Looking for plausibility

TL;DR

This paper proposes a new measure of plausibility for evaluating explanations of experimental data, contrasting it with traditional probability and possibility measures, and explores its theoretical foundations and neural network applications.

Contribution

It introduces a novel plausibility measure for data interpretation, grounded in abductive reasoning and neural network formalism, expanding beyond existing probability and evidence theories.

Findings

Defined characteristics for the plausibility measure

Compared plausibility with Bayesian probability and evidence theories

Explored neural network formalism for plausibility assessment

Abstract

In the interpretation of experimental data, one is actually looking for plausible explanations. We look for a measure of plausibility, with which we can compare different possible explanations, and which can be combined when there are different sets of data. This is contrasted to the conventional measure for probabilities as well as to the proposed measure of possibilities. We define what characteristics this measure of plausibility should have. In getting to the conception of this measure, we explore the relation of plausibility to abductive reasoning, and to Bayesian probabilities. We also compare with the Dempster-Schaefer theory of evidence, which also has its own definition for plausibility. Abduction can be associated with biconditionality in inference rules, and this provides a platform to relate to the Collins-Michalski theory of plausibility. Finally, using a formalism for wiring logic onto Hopfield neural networks, we ask if this is relevant in obtaining this measure.