Metaprobability and Dempster-Shafer in Evidential Reasoning

by   Robert Fung, et al.

Evidential reasoning in expert systems has often used ad-hoc uncertainty calculi. Although it is generally accepted that probability theory provides a firm theoretical foundation, researchers have found some problems with its use as a workable uncertainty calculus. Among these problems are representation of ignorance, consistency of probabilistic judgements, and adjustment of a priori judgements with experience. The application of metaprobability theory to evidential reasoning is a new approach to solving these problems. Metaprobability theory can be viewed as a way to provide soft or hard constraints on beliefs in much the same manner as the Dempster-Shafer theory provides constraints on probability masses on subsets of the state space. Thus, we use the Dempster-Shafer theory, an alternative theory of evidential reasoning to illuminate metaprobability theory as a theory of evidential reasoning. The goal of this paper is to compare how metaprobability theory and Dempster-Shafer theory handle the adjustment of beliefs with evidence with respect to a particular thought experiment. Sections 2 and 3 give brief descriptions of the metaprobability and Dempster-Shafer theories. Metaprobability theory deals with higher order probabilities applied to evidential reasoning. Dempster-Shafer theory is a generalization of probability theory which has evolved from a theory of upper and lower probabilities. Section 4 describes a thought experiment and the metaprobability and DempsterShafer analysis of the experiment. The thought experiment focuses on forming beliefs about a population with 6 types of members 1, 2, 3, 4, 5, 6. A type is uniquely defined by the values of three features: A, B, C. That is, if the three features of one member of the population were known then its type could be ascertained. Each of the three features has two possible values, (e.g. A can be either "a0" or "al"). Beliefs are formed from evidence accrued from two sensors: sensor A, and sensor B. Each sensor senses the corresponding defining feature. Sensor A reports that half of its observations are "a0" and half the observations are 'al'. Sensor B reports that half of its observations are "b0,' and half are "bl". Based on these two pieces of evidence, what should be the beliefs on the distribution of types in the population? Note that the third feature is not observed by any sensor.


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