A Formal Solution to Reichenbach’s Reference Class Problem

Following Reichenbach, it is widely held that in making a direct inference, one should base one’s conclusion on a relevant frequency statement concerning the most specific reference class for which one is able to make a warranted and relatively precise-valued frequency judgment. In cases where one has accurate and precise-valued frequency information for two relevant reference classes, R1 and R2, and one lacks accurate and precise-valued frequency information concerning their intersection, R1 intersect R2, it is widely held, following Reichenbach, that no inference may be drawn. In contradiction to Reichenbach and the common wisdom, I argue for the view that it is often possible to draw a reasonable informative conclusion, in such circumstances. As a basis for drawing such a conclusion, I show that one is generally in a position to formulate a reasonable direct inference for a reference class that is more specific than either of R1 and R2

Qualitative probabilistic inference under varied entropy levels

In previous work, we studied four well known systems of qualitative probabilistic inference, and presented data from computer simulations in an attempt to illustrate the performance of the systems. These simulations evaluated the four systems in terms of their tendency to license inference to accurate and informative conclusions, given incomplete information about a randomly selected probability distribution. In our earlier work, the procedure used in generating the unknown probability distribution (representing the true stochastic state of the world) tended to yield probability distributions with moderately high entropy levels. In the present article, we present data charting the performance of the four systems when reasoning in environments of various entropy levels. The results illustrate variations in the performance of the respective reasoning systems that derive from the entropy of the environment, and allow for a more inclusive assessment of the reliability and robustness of the four systems

A Formal Solution to Reichenbach’s Reference Class Problem

Following Reichenbach, it is widely held that in making a direct inference, one should base one’s conclusion on a relevant frequency statement concerning the most specific reference class for which one is able to make a warranted and relatively precise-valued frequency judgment. In cases where one has accurate and precise-valued frequency information for two relevant reference classes, R1 and R2, and one lacks accurate and precise-valued frequency information concerning their intersection, R1 intersect R2, it is widely held, following Reichenbach, that no inference may be drawn. In contradiction to Reichenbach and the common wisdom, I argue for the view that it is often possible to draw a reasonable informative conclusion, in such circumstances. As a basis for drawing such a conclusion, I show that one is generally in a position to formulate a reasonable direct inference for a reference class that is more specific than either of R1 and R2

Qualitative Probabilistic Inference with Default Inheritance

There are numerous formal systems that allow inference of new conditionals based on a conditional knowledge base. Many of these systems have been analysed theoretically and some have been tested against human reasoning in psychological studies, but experiments evaluating the performance of such systems are rare. In this article, we extend the experiments in [19] in order to evaluate the inferential properties of c-representations in comparison to the well-known Systems P and Z. Since it is known that System Z and c-representations mainly differ in the sorts of inheritance inferences they allow, we discuss subclass inheritance and present experimental data for this type of inference in particula