Ambiguity and Uncertainty in Probabilistic Inference.
CHICAGO UNIV IL CENTER FOR DECISION RESEARCH
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Ambiguity results from having limited knowledge of the process that generates outcomes. It is argued that many real-world processes are perceived to be ambiguous moreover, as Ellsberg demonstrated, this poses problems for theories of probability operationalized via choices amongst gambles. A descriptive model of how people make judgments under ambiguity in tasks where data come from a source of limited, but not exactly known reliability, is proposed. The model assumes an anchoring-and-adjustment process in which data provides the anchor, and adjustments are made for what might have been. The latter is modeled as the result of a mental simulation process that incorporates the unreliability of the source and ones attitude toward ambiguity in the circumstances. A two-parameter model of this process is shown to be consistent with Keynes idea of the weight of evidence, the non-additivity of complementary probabilities, current psychological theories of risk, and Ellsbergs original paradox. The model is tested in four experiments at both the individual and group levels. In experiments 1-3, the model is shown to predict judgments quite well in experiment 4, the inference model is shown to predict choices between gambles. The results and model are then discussed with respect to the importance of ambiguity in assessing perceived uncertainty the use of cognitive strategies in judgments under ambiguity the role of ambiguity in risky choice and extensions of the model. Author
- Statistics and Probability