IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2210.07322.html
   My bibliography  Save this paper

Human Behavioral Models Using Utility Theory and Prospect Theory

Author

Listed:
  • Anuradha M. Annaswamy
  • Vineet Jagadeesan Nair

Abstract

Several examples of Cyber-physical human systems (CPHS) include real-time decisions from humans as a necessary building block for the successful performance of the overall system. Many of these decision-making problems necessitate an appropriate model of human behavior. Tools from Utility Theory have been used successfully in several problems in transportation for resource allocation and balance of supply and demand \citep{ben1985discrete}. More recently, Prospect Theory has been demonstrated as a useful tool in behavioral economics and cognitive psychology for deriving human behavioral models that characterize their subjective decision-making in the presence of stochastic uncertainties and risks, as an alternative to conventional Utility Theory \citep{kahneman_prospect_2012}. These models will be described in this article. Theoretical implications of Prospect Theory are also discussed. Examples will be drawn from transportation use cases such as shared mobility to illustrate these models as well as the distinctions between Utility Theory and Prospect Theory.

Suggested Citation

  • Anuradha M. Annaswamy & Vineet Jagadeesan Nair, 2022. "Human Behavioral Models Using Utility Theory and Prospect Theory," Papers 2210.07322, arXiv.org.
    • Handle: RePEc:arx:papers:2210.07322
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2210.07322
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Marc Oliver Rieger & Mei Wang & Thorsten Hens, 2017. "Estimating cumulative prospect theory parameters from an international survey," Theory and Decision, Springer, vol. 82(4), pages 567-596, April.
    2. Mohammed Abdellaoui & Han Bleichrodt & Olivier L’Haridon, 2008. "A tractable method to measure utility and loss aversion under prospect theory," Journal of Risk and Uncertainty, Springer, vol. 36(3), pages 245-266, June.
    3. Drazen Prelec, 1998. "The Probability Weighting Function," Econometrica, Econometric Society, vol. 66(3), pages 497-528, May.
    4. Tversky, Amos & Kahneman, Daniel, 1992. "Advances in Prospect Theory: Cumulative Representation of Uncertainty," Journal of Risk and Uncertainty, Springer, vol. 5(4), pages 297-323, October.
    5. Wang, Shenhao & Zhao, Jinhua, 2019. "Risk preference and adoption of autonomous vehicles," Transportation Research Part A: Policy and Practice, Elsevier, vol. 126(C), pages 215-229.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ruß, Jochen & Schelling, Stefan, 2021. "Return smoothing in life insurance from a client perspective," Insurance: Mathematics and Economics, Elsevier, vol. 101(PA), pages 91-106.
    2. Lobel, Robert Eugene & Klotzle, Marcelo Cabus & Silva, Paulo Vitor Jordão da Gama & Pinto, Antonio Carlos Figueiredo, 2017. "Teoria do prospecto: Uma análise paramétrica de formas funcionais no Brasil," RAE - Revista de Administração de Empresas, FGV-EAESP Escola de Administração de Empresas de São Paulo (Brazil), vol. 57(5), October.
    3. Mohammed Abdellaoui & Olivier L’Haridon & Horst Zank, 2010. "Separating curvature and elevation: A parametric probability weighting function," Journal of Risk and Uncertainty, Springer, vol. 41(1), pages 39-65, August.
    4. Bocqueho, Geraldine & Jacquet, Florence & Reynaud, Arnaud, 2011. "Expected Utility or Prospect Theory Maximizers? Results from a Structural Model based on Field-experiment Data," 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland 114257, European Association of Agricultural Economists.
    5. Sanjit Dhami & Narges Hajimoladarvish, 2020. "Mental Accounting, Loss Aversion, and Tax Evasion: Theory and Evidence," CESifo Working Paper Series 8606, CESifo.
    6. Mohammed Abdellaoui & Emmanuel Kemel, 2014. "Eliciting Prospect Theory When Consequences Are Measured in Time Units: “Time Is Not Money”," Management Science, INFORMS, vol. 60(7), pages 1844-1859, July.
    7. Heutel, Garth, 2019. "Prospect theory and energy efficiency," Journal of Environmental Economics and Management, Elsevier, vol. 96(C), pages 236-254.
    8. Víctor González-Jiménez, 2021. "Incentive contracts when agents distort probabilities," Vienna Economics Papers vie2101, University of Vienna, Department of Economics.
    9. Campos-Vazquez, Raymundo M. & Cuilty, Emilio, 2014. "The role of emotions on risk aversion: A Prospect Theory experiment," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 50(C), pages 1-9.
    10. Matthew D. Rablen, 2023. "Loss Aversion, Risk Aversion, and the Shape of the Probability Weighting Function," Working Papers 2023013, The University of Sheffield, Department of Economics.
    11. Kontek, Krzysztof, 2011. "What is the actual shape of perception utility?," MPRA Paper 31715, University Library of Munich, Germany.
    12. Peter Brooks & Simon Peters & Horst Zank, 2014. "Risk behavior for gain, loss, and mixed prospects," Theory and Decision, Springer, vol. 77(2), pages 153-182, August.
    13. Simon Gächter & Eric J. Johnson & Andreas Herrmann, 2022. "Individual-level loss aversion in riskless and risky choices," Theory and Decision, Springer, vol. 92(3), pages 599-624, April.
    14. Kpegli, Yao Thibaut & Corgnet, Brice & Zylbersztejn, Adam, 2023. "All at once! A comprehensive and tractable semi-parametric method to elicit prospect theory components," Journal of Mathematical Economics, Elsevier, vol. 104(C).
    15. Rablen, Matthew D., 2019. "Foundations of the Rank-Dependent Probability Weighting Function," IZA Discussion Papers 12701, Institute of Labor Economics (IZA).
    16. Adam Booij & Bernard Praag & Gijs Kuilen, 2010. "A parametric analysis of prospect theory’s functionals for the general population," Theory and Decision, Springer, vol. 68(1), pages 115-148, February.
    17. Yao Thibaut Kpegli, 2023. "Smoothing Spline Method for Measuring Prospect Theory Components," Working Papers 2303, Groupe d'Analyse et de Théorie Economique Lyon St-Étienne (GATE Lyon St-Étienne), Université de Lyon.
    18. Kemel, Emmanuel & Paraschiv, Corina, 2013. "Prospect Theory for joint time and money consequences in risk and ambiguity," Transportation Research Part B: Methodological, Elsevier, vol. 56(C), pages 81-95.
    19. Donkers, A.C.D. & Lourenço, C.J.S. & Dellaert, B.G.C. & Goldstein, D.G., 2013. "Using Preferred Outcome Distributions to Estimate Value and Probability Weighting Functions in Decisions under Risk," ERIM Report Series Research in Management ERS-2013-005-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    20. Nathaniel T. Wilcox, 2017. "Random expected utility and certainty equivalents: mimicry of probability weighting functions," Journal of the Economic Science Association, Springer;Economic Science Association, vol. 3(2), pages 161-173, December.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2210.07322. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.