  This paper is concerned with two theories of probability judgment: the
Bayesian theory and the theory of belief functions. It illustrates these
theories with some simple examples and discusses some of the issues that arise
when we try to implement them in expert systems. The Bayesian theory is well
known; its main ideas go back to the work of Thomas Bayes (1702-1761). The
theory of belief functions, often called the Dempster-Shafer theory in the
artificial intelligence community, is less well known, but it has even older
antecedents; belief-function arguments appear in the work of George Hooper
(16401723) and James Bernoulli (1654-1705). For elementary expositions of the
theory of belief functions, see Shafer (1976, 1985).
