Combining Probabilities with Full Beliefs

Abstract One of the major problems in formal epistemology is the difficulty of combining probabilistic and dichotomous (all-or-nothing) beliefs in one and the same formal framework. Based on the properties of actual human belief systems, a set of ten desiderata is proposed for belief system models that contain both probabilistic and full beliefs. Previously proposed combined belief systems are shown to fall short of these desiderata. A proof of concept is provided to show that they can be satisfied in a formal model. In the proposed model, the amnesia problem of probabilistic updating is solved by introducing infinitesimal probabilities as carriers of memories of beliefs that have been given up. In the resulting system of probability revision, contingent propositions can be transferred in both directions between full beliefs and beliefs held to a lower degree, in a way that mirrors real-life acquisitions and losses of full beliefs. The subsystem consisting of full beliefs has a pattern of change that constitutes a credible system of dichotomous belief change.

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Publication Details

Journal
Erkenntnis
Published
2024-09-27
DOI
https://doi.org/10.1007/s10670-024-00864-x
Citations
3
Primary Topic
Bayesian Modeling and Causal Inference
Type
article
Field-Weighted Citation Impact
0.69

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article

Combining Probabilities with Full Beliefs

Sven Ove Hansson
3 citations
Erkenntnis
Bayesian Modeling and Causal Inference
0.69
article

Combining Probabilities with Full Beliefs

Sven Ove Hansson
article en
3 citations

Abstract

Abstract One of the major problems in formal epistemology is the difficulty of combining probabilistic and dichotomous (all-or-nothing) beliefs in one and the same formal framework. Based on the properties of actual human belief systems, a set of ten desiderata is proposed for belief system models that contain both probabilistic and full beliefs. Previously proposed combined belief systems are shown to fall short of these desiderata. A proof of concept is provided to show that they can be satisfied in a formal model. In the proposed model, the amnesia problem of probabilistic updating is solved by introducing infinitesimal probabilities as carriers of memories of beliefs that have been given up. In the resulting system of probability revision, contingent propositions can be transferred in both directions between full beliefs and beliefs held to a lower degree, in a way that mirrors real-life acquisitions and losses of full beliefs. The subsystem consisting of full beliefs has a pattern of change that constitutes a credible system of dichotomous belief change.

ErkenntnisVol. 90(8)
Uppsala University (SE)
Vetenskapsrådet, Uppsala Universitet
Openalex Percentile: Top 24%
Bayesian Modeling and Causal Inference
0.69
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