Testing abductions from uncertain evidence

Abstract Inference to the Best Explanation (IBE) is traditionally conceived of as a rule of inference, in which one infers to the hypothesis that provides the best explanation of one’s evidence. But what if some of that evidence is uncertain?How, if at all, can the traditional conception of IBE be extended to handle this common class of cases? This paper presents a new general model for investigating rules of inference from uncertain evidence, and then applies this approach to evaluate several different ways of extending IBE to handle uncertain evidence. The model employs computer simulations in which inference rules receive rewards (penalties) for making correct (incorrect) inferences in randomly generated scenarios in which the evidence is uncertain to various degrees. The model allows us to move beyond reliance on intuitive or historical examples in evaluating how well different inference rules handle cases in which the evidence is uncertain.