance Decisions in Multi-agent Based Simulation

  • Luis Antunes
  • João Balsa
  • Ana Respício
  • Helder Coelho
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4442)

Abstract

Tax compliance is a field that crosses over several research areas, from economics to machine learning, from sociology to artificial intelligence and multi-agent systems. The core of the problem is that the standing general theories cannot even explain why people comply as much as they do, much less make predictions or support prescriptions for the public entities. The compliance decision is a challenge posed to rational choice theory, and one that defies the current choice mechanisms in multi-agent systems. The key idea of this project is that by considering rationally-heterogeneous agents immersed in a highly social environment we can get hold of a better grasp of what is really involved in the individual decisions. Moreover, we aim at understanding how those decisions determine tendencies for the behaviour of the whole society, and how in turn those tendencies influence individual behaviour. This paper presents the results of some exploratory simulations carried out to uncover regularities, correlations and trends in the models that represent first and then expand the standard theories on the field. We conclude that forces like social imitation and local neighbourhood enforcement and reputation are far more important than individual perception of expected utility maximising, in what respects compliance decisions.

Keywords

Central Authority Ethical Attitude Social Simulation Internal Revenue Service Target Phenomenon 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Luis Antunes
    • 1
  • João Balsa
    • 1
  • Ana Respício
    • 1
  • Helder Coelho
    • 1
  1. 1.GUESS/Universidade de LisboaPortugal

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