Judgment under Uncertainty: Heuristics and Biases
- Amos Tversky and Daniel Kahneman
Many decisions are based on beliefs concerning the likelihood of uncertain
events. These beliefs are usually expressed in statements but they may also be
expressed in numerical form.
Judgements are all based on data of limited validity, which are processed
according to heuristic rules. Heuristics are an approach to problem solving,
learning, or discovery that employs a practical method sufficient for the
immediate goals.
People do not make rational judgements, but their decisions are based on
heuristics.
→In this case rationality is defined as a correct probabilistic reasoning.
Three heuristics that are employed to asses probabilities and to predict values
are:
Representativeness
Availability
Anchoring
These heuristics are highly economical and usually effective, but they lead to
systematic and predictable errors.
Heuristics such as representativeness and availability are retained even though
they occasionally lead to errors in prediction or estimation. People usually do not
detect the biases in their judgement of probability.
Representativeness
In the representativeness heuristic probabilities are evaluated by the degree to
which A is representative of B (by the degree to which A resembles B).
Explanation: When A is highly representative of B, the probability that A
originates from B is judged higher. Whereas, if A is not similar to B, the
probability that A originates from B is judged low.
Example: When there is a description of a person (e.g. Steve) and you are
asked what the occupation of Steve is, you assess the probability that Bob has a
certain job based on this description. The probability that Steve is a librarian is
assessed by the degree to which his description is similar to the stereotype of a
librarian.
Possible fallacy - Misconception of chance:
- Amos Tversky and Daniel Kahneman
Many decisions are based on beliefs concerning the likelihood of uncertain
events. These beliefs are usually expressed in statements but they may also be
expressed in numerical form.
Judgements are all based on data of limited validity, which are processed
according to heuristic rules. Heuristics are an approach to problem solving,
learning, or discovery that employs a practical method sufficient for the
immediate goals.
People do not make rational judgements, but their decisions are based on
heuristics.
→In this case rationality is defined as a correct probabilistic reasoning.
Three heuristics that are employed to asses probabilities and to predict values
are:
Representativeness
Availability
Anchoring
These heuristics are highly economical and usually effective, but they lead to
systematic and predictable errors.
Heuristics such as representativeness and availability are retained even though
they occasionally lead to errors in prediction or estimation. People usually do not
detect the biases in their judgement of probability.
Representativeness
In the representativeness heuristic probabilities are evaluated by the degree to
which A is representative of B (by the degree to which A resembles B).
Explanation: When A is highly representative of B, the probability that A
originates from B is judged higher. Whereas, if A is not similar to B, the
probability that A originates from B is judged low.
Example: When there is a description of a person (e.g. Steve) and you are
asked what the occupation of Steve is, you assess the probability that Bob has a
certain job based on this description. The probability that Steve is a librarian is
assessed by the degree to which his description is similar to the stereotype of a
librarian.
Possible fallacy - Misconception of chance:
