Ambiguity and uncertainty in probabilistic inference.

Ambiguity results from having limited knowledge of the process that generates outcomes. It is argued that many real-world processes are perceived to be ambigious; 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 is proposed. The model assumes an anchoring-and-adjustment process in which an initial estimate provides the anchor, and adjustments are made for what might be. The latter is modeled as the result of a mental simulation process where the size of the simulation is a function of the amount of ambiguity, and differential weighting of imagined probabilities reflects one's attitude toward ambiguity. A two-parameter model of this process is shown to be consistent with: Ellsberg's original paradox, the non-additivity of complementary probabilities, current psycho-loical theories of risk, and Keynes' idea of the weight of evidence. The model is tested in four experiments involving boht individual and group analyses. In experiments 1 and 2, the model is shown to predict judgments quite well; in experiment 3, the inference model is shown to predict choices between gambles; experiment 4 shows how buying and selling prices for insurance are systematically influenced by one's attitude toward ambiguity.