Probabilistic temporal reasoning

A theory of causal reasoning under uncertainty that uses easily obtainable statistical data to provide expectations concerning how long propositions are likely to persist in the absence of specific knowledge to the contrary is described.

Abstract

Reasoning about change requires predicting how long a proposition, having become true, will continue to be so. Lacking perfect knowledge, an agent may be constrained to believe that a proposition persists indefinitely simply because there is no way for the agent to infer a contravening proposition with certainty. In this paper, we describe a theory of causal reasoning under uncertainty. Our theory uses easily obtainable statistical data to provide expectations concerning how long propositions are likely to persist in the absence of specific knowledge to the contrary. We consider a number of issues that arise in combining evidence, and describe an approach to computing probabilistic assessments of the sort licensed by our theory.