Undermining Beliefs and Imprecise Probabilities

Last updated on May, 2020

This studies cases where one’s beliefs (thought of as probabilistic degrees-of-belief) undermine themselves. For example, suppose you’re going to forget your passport just if you believe you won’t, and you know this about yourself. What’s the rational response to such cases?

Such cases bear a close relationship to the liar paradox and we can apply such solution from the liar paradox to beliefs and develop well-motivated proposals for rational responses. In particular, a supervaluational Kripke account motivates adopting imprecise probabilities in such cases.

Funded by a Leverhulme Research Grant

Publications

Catrin Campbell-Moore
2021 In Modes of Truth, Routledge
Indeterminate Truth and Credences

We investigate the supervaluational Kripkean account of truth and show how it can apply to finding rational indeterminate credences in undermining scenarios. Our construction is general and could apply to a whole range of domains.

DOI (open access)
Catrin Campbell-Moore
Forthcoming In Epistemic Dilemmas
Epistemic Dilemmas, Undermining Scenarios and Determinate Recommendations

Cases where every credence undermines its own adoption seem to lead to epistemic dilemmas. We move to considering indeterminate credences and look at what is determinately recommended of you. By doing this, we propose that the epistemic dilemmas are avoided.

Preprint

Draft Papers

For more details, email me.

Catrin Campbell-Moore
Last updated on May, 2022
Probability and Non-Classical Logics

We argue that the model of probabilities needs revising when non-classical logics are considered. For strong-Kleene logic we suggest a belief-pair, and for supervaluational logic adopt imprecise probability.

Catrin Campbell-Moore
Last updated on May, 2022
Accuracy and Imprecise Probabilities

We suggest that accuracy considerations should apply to the imprecise by using: what a set recommends is the set of what the individuals in it recommend. This results in a surprisingly nice picture of accuracy for the imprecise.