The Psychology of Prediction and Uncertainty Jason Baer

Overview People do not make good intuitive predictions People do not calculate by prior probabilities or statistical measures People make predictions based on: Representativeness Availability Anchoring and Adjustment A person’s mental models, prior experience and biases toward an outcome all influence intuitive predictions in some way

Probability EventProbability A not A A or B A and B A given B

Representativeness Heuristic Estimation of probable events by comparing it to an existing expectation The way we group evidence starts to build a representation Weight of our predictions is based on how we categorize the evidence Mental models and categorization

Example Consider: “Sarah loves to listen to New Age music and faithfully reads her horoscope each day. In her spare time, she enjoys aromatherapy and attending a local spirituality group.” Example from: ippie-girl-with-peace-signs-photo-p234519

The Experiment "Tom W. is of high intelligence, although lacking in true creativity. He has a need for order and clarity, and for neat and tidy systems in which every detail finds its appropriate place. His writing is rather dull and mechanical, occasionally enlivened by somewhat corny puns and by flashes of imagination of the sci-fi type. He has a strong drive for competence. He seems to feel little sympathy for other people and does not enjoy interacting with others. Self-centered, he nonetheless has a deep moral sense."

Categorical Findings The results of the likelihood group compared to the similarity group was nearly 1:1 (R =0.97), where compared to base rate was R= In a follow up, manipulating the expected accuracy of the situation influenced the probability judgments, but lower accuracy did not lead to a regression toward the base rate. Instead, the base rate was only utilized when no other information was available, violating normative rules. In the final study, despite being exposed to probability information before and after the descriptions, the descriptive information played a much greater role in determining the prediction, and prior probability has little to no influence.

Numerical Findings Prediction and Evaluation An evaluation group evaluated the quality of each description relative to a stated population (“does it impress you?”) A prediction group predicted future performance based on the description (“How well do you expect them to do?”) The observed equivalence should only occur under perfect predictive accuracy. Prediction and Translation Participants were given one of 3 evaluative scales, predictions based on that scale produced the same level of equivalence

Discussion Methodologically, they used comparative judgments and made assumptions based on their similarity, relied on very few predictions, and expected participants to use knowledge such as stereotypes that they attained before the study. Illusions of validity are produced by the degree of representativeness of the input observed, rather than accuracy, and this confidence is higher at greater extremes. Regression is a difficult concept to grasp, because we expect data to be maximally representative, and we tend toward discarding less representative results.

Characteristics of Representativeness Insensitivity to prior probability of outcomes. Insensitivity to sample size. Misconceptions of chance. Insensitivity to predictability. The illusion of validity. Misconceptions of regression.

Availability Biases due to the retrievability of instances. Biases due to the effectiveness of a search set. Biases of imaginability. Illusory correlation.

Anchoring and Adjustment Insufficient adjustment. Biases in the evaluation of conjunctive and disjunctive events. Anchoring in the assessment of subjective probability distributions.

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