Heuristics and Errors in Probability Estimation (Reference:
Wilkinson and Klaes, An Introduction to Behavioral Economics, Page 117-124,
Palgrave Macmillan, 2nd Edition, 2012.)

Teaching note By
Munish Alagh.

In the standard model of microeconomics we examine how
people form attitudes, values, preferences and finally make choices. Various
assumptions are made regarding the options and outcomes of these options in the
decision making process. As far as beliefs are concerned, a main assumptions in
the standard model is that they are Bayesian Probability Estimators.

Bayesian Probability Estimators: This means that people are
able to estimate probabilities correctly, given the relevant information, and
in particular are able to update them correctly given a sequence of prior
outcomes

Specifically, Bayes Theorem updates or modifies
probabilitys, given new pieces of evidence ( Refer to Wilkinson and Klaes for
full mathematical statement of the theorem.)

Probability Estimation

The types of deviation described in this section refer to
rational Bayesian updating.

The availability heuristic:salience, or people believe
events are more probable if examples are more easy to remember.

Representativeness heuristic:

People evaluate likelihood of a subject belonging to a
particular category, based on the degree to which the subject resembles a
typical item in that category.For eg (Refer Wilkinson and Klaes for ‘Linda is a
Bank Teller” example where strong representativeness overcomes the feature
thatProbability of two events can never be higher than a single one.)

Base Rate Bias-People tend to ignore the base rate, for eg:
however high the odds and despite error being 95%, a positive result of a test
is taken as a confirmation of disease.

The law of small numbers: principles that apply to infinite
populations are assumed to apply to small samples.

Gamblers fallacy: gamblers frequently expect a certain slot
machine or a number that has not won in a while to be ‘due” to win: Caused due
to Misapplication of the assumption of non-replacement.

Hot hand effect: The effect derives its name from the
mistaken belief among basketball players that a players chance of hitting a
shot is greater following a hit than following a miss on the previous shot.
Although it appears that this
“overinference” is the opposite of the gamblers fallacy, it is actually a
complementary effect, again involving a misapplication of the assumption of
non-replacement.

Synthesis: The law of small numbers could lead to both hot
hand and gamblers fallacy, causing both underreaction and over reaction to
market signals, in the short term investors follow the gamblers fallacy,
however after a longer sequence, investors overinfer.

In terms of a lottery situation, the law of small numbers
could lead to people exhibiting a gamblers fallacy and expecting winning
numbers to not repeat, however with regard to stores there may be a human
element with regard to how stores are selected leading to a hot hand.

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