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Modelling Agents’ Risk Perception

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Distributed Computing and Artificial Intelligence

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 217))

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Abstract

One of the open issues in risk literature is the difference between risk perception and effective risk, especially when the risk is clearly defined and measured. Until now, the main focus has been given on the behaviour of individuals and the evidences of their biases according to some stimulus. Consequently, it is important to analyse what are the main reasons for those biases and identify the dimensions and mechanisms involved. To that purpose, we tackle the classic problem of tax fraud as a case study. In this paper, we will look into how agent based modelling methodology can help unfold the reasons why individuals commit errors of judgment when risk is involved.

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Author information

Authors and Affiliations

  1. GUESS/LabMag, Universidade de Lisboa, Lisbon, Portugal

    Nuno Trindade Magessi & Luis Antunes

Authors
  1. Nuno Trindade Magessi
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  2. Luis Antunes
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Corresponding author

Correspondence to Nuno Trindade Magessi .

Editor information

Editors and Affiliations

  1. Graduate School of Engineering, Osaka Prefecture University, Sakai, Osaka, Japan

    Sigeru Omatu

  2. Department of Computing Science, University of Minho, Braga, Portugal

    José Neves

  3. Dept. of Computing Science and Control, University of Salamanca, Salamanca, Spain

    Juan M. Corchado Rodriguez

  4. Department of Computing Science, University of Salamanca, Salamanca, Spain

    Juan F Paz Santana

  5. Department of Computing Science, University of Salamanca, Salamanca, Spain

    Sara Rodríguez Gonzalez

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© 2013 Springer International Publishing Switzerland

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Magessi, N.T., Antunes, L. (2013). Modelling Agents’ Risk Perception. In: Omatu, S., Neves, J., Rodriguez, J., Paz Santana, J., Gonzalez, S. (eds) Distributed Computing and Artificial Intelligence. Advances in Intelligent Systems and Computing, vol 217. Springer, Cham. https://doi.org/10.1007/978-3-319-00551-5_34

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  • DOI: https://doi.org/10.1007/978-3-319-00551-5_34

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-00550-8

  • Online ISBN: 978-3-319-00551-5

  • eBook Packages: EngineeringEngineering (R0)

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