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Probability weighting function for experience-based decisions

Probability weighting function for experience-based decisions. Katarzyna Domurat Centre for Economic Psychology and Decision Sciences L. Kozminski Academy of Entrepreneurship and Management Warsaw, Poland. Prospect Theory.

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Probability weighting function for experience-based decisions

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  1. Probability weighting function for experience-based decisions Katarzyna Domurat Centre for Economic Psychology and Decision SciencesL. Kozminski Academy of Entrepreneurship and Management Warsaw, Poland

  2. Prospect Theory • when making decisions under risk people use decision weights in such a way that they overweight low probability events and underweight high probability events • supported in several experiments when people were provided with probabilities of potential outcomes (DD)

  3. Experience-based Decision (ED) • DM samples information about risky options (sample the payoff distributions) and then makes a choice Clicking paradigm

  4. In "experience-based" decisions (ED) people behave as if they underweight small probabilities [Hertwig et. al. (2004)] • Explanation: sampling error [Fox&Hadar (2006)] or something else?

  5. The goal of research • Estimate probability weighting function under experience condition without sampling error • The probability weighting function will be more linear for ED than for DD

  6. The experiment design • 54 two-outcome lotteries:  with six different pairs of outcomes: (150-0, 300-0, 600-0, 300-150, 450-150, 600-300) and nine levels of probability associated with maximum outcome in lottery: (0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.0, 0.95, 0.99) • 3 computerized sessions (about 20 gambles per session)

  7. The experiment design • Certainty equivalent (CE) method [Kahneman&Tversky, 1992; Wu&Gonzales, 1999] • LabSee program (labsee.boby.pl)

  8. First stage: sample a lottery (representive sample/without sampling error) 150 0

  9. Second stage: choosing CE for observed lottery  CE – approximated by the middle of final interval

  10. Estimation procedure • Standard parametric fit of the weighting function w(p) and the value function v(x) • Cumulative Prospect Theory: Nonlinear least square regression: CE-median certainty equivalent

  11. Estimation procedure • One functional form of v(x): • And four parametric specifications of w(p): (1) (3) (2) (4)

  12. Results • Estimations for two sets of median data: SET1 (N=15) and SET2 (N=7)

  13. Model 1:

  14. Model 2:

  15. Model 3:

  16. Model 4:

  17. Conclusions • The higher γ obtained under experience condition means that w(p) is more linear for ED than for DD • the effect of overweighting small probabilities is weaker • Greater sensitivity to changes in probability in ED

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