Content:
- JUDGEMENT UNDER UNCERTAINTY: HEURISTICS AND BIASES
- A DUAL-SYSTEM FRAMEWORK TO UNDERSTAND PREFERENCE CONSTRUCTION PROCESSES IN CHOICE
- A PERSPECTIVE ON JUDGMENT AND CHOICE MAPPING BOUNDED RATIONALITY
- CHOICES, VALUES, AND FRAMES
- HARNESSING THE SCIENCE OF PERSUASION
- THE USES ...
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INFLUENCING PEOPLE MINOR
2021
JUDGEMENT UNDER UNCERTAINTY:
HEURISTICS AND BIASES
Tversky & Kahneman
Many decisions are based on beliefs concerning the likelihood of uncertain events.
● People rely on a limited number of heuristic principles which reduce the complex
tasks of assessing probabilities and predicting values to simpler judgmental
operations.
○ Heuristics are useful but sometimes lead to severe and systematic errors.
○ Judgements are all based on data of limited validity, which are processed
according to heuristic values.
3 heuristics that are employed to assess probabilities and to predict values:
Representativeness → the representativeness heuristic
Probabilities are evaluated by the degree to which A is representative of B. Thus, by the
degree to which A resembles B.
This leads to serious errors: similarity is not influenced by several factors that should affect
judgements of probability:
★ Insensitivity to prior probability of outcomes: one of the factors that have no effect on
representativeness but should have a major effect on probability is the prior
probability, or base-rate frequency, of the outcomes.
○ If probability is evaluated by representativeness, prior probabilities will be
neglected.
○ Bayes’ rule → ratio of the odds (0.7/0.3)2 or 5.44 for each description.
■ Sharp violation of Bayes’ rule → subjects in the two conditions
produced essentially the same judgements.
○ When no specific evidence is given, prior probabilities are properly utilized;
when worthless evidence is given, prior probabilities are ignored.
★ Insensitivity to the sample size: to evaluate the probability of obtaining a particular
result in a sample drawn from a specified population, people typically apply the
representativeness heuristic.
○ The similarity of a sample statistic to a population parameter does not depend
on the size of the sample. Consequently, if probabilities are assessed by
representativeness, then the judged probability of a sample statistic will be
independent of sample size.
■ Insensitivity to sample size has been reported in judgments of
posterior probability (= probability that a sample has been drawn from
one population rather than from another).
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, ■ Intuitive estimates of posterior odds are far less extreme than the
correct values. The underestimation of the impact of evidence =
“conservatism”.
★ Misconceptions of chance: people expect that a sequence of events generated by a
random process will represent the essential characteristic of that process even when
the sequence is short.
○ People expect that the essential characteristics of the process will be
represented, not only globally in the entire sequence, but also locally in each
of its parts.
■ A locally representative sequence deviates systematically from
chance expectation: it contains too many alternations and too few
runs.
● Another consequence of the belief in local representativeness
is the well-known gambler’s fallacy.
■ Chance is commonly viewed as a self-correcting process in which a
deviation in one direction induces a deviation in the opposite direction
to restore the equilibrium. In fact, deviations are not “corrected” as a
chance process unfolds, they are diluted.
■ “Law of small numbers” = even small samples are highly
representative of the populations from which they are drawn
(misconception!)
■ This bias leads to the selection of samples of inadequate size and to
overinterpretation of findings.
★ Insensitivity to predictability: The degree to which the description is favorable is
unaffected by the reliability of that description or by the degree to which it permits
accurate prediction.
○ Hence, if people predict solely in terms of the favorableness of the
description, their predictions will be insensitive to the reliability of the evidence
and to the expected accuracy of the prediction.
○ This mode of judgment violates the normative statistical theory in which the
extremeness and the range of predictions are controlled by considerations of
predictability.
■ When predictability is nil, the same prediction should be made in all
cases.
■ The higher the predictability, the wider the range of predicted values.
● Intuitive predictions violate this rule; subjects show no regard
for considerations of predictability.
★ The illusion of validity: the unwarranted confidence which is produced by a good fit
between the predicted outcome and the input information may be called the illusion
of validity. This illusion persists even when the judge is aware of the factors that limit
the accuracy of his predictions.
○ The internal consistency of a pattern of inputs is a major determinant of one’s
confidence in predictions based on these inputs.
○ Highly consistent patterns are most often observed when the input variables
are highly redundant or correlated.
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, ■ Hence, people tend to have great confidence in predictions based on
redundant input variables.
● Redundancy among inputs decreases accuracy even as it
increases confidence, and people are often confident in
predictions that are quite likely to be off the mark.
★ Misconceptions of regression: consider two variables X and Y which have the same
distribution. If one selects individuals whose average X score deviates from the mean
of X bij k units, then the average of their Y scores will usually deviate from the mean
of Y by less than k units.
○ These observations illustrate a general phenomenon known as regression
toward the mean.
○ People do not develop correct intuitions about this phenomenon
■ They do not expect regression in many contexts where it is bound to
occur.
■ When they recognize the occurrence of regression, they often invent
spurious causal explanations for it.
○ The phenomenon of regression remains elusive because it is incompatible
with the belief that the predicted outcome should be maximally representative
as the input, and, hence, that the value of the outcome variable should be as
extreme as the value of the input variable.
Availability → the availability heuristic
There are situations in which people assess the frequency of a class or the probability of an
event by the ease with which instances or occurrences can be brought to mind = availability.
Availability is a useful clue for assessing frequency or probability, because instances of large
classes are usually recalled better and faster than instances of less frequent classes.
However, availability is affected by factors other than frequency and probability.
Consequently, the reliance on availability leads to predictable biases:
★ Biases due to the retrievability of instances: when the size of a class is judged by the
availability of its instances, a class whose instances are easily retrieved will appear
more numerous than a class of equal frequency whose instances are less
retrievable.
○ In addition to familiarity, there are other factors, such as salience, which affect
the retrievability of instances.
○ Recent occurrences are likely to be relatively more available than earlier
occurrences.
★ Biases due to the effectiveness of a search set: some things/words/concepts are
more easily recalled and are therefore judged as more numerous.
○ Different tasks elicit different search sets.
★ Biases of imaginability: sometimes one has to assess the frequency of a class whose
instances are not stored in memory but can be generated according to a given rule.
One typically generates several instances and evaluates frequency or probability by
the ease with which the relevant instances can be constructed. The ease of
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, constructing instances does not always reflect their actual frequency, and this mode
of evaluation is prone to biases.
○ Imaginability plays an important role in evaluation of probabilities in real-life
situations.
★ Illusory correlations: [Chapman & Chapman] bias in judgment of the frequency with
which two events co-occur. The illusory correlation effect was extremely resistant to
contradictory data.
○ It persisted even when the correlation between symptom and diagnosis was
actually negative, and it prevented the judges from detecting relationships
that were in fact present.
○ Availability provides a natural account for the illusory-correlation effect. The
judgment of how frequently two events co-occur could be based on the
strength of the associative bond between them.
■ When the association is strong, one is likely to conclude that the
events have been frequently paired. Consequently, strong associates
will be judged to have occurred together frequently.
○ In general, instances of large classes are recalled better and faster than
instances of less frequent classes; that likely occurrences are easier to
imagine than unlikely ones; and that the associative connections between
events are strengthened when the events frequently co-occur.
■ As a result, man has at his disposal a procedure (availability heuristic)
for estimating the numerosity of a class, the likelihood of an event, or
the frequency of co-occurrences, by the ease with which the relevant
mental operations of retrieval, construction, or association can be
performed.
■ This valuable estimation procedure results in systematic errors.
Adjustment and Anchoring
People make estimates by starting from an initial value that is adjusted to yield the final
answer.
The initial value (starting point) may be suggested by the formulation of the problem, or it
may be the result of a partial computation. Different starting points yield different estimates,
which are biased toward the initial values. = Anchoring.
★ Insufficient adjustment: anchoring occurs not only when the starting point is given to
the subject but also when the subject bases his estimate on the result of some
incomplete computation.
○ Because adjustments are typically insufficient, this procedure should lead to
underestimation.
★ Biases in the evaluation of conjunctive and disjunctive events: People tend to
overestimate the probability of conjunctive events and underestimate the probability
of disjunctive events.
○ These biases are readily explained as effects of anchoring. The stated
probability of the elementary event (success at any one stage) provides a
natural starting point for the estimation of the probabilities of both conjunctive
and disjunctive events.
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