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Competence effects for choices involving gains and losses

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Abstract

We investigate how choices for uncertain gain and loss prospects are affected by the decision maker’s perceived level of knowledge about the underlying domain of uncertainty. Specifically, we test whether Heath and Tversky’s (J Risk Uncertain 4:5–28, 1991) competence hypothesis extends from gains to losses. We predict that the commonly-observed preference for high knowledge over low knowledge prospects for gains reverses for losses. We employ an empirical setup in which participants make hypothetical choices between gain or loss prospects in which the outcome depends on whether a high or low knowledge event occurs. We infer decision weighting functions for high and low knowledge events from choices using a representative agent preference model. For gains, we replicate the results of Kilka and Weber (Manage Sci 47:1712–1726, 2001), finding that decision makers are more attracted to choices that they feel more knowledgeable about. However, for losses, we find limited support for our extension of the competence effect.

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Notes

  1. Machina (2009) presented a four-color generalization of the Ellsberg Paradox that violates expected utility, as well as Choquet expected utility. Baillon et al. (forthcoming) showed that this counter-example violates most existing models of ambiguity as well.

  2. Wu and Gonzalez (1999) showed that the weighting function satisfies a stronger property for gains, concavity for low probability events and convexity for high probability events.

  3. Support theory allows nonadditive probability judgments, P(A ∪ A′) ≤ P(A) + P(A′). There are other hypotheses besides binary complementarity that follow from support theory, such as proportionality, product rule, and unpacking principle. However, binary complementarity is the only one that our design permits us to test.

  4. Full instructions and program code are available upon request.

  5. The same basic findings discussed below hold if we use different criteria to distinguish between low and high knowledge domains.

  6. We also computed Pearson statistics and found similar results.

  7. We also produced similar results using Prelec’s (1998) two-parameter function.

  8. We also performed an additional estimation, restricting the sample of the observations in which the reported knowledge difference exceeded 3, leaving us with 468 of 1,284 observation. This analysis produced qualitatively similar results (Gains: Δi ≈ 0.50, p < 0.001; Losses: Δi ≈ − 0.05, n.s.).

  9. We also compared expected utility to our more general model. The expected utility restriction was conducted by setting all parameters equal to 1. A likelihood ratio test strongly rejected the expected utility model for both gains and losses.

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Acknowledgements

We thank Hugo Sonnenschein and Lars Hansen for their help and Peter Wakker for his useful comments. Special thanks goes to Heleno Pioner. José Guilherme de Lara Resende gratefully acknowledges financial support from the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), Ministry of Education, Brasil, and from the Haddad fellowship, University of Chicago.

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  1. Departamento de Economia—Campus Universitário Darcy Ribeiro, Universidade de Brasília, ICC Ala Norte Caixa Postal 04587, Brasília, DF, 70919-970, Brazil

    José Guilherme de Lara Resende

  2. Booth School of Business, University of Chicago, 5807 S. Woodlawn Avenue, Chicago, IL, 60637, USA

    George Wu

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de Lara Resende, J.G., Wu, G. Competence effects for choices involving gains and losses. J Risk Uncertain 40, 109–132 (2010). https://doi.org/10.1007/s11166-010-9089-6

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