Justifying the Norms of Inductive Inference

TL;DR

This paper explores general inductive inference methods beyond traditional Bayesian approaches, proposing new updating rules and arguing for probabilism using an accuracy-based framework in contexts where truth is not the primary goal.

Contribution

It introduces a novel inductive inference framework that generalizes Bayesian updating and proposes two reasonable updating rules suitable for diverse contexts.

Findings

Standard Bayesian updating is a special case of the proposed rules.

A new, alternative updating rule is introduced and justified.

Probabilism is supported through an accuracy-based argument.

Abstract

Bayesian inference is limited in scope because it cannot be applied in idealized contexts where none of the hypotheses under consideration is true and because it is committed to always using the likelihood as a measure of evidential favoring, even when that is inappropriate. The purpose of this paper is to study inductive inference in a very general setting where finding the truth is not necessarily the goal and where the measure of evidential favoring is not necessarily the likelihood. I use an accuracy argument to argue for probabilism and I develop a new kind of argument to argue for two general updating rules, both of which are reasonable in different contexts. One of the updating rules has standard Bayesian updating, Bissiri et al's (2016) general Bayesian updating, Douven's (2016) IBE-based updating, and Vassend's (2019a) quasi-Bayesian updating as special cases. The other updating rule is novel.