Support Theory: A Nonextensional Representation of Probability Judgment

Abstract

Abstract We present a descriptive theory of belief in which probability judgments are not assigned to events—as in other models—but rather to descriptions of events, called hypotheses. In this theory, judged probability is expressed as normalized support, or strength of evidence, of the focal hypothesis relative to the alternative. The theory is nonextensional because alternative descriptions of the same event can give rise to different judgments. A review of the experimental literature and the results of new studies confirm the major predictions of support theory. First, judged probability increases by unpacking the focal hypothesis and decreases by unpacking the alternative hypothesis. Second, judged probabilities are complementary in the binary case and sub additive in the general case, contrary to both classical and revisionist models of belief. Third, subadditivity is more pronounced for probability judgments than for frequency judgments. Support theory provides a unified treatment of a wide range of empirical findings. The theory is extended to the assessment of upper and lower probabilities. Prescriptive implications are explored.