ABSTRACT
There are two major problems associated with propagation of uncertainty in the rule-based modeling of human reasoning. One concerns how the possibly uncertain evidence in a rule's antecedents affects the rule's conclusion. The other concerns the issue of combining evidence across rules having the same conclusion. Two experiments were conducted in which psychological data were compared with a variety of mathematical models for managing uncertainty. Results of an experiment on the first problem suggested that the certainty of the antecedents in a production rule can be summarized by the maximum of disjunctively connected antecedents and the minimum of conjunctively connected antecedents (maximin summarizing), and that the maximum certainty of the rule's conclusion can be scaled down by multiplication with the results of that summary (multiplication scaling). A second experiment suggested that the second problem can be solved with Heckerman's modified certainty factor model which sums the certainties contributed by each of two rules and divides by 1 plus their product.
