
Motivation and the basic idea
Experimental studies of human choice behavior have documented clear
violations of rational economic theory and triggered the development
of behavioral economics. Yet, the impact of these careful studies on
applied economic analyses, and policy decisions, is not large. One
justification for the tendency to ignore the experimental evidence
involves the assertion that the behavioral literature highlights
contradicting deviations from maximization, and it is not easy to
predict which deviation is likely to be more important in specific
situations.
To address this problem Kahneman and Tversky (1979) proposed a model
(Prospect theory) that captures the joint effect of four of the most
important deviations from maximization: the certainty effect (Allais
paradox, Allais, 1953), the reflection effect, overweighting of low probability extreme
events, and loss aversion (see top four rows in Table 1). The current paper extends this and similar efforts (see e.g., Thaler & Johnson, 1990; Brandstätter, Gigerenzer, & Hertwig, 2006; Birnbaum, 2008; Wakker, 2010; Erev et al., 2010) by facilitating the derivation and comparison of models that capture the joint impact of the four "prospect theory effects" and ten additional phenomena (see Table
1).
These choice phenomena were replicated under one "standard" setting
(Hertwig & Ortmann, 2001): choice with real stakes in a space of
experimental tasks wide enough to replicate all the phenomena illustrated in Table 1. The
results suggest that all 14 phenomena emerge in our setting. Yet,
their magnitude tends to be smaller than their magnitude
in the original demonstrations.
The current choice prediction competition focuses on developing models
that can capture all of these phenomena but also predict behavior in
other choice problems. To calibrate the models we ran an “estimation
set” study that included 60, randomly selected, choice problems.
The original description of the competition that was available when the competition was announced can be found here: Initial_Description_19.11.2014
For the paper that summarizes the competition, forthcoming in Psychological Review, click here:
DEcompPaper_22.12.2016
And now the movie: a video featuring Ido's talk on the competition is available here CPC talk Jan 16 2017
Table 1. Typical Examples of Fourteen Phenomena and Their Replications in the
Current Study



Phenomenon

Problems

%B Choice

Problems

%B Choice

Description Phenomena

1. Certainty effect/Allais paradox (Kahneman & Tversky, 1979; following Allais,
1953)


A: 3000 with certainty
B: 4000, .8; 0 otherwise
A’: 3000, .25; 0 otherwise
B’: 4000, .20; 0 otherwise

20%
65%

A: 3 with certainty
B: 4, .8; 0 otherwise
Aʹ: 3, .25; 0 otherwise
Bʹ: 4, .20; 0 otherwise

42%
61%

2. Reflection effect (Kahneman & Tversky, 1979)


A: 3000 with certainty
B: 4000, .8; 0 otherwise
A’: 3000 with certainty
B’: 4000, .8; 0 otherwise

20%
92%

A: 3 with certainty
B: 4, .8; 0 otherwise
Aʹ: 3 with certainty
Bʹ: 4, .8; 0 otherwise

42%
49%

3. Overweighting of rare events (Kahneman & Tversky, 1979)


A: 5 with certainty
B: 5000, .001; 0 otherwise

72%

A: 2 with certainty
B: 101, .01; 1 otherwise

55%

4. Loss aversion (Ert & Erev, 2013; following Kahneman & Tversky, 1979)


A: 0 with certainty
B: 100, .5; 100 otherwise

22%

A: 0 with certainty
B: 50, .5; 50 otherwise

34%

5. Low magnitude eliminates loss aversion (Ert & Erev, 2013)


A: 0 with certainty
B: 10, .5; 10 otherwise

48%

A: 0 with certainty
B: 1, .5; 1 otherwise

49%

6. St. Petersburg paradox/risk aversion (Bernoulli, 1738/1954)


A fair coin will be flipped until it comes up heads. The number of flips will be
denoted by the letter k. The casino pays a gambler 2^{k}. What is the maximal
amount of money that you are willing to pay for playing this game?

Modal response: less than 8

A: 9 with certainty
B: 2, 1/2; 4, 1/4; 8; 1/8; 16, 1/16; 32, 1/32; 64, 1/64; 128, 1/128; 256 otherwise

38%

7. Ellsberg paradox/ Ambiguity aversion (Einhorn & Hogarth, 1986; following
Ellsberg, 1961)


Urn K includes 50 Red, and 50 White balls. Urn U includes 100 balls, each either Red
or White with unknown proportions. Choose between:
A: 100 if a ball drawn from K is Red; 0 otherwise
B: 100 if a ball drawn from U is Red; 0 otherwise
C: Indifference

47%
19%
34%

A: 10 with probability .5;
0 otherwise
B: 10 with probability ‘p’; 0 otherwise (‘p’ unknown constant)

37%

8. Break even effect (Thaler & Johnson, 1990)


A: 2.25 with certainty
B: 4.50, .5; 0 otherwise
A': 7.50 with certainty
B': 5.25 .5; 9.75 otherwise

87%
77%

A: 1 with certainty
B: 2, .5; 0 otherwise
Aʹ: 2 with certainty
Bʹ: 3 .5; 1 otherwise

58%
48%

9. Get something effect (Ert & Erev, 2013, following Payne, 2005)


A: 11, .5; 3 otherwise
B: 13, .5; 0 otherwise
A’: 12, .5; 4 otherwise
B’: 14, .5; 1 otherwise

21%
38%

A: 1 with certainty
B: 2, .5; 0 otherwise
Aʹ: 2 with certainty
Bʹ: 3 .5; 1 otherwise

35%

10. Splitting effect (Birnbaum, 2008)


A: 96; .90; 14, .05; 12 .05
B: 96; .85; 90, .05; 12, .10

73%

A: 16 with certainty
B: 1, .6; 50, .4
Aʹ: 16 with certainty
Bʹ: 1, .6; 44, .1; 48, .1; 50, .2

49.9%
50.4%

Experience Phenomena

11. Underweighting of rare events (Barron & Erev, 2003)


A: 3 with certainty
B: 32, .1; 0 otherwise
A’: 3 with certainty
B’: 32, .1; 0 otherwise

32%
61%

A: 1 with certainty
B’: 20, .05; 0 otherwise
Aʹ: 1 with certainty
Bʹ: 20 .05; 0 otherwise
Aʺ: 2 with certainty
Bʺ: 101, .01; 0 otherwise

29%
64%
42%

12. Reversed reflection (Barron & Erev, 2003)


A: 3 with certainty
B: 4, .8; 0 otherwise
A’: 3 with certainty
B’: 4, .8; 0 otherwise

63%
40%

A: 3 with certainty
B: 4, .8; 0 otherwise
Aʹ: 3 with certainty
Bʹ: 4, .8; 0 otherwise

65%
36%

13. Payoff variability effect (Erev & Haruvy, 2010; following Busemeyer & Townsend, 1993)


A: 0 with certainty
B: 1 with certainty
A’: 0 with certainty
B’: 9, .5; 11 otherwise

96%
58%

A: 2 with certainty
B: 3 with certainty
A': 6 if E; 0 otherwise
B': 9 if not E; 0 otherwise

100%
84%

14. Correlation effect (Grosskopf et al., 2006; following Diederich & Busemeyer, 1999)


A: 150+N_{1} if E; 50+N_{1} otherwise
B: 160+N_{2} if E'; 70 +N_{2} otherwise
A': 150+N_{1} if E; 50+N_{1} otherwise
B': 160+N_{2} if E; 60 +N_{2} otherwise
N_{i} ~ N(0,20), P(E) = P(E') = .5

82%
98%

A: 6 if E; 0 otherwise
B: 9 if not E; 0 otherwise
A’: 6 if E; 0 otherwise
B’: 8 if E; 0 otherwise
P(E) =0.5

84%
98%

The basic task:
The participants in each competition will be allowed to study the results
of the estimation set. Their goal will be to develop a model that
will predict the results of the competition set. In order to qualify
to the competition, the model will have to capture all 14 choice phenomena
of Table 1. The model should be implemented in a computer program that
reads the parameters of the problems as an input and predicts the
proportion of choices of Option B as an output. Thus, we use the generalization
criterion methodology (see Busemeyer & Wang, 2000).
Additional information concerning the competition can be found in the
following pages:
 The “registration” and submission page explains the actions that have
to be taken in order to participate in the competition.
 The “problem selection algorithm” page presents the algorithm that
was used to select the problems in the estimation set, and is used to
select the problems in the competition set.
 The “Method” page presents the experimental method.
 The “aggregated results” page presents the problems that were
studied in the estimation set and the aggregated results.
 The “raw data” pages present the raw data for each of the participants.
 The “competition rules” page explains the time schedule, and the
required features of the submissions.
 The “baseline model” page presents examples of possible submissions
to the competition.
GOOD LUCK!
