Making decisions with evidential probability and objective Bayesian calibration inductive logics

International Journal of Approximate Reasoning:1-37 (forthcoming)
  Copy   BIBTEX

Abstract

Calibration inductive logics are based on accepting estimates of relative frequencies, which are used to generate imprecise probabilities. In turn, these imprecise probabilities are intended to guide beliefs and decisions — a process called “calibration”. Two prominent examples are Henry E. Kyburg's system of Evidential Probability and Jon Williamson's version of Objective Bayesianism. There are many unexplored questions about these logics. How well do they perform in the short-run? Under what circumstances do they do better or worse? What is their performance relative to traditional Bayesianism? In this article, we develop an agent-based model of a classic binomial decision problem, including players based on variations of Evidential Probability and Objective Bayesianism. We compare the performances of these players, including against a benchmark player who uses standard Bayesian inductive logic. We find that the calibrated players can match the performance of the Bayesian player, but only with particular acceptance thresholds and decision rules. Among other points, our discussion raises some challenges for characterising “cautious” reasoning using imprecise probabilities. Thus, we demonstrate a new way of systematically comparing imprecise probability systems, and we conclude that calibration inductive logics are surprisingly promising for making decisions.

Other Versions

No versions found

Links

PhilArchive



    Upload a copy of this work     Papers currently archived: 103,388

External links

Setup an account with your affiliations in order to access resources via your University's proxy server

Through your library

Similar books and articles

Ambiguous Decisions in Bayesianism and Imprecise Probability.Mantas Radzvilas, William Peden & Francesco De Pretis - 2024 - British Journal for the Philosophy of Science Short Reads.
The Ambiguity Dilemma for Imprecise Bayesians.Mantas Radzvilas, William Peden & Francesco De Pretis - forthcoming - The British Journal for the Philosophy of Science.
How Uncertain Do We Need to Be?Jon Williamson - 2014 - Erkenntnis 79 (6):1249-1271.
On nonparametric predictive inference and objective bayesianism.F. P. A. Coolen - 2006 - Journal of Logic, Language and Information 15 (1):21-47.
Evidentialism, Inertia, and Imprecise Probability.William Peden - 2024 - The British Journal for the Philosophy of Science 75 (4):797-819.

Analytics

Added to PP
2023-10-02

Downloads
89 (#243,458)

6 months
12 (#218,371)

Historical graph of downloads
How can I increase my downloads?

Author Profiles

Citations of this work

No citations found.

Add more citations