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(Sub) Optimality and (Non) Optimal Satisficing in Risky Decision Experiments

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Listed:
  • Daniela Di Cagno
  • Arianna Galliera
  • Werner Güth
  • Francesca Marzo
  • Noemi Pace

Abstract

A risky choice experiment is based on one-dimensional choice variables and risk neutrality induced via binary lottery incentives. Each participant confronts many parameter constellations with varying optimal payoffs. We assess (sub)optimality, as well as (non)optimal satisficing, partly by eliciting aspirations in addition to choices. Treatments differ in the probability that a binary random event, which are payoff- but not optimal choice–relevant, is experimentally induced and whether participants choose portfolios directly or via satisficing, i.e., by forming aspirations and checking for satisficing before making their choice. By incentivizing aspiration formation, we can test satisficing, and in cases of satisficing, determine whether it is optimal.

Suggested Citation

  • Daniela Di Cagno & Arianna Galliera & Werner Güth & Francesca Marzo & Noemi Pace, 2016. "(Sub) Optimality and (Non) Optimal Satisficing in Risky Decision Experiments," Working Papers CESARE 3/2016, Dipartimento di Economia e Finanza, LUISS Guido Carli.
    • Handle: RePEc:lui:cesare:1603
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    References listed on IDEAS

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    1. Daniela Cagno & Arianna Galliera & Werner Güth & Noemi Pace, 2018. "Behavioral patterns and reduction of sub-optimality: an experimental choice analysis," Theory and Decision, Springer, vol. 85(2), pages 151-177, August.
    2. Daniela Di Cagno & Werner Güth & Noemi Pace, 2021. "Experimental evidence of behavioral improvement by learning and intermediate advice," Theory and Decision, Springer, vol. 91(2), pages 173-187, September.

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    More about this item

    Keywords

    (un)Bounded Rationality; Satisficing; Risk; Uncertainty; Experiments;
    All these keywords.

    JEL classification:

    • D03 - Microeconomics - - General - - - Behavioral Microeconomics: Underlying Principles
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior

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