Ambiguity and Uncertainty in Probabilistic Inference.

TL;DR: In this article, a 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, which assumes an anchoring-and-adjustment process in which data provides the anchor, and adjustments are made for what might have been.

Abstract: : 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 one's 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 Ellsberg's 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)