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On cognitive science, behaviorism, and behavioral economics Ido Erev, Technion and Univ. of Warwick BF Skinner, one the founding fathers of behavioral psychology, was not impressed by the early research in behavioral economics (that he called the cognitive study of c hoice behavior).

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On cognitive science, behaviorism, and behavioral economics

Ido Erev, Technion and Univ. of Warwick

BF Skinner, one the founding fathers of behavioral

psychology, was not impressed by the early research in

behavioral economics (that he called the cognitive study of

choice behavior).

To him (Skinner, 1985) this research focuses on over-fitting situation-specific overgeneralizations, and then on overgeneralizing the over-fitted models.

The current class focuses on Skinner’s critique and its implications.

The first part is based on Skinner (1985)

and on an incomplete paper with Al Roth, and the economics of small decisions section in

The basic idea behind Skinner’s view is the assertion that

behavior is selected by the contingencies of reinforcements (COR). That is, we select the alternative that has led the best outcomes in similar situations in the past.

Skinner notes that selection by COR implies behavior that can be approximated by Savage’s model, but does not imply explicit understanding of probabilities and/or utilities. Correcting this approximation based on Allais-like studies is as wise as correcting the prediction of the outcomes fadeaway jumpers based on studies that focus on questions like:

Assume that you are 5 meters from the basket moving

In a 189 degrees, 1.3 meter per second, and have to throw

the ball in 45 degrees. Which speed would you prefer:

8 meter per second

7 meter per second

Yet, SEU is only an approximation, and not necessary a useful one. It is not so useful because it uses unobserved terms (subjective utilities and personal probabilities), and because we can expect deviations from maximization even if behavior is selected by the contingencies of reinforcements (COR).

It is important to distinguish between two classes of behavioral phenomena that can lead to deviations from maximization:

1. Overgeneralizations (framing and priming effects): using past experiences that only look similar to the current problem. These deviations tend to be situation-specific and slippery.

2. COR learning equilibria: Situations in which human learning is likely to lead to a steady state (at least in the intermediate term). In some cases these steady states differ from Nash equilibrium. Deviation from ex-post maximization is possible even under the best possible learning process.

Examples of overgeneralizations

OG1 Mental accounting (Skinner’s analysis of Kahneman & Tversky, 1981):

People report that they are less likely to buy a second ticket to the theatre if a first was lost than to buy a first ticket after losing the money that was set aside for that purpose.

K&T explain their result as a product of the psychological accounting.

Skinner notes that there is a simple and less

interesting explanation: overgeneralization from

the common experiences in which it is possible

to recover a lost ticket, and/or to convince others

that we did what we were suppose to do.

OG2. Loss aversion(Ert & Erev, 2013; Erev et al., 2013):
  • Cond 1: Would you accept a gamble that can lead to a gain or a loss?
  • Cond 2: Choose between:
  • Stay out of the game (the safer option)
  • Enter the game (risky option that leads to a gain or a loss)




The Willingness to Pay—Willingness to Accept Gap, the “Endowment Effect, mugs and lotteries (Plot & Zeiler vs. Isoni, Loomes & Sugden)

Misconceptions vs. the effect of the contingencies of reinforcements

OG3. The possibility/certainty effect (Marchiori et al., 2013)

Gambling rate

First trial: 54%

After trial 3: 33%

100 with p=.05

0 otherwise

5 with certainty

OG4. Checking and ambiguity aversion (Y. Roth & Erev)

Choose between:


First trial: 50%

After trial 3: 28%

Product A

+1 if E, -1 in notE

Product B

-1 if E, +1 in notE

Check (cost of.9)

0.1 with certainty

The difference between social psychology and mainstream behavioral economics

Social psychologists tend to use explicit manipulations of priming. For example, the subjects are asked to think on situation in which the were powerful (Galinsky et al., 2003), or are explicitly ask to perform a task that will affect the psychological distance (Trope & Liberman, 2003), or their regulatory focus (Higgins, 1987)

In contrast, in much of the research in behavioral economics these manipulations are implicit (they are part of the framing), and some of the authors appear to believe that the results reflect general propensities (e.g., utility and/or weighting functions).

Examples of COR learning equilibria

Punishment and inefficient equilibria(Skinner’s analysis of the negative effects of punishment, and see Denrell & March, 2001; Sobolev & Erev, in prep.).

Two agents: A teacher and a student.

The student has to select one of the following cells:

The numbers in the cells determine the teacher’s payoff. Only the teacher sees them. The payoff for the student (between -2 to +2) is determined by the teacher.

What would you do as a teacher after the first 0?

With shelters

Skinner’s effect on the use of punishments in school

Not always Nash equilibrium, and not always bad

Roth (2002) notes that it is rarely the case that one can use game theory to logically address economic design problems. For example the “Stable matching” mechanism that matches new MDs to hospitals, is not stable in the game theoretic sense (it is enough that one person will change his preferences between) to destroy stability. Similarly, auction designers use many intuitive tricks.

We believe (see Erev & Roth, 1998; in prep.) that this gap between pure game theory and mechanism design reflects effective use of COR learning equilibria.

For example, the medical job market matching is effective because the probability that a new MD that has changed his preferences will find a better match is sufficiently low. So accepting the match works when it is a COR learning equilibrium.

Implications to the enforcement of safety rules(Erev & Rodensky, 2004; Schurr et al., 2012;
  • Enforcement is necessary
  • Probability of punishment is more important than magnitude
  • When large punishments are too costly, gentle enforcement can be optimal

Implementation in 12




Meeting with workers

Small fines by supervisors

Cheating in exams (Erev, Ingram, Raz & Shani, 2010)

The herd effect. Many enforcement problems have 2 Nash equilibria:

Many violators (small probability of detection)

No Violations (high probability of detection).

To reach the No Violations equilibrium it is constructive to

start with high resources.

Thus, good teachers know that it is easy to get to the

no cheating equilibrium. It is enough to move the first people

That move their head to the first row.

Related ideas

A fine is a price (GneezyRustichini, 2000). The fact that money is important does not imply that it is the only reinforcement.

Green boxes, small brothers, Ariely and Dawes

Self checkouts (Retalix and Dan Ariely)

Reading instructions and contracts

Creativity, skill acquisition, and learned helplessness (with Kinneret Teodorescu)

The current logic suggests that insufficient creativity and skill acquisition emerge when the common outcome of trying new strategies is disappointing.

Reliance on small samples leads people to give up too early.

Emphasis change training (Gopher et al., 1989) is effective because it reduces this risk.

Law, regulations, freedom and privacy

Many laws and regulations appear to reflect sensitivity to the current results. For example, the law does not trust people ability to ensure that their car is safe. They are forced to do it once a year. Similarly, people are forced to buy certain insurance policies.

It is possible that more regulations of this type can help.

The cost of freedom and privacy is often larger than it seems.

Poverty, pollution, and intergroup conflicts (a possible extension of Hardin, 1968)

Relationship to mainstream economic analysis

Mainstream economic analyses are based on the assumption that during the design of new markets and/or regulations it is useful to ensure that the socially desirable behavior maximizes the agents’ expected utility.

Behavioral economists try to refine these analyses by adding terms to the utility function (loss aversion, inequality aversion).

We propose an alternative addition: We agree that it is good to ensure that the socially desirable behavior maximizes the agents’ expected return, and suggest that it will be even better if it leads to the best outcome most of the time(in similar situations).

Relationship to Nudge (Thaler & Sunstein, 2008)

One can use nudges to address the two classes of deviations from maximization discussed above (overgeneralization and COR learning equilibria).

In practice, the nudge research tends to focus overgeneralizations like defaults and choice architecture.

The current analysis suggests that paying more attention to COR learning equilibria can help.

  • Many natural decisions are made under severe ambiguity, and the decision makers have to rely on their experience. Selection by the contingencies of reinforcements (collaborative filtering) approximates the optimal strategy in these cases.
  • Two possible reasons for deviations from maximization:
  • Situation specific overgeneralizations
  • COR learning equilibria.
  • The massive study of overgeneralizations has led to important observations, but without models that clarify the boundary conditions it just adds overgeneralizations.
  • The early study of COR learning equilibria was highly important, but for some reason it was neglected. We believe that this topic should receive more attention.