Ido Erev, Technion and Warwick University
Based on recent research with
Alvin E.
Roth
Eyal
Ert
Kinneret
Teodorescu
Ori
Plonsky
1

Experimental studies of the impact of rare events on choice behavior reveal large differences between decisions that are made based on a description of the incentive structure (as in Kahneman & Tversky, 1979), and decisions that are made based on past experience.
People tend to overweight rare events in decisions from description , and exhibit the opposite bias in decisions from experience (Barron & Erev,
2003); experience leads people to behave as if they believe that it “won’t happen to me.”
The current session tries to clarify this ”experience-description gap”
(Hertwig & Erev, 2009), and its implications to medical decision making

The classical studies of human decision making focus of decisions from description : The subjects are presented with a description of pairs of hypothetical prospects, and are asked to choose one once.
For example, the participants in Kahneman & Tversky (1979) were faced with the following problems:
Observed R-rate Choose between:
S: 5 with certainty
R: 5000 with probability 1/1000, 0 otherwise (EV = 5) 72%
S: -5 with certainty
R: -5000 with probability 1/1000, 0 otherwise (EV = -5) 20%
Kahneman & Tversky (1979) noticed that this pattern is consistent with the observation that many people buy lottery and insurance, and captured it (in prospect theory) with a weighting function that implies overweighting of rare events.

The original goal of our study of decisions from experience was to examine the robustness of this overweighting pattern to experience.
We used the following paradigm
The clicking paradigm (Barron & Erev, 2003)
The current experiment includes many trials. Your task, in each trial, is to click on one of the two keys presented on the screen.
Each click will be followed by the presentation of the keys’ payoffs.
Your payoff for the trial is the payoff of the selected key.
0
1
You selected Right. Your payoff in this trial is 1
Had you selected Left, your payoff would be 0
4

The results surprised us. We predicted that experience will reduce the overweighting pattern (and increase maximization), and observed robust deviations from maximization in the opposite direction.
(choice rate of the non-zero option)
1,00
0,75
Problem 1: "-10 with p =0.1; +1 otherwise" (EV = -0.1) or 0
0,50
0,25
Problem 2: "+10 with p = 0.1, -1 otherwise" (EV = +0.1) or 0
0,00
1 2
3 4 5
5

Kahneman and Tversky’s (1979) classical study of decisions under risk reveals overweighting of rare events
R rate (overweighting)
S: 5 with certainty
R: 5000 with probability 1/1000, 0 otherwise (EV = 5)
S: -5 with certainty
R: -5000 with probability 1/1000, 0 otherwise (EV = -5)
72% (72%)
20% (80%)
The study of decisions from experience reveals the opposite pattern
S: 0 with certainty
R: 10 with probability 1/10, -1 otherwise (EV = +0.1)
S: 0 with certainty
R: -10 with probability 1/10, 1 otherwise (EV = -0.1)
27%
58%
(27%)
(42%)
6

The difference between the two studies presented above can be attributed to many factors:
Large hypothetical payoffs vs. small real payoffs
Different probabilities (1/1000 vs. 1/10)
One-shot vs. repeated play
Risk vs. uncertainty
The role of experience
Follow-up studies show that the last factor “the role of experience” is most important
Erev, Ert & Plonsky (2015): 5 blocks of 5 trials, full description, feedback after the 6 th choice
7

Example of the basic experimental task, trial 1, initial screen
Please select one of the following options:
A:
2 with certainty
A
B:
101 with p = 0.01
1 with p = 0.99
B

Example of the basic experimental task, trial 1, limited feedback
A:
2 with certainty
A
You selected B
B:
101 with p = 0.01
1 with p = 0.99
B

Example of the basic experimental task, trial 6, initial screen
Please select one of the following options:
A:
2 with certainty
A
B:
101 with p = 0.01
1 with p = 0.99
B

Example of the basic experimental task, trial 6, feedback screen
A:
2 with certainty
A
2
You selected B, your payoff is 1
Had you selected A your payoff would be 2
B:
101 with p = 0.01
1 with p = 0.99
B
1

Erev, Ert & Plonsky (2015): 5 blocks of 5 trials, full description, feedback after the 6 th choice
A
B
2 with certainty
101 with p= .01, 1 otherwise
Block 1 (No FB)
55%
5 (with FB)
42%
A
B
-1 with certainty
Some overweighting of rare events before feedback, and robust underweighting with feedback
0,75
0,50
0,25
0,00
1 2
48%
3
63%
4
Block
5

Another robust class of deviations from maximization is documented in studies that examine the effect of payoff variability
(choice rate of the non-zero option)
1
0,75
Problem 3: "+1 with certainty" or 0
Problem 5: "+11 with p =0.5, -9 otherwise" (EV = +1) or 0
0,5
Problem 6: "-11 with p = 0.5; +9 otherwise" (EV = -1) or 0
0,25
Problem 4 “-1 with certainty" or 0
0
1 2
4 5
The payoff variability effect (Busemeyer and Townsend, 1993; Diederich and Busemeyer, 1999).
13

The reliance on small sample explanation of the basic properties of decisions from experience (Hertwig et al., 2004, and see related observations in Kareev 2000; Fiedler, 2000)
Action (the alternative to the status quo)
(-10, 0.1; +1, 0.9)
EV
-0.1
Action-rate
60%
Prediction of a
‘sample of 5’ model
62%
0 1
0
0 1
1
0 1
0 1
A sample of size k will include the rare event with probability below .5 when
P(no rare) = (1-p) k <0.5. This inequality implies that k< Log(.5)/Log(1-p).
For example, when p = .1, when k < 6.57.

The reliance on small sample explanation of the basic properties of decisions from experience (Hertwig et al., 2004, and see related observations in Kareev 2000; Fiedler, 2000)
Action (the alternative to the status quo)
(-10, 0.1; +1, 0.9)
EV
-0.1
Action-rate
60%
Prediction of a
‘sample of 5’ model
62%
(+10, 0.1; -1,0.9) +0.1
27% 38%
+1 +1 with certainty
-1 with certainty
(+11, 0.5; -9, 0.5)
(+9, 0.5; -11, 0.5)
-1
+1
-1
95%
5%
53%
42%
99%
1%
50%
50%
15

Why small samples (Plonsky, Teodorescu & Erev, 2015)? Consider a variant of the clicking experiment in which each trial starts with a presentation of a color signal. What would you select in trial 16?
Pre-choice signal: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
-1 -1 -1 +2 -1 -1 -1 +2 -1 -1 -1 +2 -1 -1 -1
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Why small samples (Plonsky, Teoderescu & Erev, 2015)? Consider a variant of the clicking experiment in which each trial starts with a presentation of a color signal. What would you select in trial 16?
Pre-choice signal: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
-1 -1 -1 +2 -1 -1 -1 +2 -1 -1 -1 +2 -1 -1 -1
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Most subjects select Up, even without the color signals:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
-1 -1 -1 +2 -1 -1 -1 +2 -1 -1 -1 +2 -1 -1 -1
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Thus, they behave as if they use on 3 past experiences. And in trial 8 of the following example, many behave as if they use only 1 past experience:
1 2 3 4 5 6 7 8
-1 -1 -1 +2 -1 -1 -1
0 0 0 0 0 0 0
17

We believe that the experience-description gap reflects the existence of two classes of deviations from maximization (Marchiori, DiGuida & Erev, 2014)
First, initial decisions reflect overgeneralizations from past experiences in similar, but not identical situations.
Second, when people gain repeated experience in a given setting they rely on the most similar past experiences. This tendency increases maximization in dynamic environments, but when the environment is highly stochastic it implies reliance on small samples and underweighting of rare events.
Some of the implications of this summary of the results are presented below

(Erev & Rodensky, 2004)
• Enforcement is necessary
• Workers like enforcement programs (they plan to be careful)
• Probability of punishment is more important than magnitude
• When large punishments are too costly, gentle enforcement can be optimal
Implementation in 12
Factories:
Baseline measurement
Meeting with workers
Small fines by supervisors'
19

Washing hands and using gloves in hospitals
In 1847, Dr. Ignaz Semmelweis first demonstrated that routine hand-washing could prevent the spread of disease. In an experiment, Dr. Semmelweis insisted that his students staffing a Vienna hospital’s maternity ward wash their hands before treating the maternity patients--and deaths on the maternity ward fell
From near 10% to around 2%. Though his findings were published, there was no apparent increase in hand washing by doctors until the discoveries of
Louis Pasteur years after Dr. Semmelweis died in a mental asylum (Nuland,
2003).
Moreover, it is still wise to enforce rules that require safe behavior by doctors (Erev,
Rodensky, Levi, Englard-
Hershler, Admi & Donchin,
2010).
20

Buying insurance and lotteries
The observation that people buy both lotteries and insurance is naturally explained with the assumption that rare events are over-weighted. Thus, it appear to be inconsistent with the suggestion that experience leads to the opposite bias.
But how common is this “buying insurance & lotteries” observation?
Studies of gambling behavior show that many people buy lotteries from time to time, but most of the do not do that very often. For example, only 10% of the New Zealanders buy more than 6 lotteries per year (Amey, 2001).
Similarly, it is not clear that people buy enough insurance. It is often necessary to force them to buy one.

The impact of mandatory medical insurance:
Assuming that people maximizes expected utility, mandatory health insurance creates two types moral of hazard problems:
“ex-ante” moral hazards: taking more risks (e.g., reduction in preventive effort)
“ex-post” moral hazards, increasing demand for medical care.
Replacing the maximization assumption with the current “reliance on small samples assumption” suggests that the second ( ex-post ) class is likely to be most important. Insurance changes the common outcome of this behavior, and has little effect on the common outcome of reckless behavior
This hypothesis is consistent with empirical research (Zweifel and Manning,
2000): the evidence for ex-ante moral hazard is weak, while the evidence for ex-post moral hazard is much stronger.
The value of unpleasant treatments

The effect of the timing of warning (Barron, Leider & Stack, 2008)
Evaluation of the impact of warnings reveals a large effect of prior experience. Individuals who have had good experiences in the past are less affected by the warning. For example, when the FDA added a blackbox warning to the drug Cisapride, the data show an increase in usage of
2% among repeat users, but a decrease of 17% amongst first-time users
(Smalley, et. al., 2000). Another example is provided by a study of parentadolescent sexual communication. Regular condom use was found to be lower when parent-adolescent sexual communication occurred at a later age (Hutchinson, 2002).
Barron, Leider and Stack (2008) show that part of the effect of experience remains even after controlling for the available information. This part appears to be a reflection of the experience-description gap.
23

Summary:
People tend to exhibit oversensitivity to rare events when they rely on a description of the incentive structure, but experience reverses this bias.
The initial overweighting appears to reflect overgeneralization. The tendency to underweight rare events following experience can be a negative side effect of a sophisticated effort to response to dynamic environments.
We believe that the understanding of this experience-description gap can shed light on interesting medical decision making problems.
It suggests that it is not enough to give people clear information and avoid conflict of interests. In some situations it is necessary to change the incentive structure to reduce the negative effect of underweighting of rare events.