Belief, Evidence, and Uncertainty: Problems of Epistemic Inference

by Prasanta S. Bandyopadhyay, G.G. Brittan, Mark L. Taper

This work breaks new ground by carefully distinguishing the concepts of belief, confirmation, and evidence and then integrating them into a better understanding of personal and scientific epistemologies. It outlines a probabilistic framework in which subjective features of personal knowledge and objective features of public knowledge have their true place. It also discusses the bearings of some statistical theorems on both formal and traditional epistemologies while showing how some of the existing paradoxes in both can be resolved with the help of this framework.This book has two central First, to make precise a distinction between the concepts of confirmation and evidence and to argue that failure to recognize this distinction is the source of certain otherwise intractable epistemological problems. The second goal is to demonstrate to philosophers the fundamental importance of statistical and probabilistic methods, at stake in the uncertain conditions in which for the most part we lead our lives, not simply to inferential practice in science, where they are now standard, but to epistemic inference in other contexts as well. Although the argument is rigorous, it is also accessible. No technical knowledge beyond the rudiments of probability theory, arithmetic, and algebra is presupposed, otherwise unfamiliar terms are always defined and a number of concrete examples are given. At the same time, fresh analyses are offered with a discussion of statistical and epistemic reasoning by philosophers. This book will also be of interest to scientists and statisticians looking for a larger view of their own inferential techniques.The book concludes with a technical appendix which introduces an evidential approach to multi-model inference as an alternative to Bayesian model averaging.

Genres: Philosophy

191 pages, Paperback

Published March 14, 2016

Reviews

[Luke Allen · Rating 4 out of 5]

This is an interesting and somewhat technical book. It may not be appropriate for a beginner philosopher or statistician. I'm sure I am lacking a lot of nuance and technical precision here. However, in broad strokes, I see the main takeaway is that it is often problematic to fail to distinguish between *confirmation* and *evidence,* and this failure to distinguish is the norm, resulting in apparent paradoxes and confusion. Confirmation is described as subjective and could be thought of as "in the head," relating to how an agent's belief in a hypothesis increases with new data. In contrast, evidence is objective and can be thought of as "in the world," pertaining to the relationship between data and hypotheses, independent of the agent's beliefs.

One example of the importance of this distinction comes from interpreting medical test results (e.g., tuberculosis tests). The authors highlight that while a positive TB test may "confirm" the presence of TB (increasing the likelihood of the hypothesis), it doesn't necessarily offer strong evidence due to a high rate of false positives. This misinterpretation can lead to inflated estimations of TB prevalence and misguided medical decisions. However, much of the book focuses more on discussing the broader philosophical and statistical problems than necessarily empirical ones (such as the TB example).