Lecture 3 - Judgement Flashcards
What is the Bayesian inference formula
not necessasry
What is Bayesian inference
not necessasry
“A form of statistical inference in which initial beliefs (prior probabilities) are modified by evidence or experience to produce posterior probabilities.”
Eysenck & Keane textbook
What are the two approaches for making judgements
Heuristics & Biases Approach
Frequentist Approach
What are heuristics
Heuristics are mental shortcuts ppl use to make decisions quickly and with minimal cognitive effort
What are biases
Biases are systematic errors in judgment that occur when heuristics lead to incorrect conclusions
What is the conjunction fallacy
Tversky & Kahneman (1983)
The mistaken assumption that the probability of two events occurring (a conjunction) is greater than the probability of one of them
What is the representativeness heuristic
Why do people make the conjunction heuristic
Judging the probability of the likelihood of an event by comparing it to a mental image of what a person considers typical (representative) for that category
What does the representativeness heuristic explain
Base Rate Fallacy:
Ignoring the actual frequency of events
Stereotyping:
Making assumptions based on how well they fit a category
What is the availability heuristic
The easier it is to bring an event to mind,
the more likely that event is judged to be
(is it more likely for words to begin with ‘r’ or have their 3rd letter be ‘r’)
Tversky & Kahneman (1974)
What did Lichtenstein et al. (1978) find regarding availability heuristic
Asked pps to judge which was the most likely of two causes of death
58% of student sample said tornado > likely (asthma is 20x likely)
What did Coombs & Slovic (1979) find
Newspaper over-represented more ‘dramatic’ death causes
Ppl’s risk judgements were related to freq of media coverage
Availability heuristic
Clark & Teasdale (1985)
+ve & -ve memories recalled > in appropriate mood
(depression patients)
availability
What is support theory
Tversky and Koehler (1994)
Event appears > or < likely depending on how it’s described
built on availability heuristic
Support theory study
%? You will die summer hols
Same Q (w/ examples)
2nd one was rated more probable
2 reasons for support theory
1Explicit description draws attention to aspects of event less obvious in non-explicit description
2.Mem limits prevents ppl from remembering all relevant info if not supplied
What does support theory explain
Overestimations of events occurring
Overconfidence
Conjunction Fallacy
What is affect heuristics
Making a judgement or decision based on one’s emotional responses rather than objective information
Finucane et al. (2000)
Affect heuristics
Ppl’s judgments of risk & benefit are inversely related & heavily influenced by their emo responses
+affect -> low risk & ^ benefits
What does the affect heuristic explain
Emo Influence: Emos skew perception of risks & benefits, leading to decisions that don’t align w/ reality
Efficiency vs. Accuracy: Emos can speed up decision-making but often sacrifices accuracy & thoroughness
What’s end anchoring
Tversky & Kahneman (1974)
Initial estimate is made (the anchor)
Then over relies on anchor in future estimates/judgments
Tversky & Kahneman (1974)
Asked 2 groups to estimate % of African countries in UN, when Q was worded asking <>10% vs 65% (anchors), answers changed (24% & 65%)
What does end anchoring explain
Tversky & Kahneman (1974)
Influence of Initial Info: Initial nums or info heavily skews judgments
Bias in Estimation: Even arbitrary or irrelevant anchors leads to systematic biases in estimation
What is the frequentist approach
Ppl have evolved to think wrt freqs, not single event probabilities
