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Behavioral patterns and reduction of sub-optimality: an experimental choice analysis

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Abstract

This paper attempts to identify behavioral patterns and compare their average success considering several criteria of bounded rationality. Experimentally observed choice behavior in various decision tasks is used to assess heterogeneity in how individual participants respond to 15 randomly ordered portfolio choices, each of which is experienced twice. Treatments differ in (not) granting probability information and in (not) eliciting aspirations. Since in our setting neither other regarding concerns nor risk attitude matter and probability of the binary chance move is (optimal) choice irrelevant, categorizing decision types relies on parameter dependence and choice adaptations. We find that most participants reduce systematically sub-optimality when following the identified criteria.

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Notes

  1. For an analysis of the concepts of aspiration and satisficing, see the seminal contributions of Simon (1955), Siegel (1957) and Manski (2017) Sauermann and Selten (1962) and Selten (1998) for the adaptation theory (AAT). For experimental analyses, see Selten et al. (2012) and Hey et al. (2017).

  2. To avoid other relevant concerns, see Harrison and Johnson (2006).

  3. This induces risk neutrality in decision-making (see Roth and Malouf 1979, for an early use in bargaining).

  4. For a critical assessment, see Selten et al. (1999) which, however, questions expected utility theory, not binary lottery incentives. Rejecting binary lottery incentives but maintaining expected utility theory is impossible.

  5. Di Cagno et al. (2017) also analyze three control cases with \(c=0\).

  6. The probability information is the only difference between T1 and T2.

  7. In the experimental setting of T3 and T4 the aspiration level is set on the €14 bar probabilities and we avoid confusion with the second (complementary) bar.

  8. Paying participants only for a random round is required by binary-lottery incentives and avoids past-earnings effects.

  9. In a similar manner, one could check slider positions for \(i_{t^*-\tau }\le {i^*}\) with \(\tau =1,2\) to determine whether they return to the range \([0,i^*]\) via \(i_{t^*}\le {i^*}\); however, such an analysis is omitted because only too few data are available.

  10. We adopt the \(60\%\) individual level of compliance per phase.

  11. The reason for distinguishing phases 1 and 2 in Table 10 is that the categorization of the eight behavioral categories is independently performed for phase 1 and phase 2 data, i.e., an individual participant may belong in phase 2 to a different class than in phase 1.

  12. Table 14 (in the Appendix A) additionally controls for whether participants switch categories from phase 1 to 2. Again, the dominant categories are those with (yes, yes, yes)-participants in both phases (41 in T1 and 31 in T2), whose average non-improving score in phase 2 is 2.68 in T1 and 2.74 in T2, i.e. slightly lower than the respective phase 2-score in Table  10.

  13. An exception is phase 1 of T2 and the case (no, yes, yes), whose non-improving frequency score is 3.30 and thus smaller than the score of 3.46 for (yes, yes, yes).

  14. Exceptions exist is phase 1 of T4, e.g. case (no, yes, yes) with only 7 participants whose non-improving score is 5.43, less than 6.00 for (yes, yes, yes).

  15. Thus, we obviously cannot refer separately to phases 1 and 2 and note the greater success in phase 2 than in phase 1.

  16. In the following analysis, as in the previous, compliance with a selected criterion is calculated based on 60% average compliance.

  17. We admit that our method of incentivizing aspiration formation by letting participants lose all chances of earning more €14, rather than only €4 is partly responsible for the striking confirmation of satisficing, at least in phase 2 of treatments T3 and T4.

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Authors and Affiliations

  1. Luiss, Rome, Italy

    Daniela Di Cagno, Arianna Galliera & Werner Güth

  2. Max Planck Institute, Bonn, Germany

    Werner Güth

  3. Ca’ Foscari University of Venice, Venice, Italy

    Noemi Pace

  4. Politecnico di Milano, Milan, Italy

    Arianna Galliera

Authors
  1. Daniela Di Cagno
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  2. Arianna Galliera
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  3. Werner Güth
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  4. Noemi Pace
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Corresponding author

Correspondence to Daniela Di Cagno.

Additional information

The research presented in this paper was financed by the Max Planck Institute for Research on Collective Goods of Bonn.

Appendix

Appendix

See Fig. 4 and Tables 13, 14.

Fig. 4
figure 4

Investment choice in T3 and T4

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Table 13 Summary results regarding slider adjustments
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Table 14 Non-improving frequency by improvement criteria in phases 1 and 2
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Di Cagno, D., Galliera, A., Güth, W. et al. Behavioral patterns and reduction of sub-optimality: an experimental choice analysis. Theory Decis 85, 151–177 (2018). https://doi.org/10.1007/s11238-018-9653-0

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