probability weighting function for experience-based decisions
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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. - PowerPoint PPT PresentationTRANSCRIPT
Probability weighting function for experience-based decisions
Katarzyna Domurat
Centre for Economic Psychology and Decision SciencesL. Kozminski Academy of Entrepreneurship and Management
Warsaw, Poland
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)
Experience-based Decision (ED)
• DM samples information about risky options (sample the payoff distributions) and then makes a choice
Clicking paradigm
• 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?
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
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)
• Certainty equivalent (CE) method [Kahneman&Tversky, 1992; Wu&Gonzales, 1999]
• LabSee program (labsee.boby.pl)
The experiment design
First stage: sample a lottery (representive sample/without sampling error)
150
0
Second stage: choosing CE for observed lottery
OutcomeX (PLN)
Prefer SureOutcome X
Prefer Lottery
150 ο
120 ο
90 ο
60 ο
30 ο
0 ο
OutcomeX (PLN)
Prefer SureOutcome X
PreferLottery
60 ο
54 ο
48 ο
42 ο
36 ο
30ο
CE – approximated by the middle of final interval
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
.0),())(1()()()( 2121 xxwherexvpwxvpwCEv
)).())(1()()(( 211 xvpwxvpwvCE
Estimation procedure
• One functional form of v(x):
• And four parametric specifications of w(p):
(1) (3)
(2) (4)
.0,)( xdlaxxv
1
])1([
)(
pp
ppw
)1()(
pp
ppw
))ln(exp()( ppw
))ln(exp()( ppw
Results
• Estimations for two sets of median data:SET1 (N=15) and SET2 (N=7)
1
])1([
)(
pp
ppw
Model 1:
)1()(
pp
ppw
Model 2:
))ln(exp()( ppw Model 3:
))ln(exp()( ppw Model 4:
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