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Emotional balance and probability weighting

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Abstract

We find suggestive evidence that emotional balance has an impact on probability weighting incremental to demographic controls. Specifically, low negative affectivity (implying high emotional balance) tends to be a characteristic of those whose probability weighting functions exhibit lower curvature and more neutral elevation. In other words, emotional balance seems to push people in the direction of normative expected utility theory.

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Notes

  1. It is common to equate the EUT axioms with rationality (e.g., Wakker 2010; Edwards 1992).

  2. See Abdellaoui et al. (2008) for a discussion of different definitions of loss aversion.

  3. Probabilistic insensitivity (implied by these parameters exceeding 1.0) entails mid-range probabilistic insensitivity coupled with over-sensitivity in the neighborhood of the endpoints.

  4. Figure A1 of the Web-Appendix shows probability weighting functions based on the Tversky and Kahneman (1992) estimated parameters, as well as those estimated here.

  5. For example, using TK and Tversky and Kahneman’s (1992) median values of \(\gamma \) and \(\delta ,\) the intersections with the 45-degree line are at \(q = .34\) (for \(\gamma \)) and \(q = .38\) (for \(\delta \)).

  6. Prelec (1998) imposes the constraint \(\gamma =\delta \) for both his one-parameter and two-parameter models, showing this follows from his axiomatic approach. We find that both under P1 and P2 this constraint cannot be rejected for a majority of the subjects in our sample.

  7. See Web-Appendix for the experimental instructions along with screen shots viewed by participants.

  8. These questions as well as all other categories of questions are provided in the Web-Appendix.

  9. It is based on the PANAS instrument developed by Watson et al. (1988), who show that NA and PA are by and large uncorrelated, internally consistent and stable over time.

  10. The 33 questions from Schutte et al. (1998) were subjected to factor analysis by Petrides and Furnham (2000), who identified four factors, which they labeled optimism/mood regulation (9), appraisal of emotions (9), social skills (11), and utilization of emotions (4), where the number of questions with the highest loading on a particular factor is in brackets. Our questionnaire omits the 11 “social skills” questions, leaving us with 22 questions.

  11. See the Web-Appendix (Table A1; Fig. A3) for a table (with more detail) and frequency distributions.

  12. While the independent variables are clearly correlated, their degree of correlation is not severe enough so that multicollinearity is a problem.

  13. See Table A2 of Web-Appendix.

  14. If we restrict ourselves to one-parameter models, it is essentially a dead heat.

  15. From this point on the paper deals with estimates of PT parameters, not the true parameters. As a verbal abbreviation, however, we still speak of parameters when we really mean their estimates, but no confusion is likely to ensue from this. See the Web-Appendix for frequency distributions of the probability weighting function parameters (Fig. A4) as well as for the value function parameters \(\alpha \) and \(\beta \) (Fig. A5).

  16. Using the P1 and P2 median values of \(\gamma , \delta , \gamma _{1}, \delta _{1}, \gamma _{2}\) and \(\delta _{2}\) it is apparent (see Fig. A1 of the Web-Appendix) that these lead to probability weighting functions not unlike those estimated by Tversky and Kahneman (1992).

  17. In Fig. A4 of Web-Appendix, we also report frequency distributions for \(\Gamma ,\Gamma _{1}, \gamma _{2 }\)and \(\delta _{2}.\)

  18. While \(\alpha \) and \(\beta \) are not relevant to our hypotheses, we also ran regressions of these parameters on the same set of variables (both with and without the constraint that \(\gamma =\delta \)). The principal finding was that in the negative domain, males were closer to EUT in the sense that \(\beta \) was higher (i.e., typically closer to linearity).

  19. These are White’s test statistic for heteroscedasticity of unknown form, a redundant variable F test and White’s LM test for model misspecification.

  20. These two parameters do not quite cleanly decompose these two behavioral tendencies. See Fehr-Duda et al. (2006) for a discussion.

  21. This is the same percentage rejection as the \(\gamma =\delta \) test under P1.

  22. It should be noted that results for \(\gamma _{2}\) and \(\delta _{2 }\) will vary depending on whether or not the \(\gamma _{1} = \delta _{1 }\) condition is imposed.

  23. Results without 10 % filtering are broadly similar, but weaker (see Tables A6 and A7 of the Web-Appendix).

  24. See Web-Appendix for comparable tables (A14, A15). In the same Web-Appendix, Tables A11–A13 for GE are comparable to Tables A3–A5 for P2.

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Acknowledgments

An anonymous referee of this journal provided helpful comments, as did James Choi, Werner DeBondt, Mark Kamstra, Lisa Kramer, Andy Previtero, Kent Womack, and attendees at Queen’s University’s Annual Behavioral Finance Conference (2011) and National Taiwan University’s International Conference on Economics, Finance and Accounting (2011), along with workshops at Thammasat University of Thailand (2011) and National Cheng Chi University of Taiwan (2011). Remaining errors are our own responsibility.

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Authors and Affiliations

  1. McMaster University, Hamilton, ON, Canada

    Narat Charupat, Richard Deaves & Peter Miu

  2. Wikihow, Washington, DC, USA

    Travis Derouin

  3. PUC—Rio, Rio de Janeiro, Brazil

    Marcelo Klotzle

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  1. Narat Charupat
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Correspondence to Richard Deaves.

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Charupat, N., Deaves, R., Derouin, T. et al. Emotional balance and probability weighting. Theory Decis 75, 17–41 (2013). https://doi.org/10.1007/s11238-012-9348-x

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