Abstract

I generalise Kolodny and MacFarlane’s miners puzzle by showing epistemic analoguesof their case exist. After motivating various conservative approaches to the originalproblem, I show how they fail to solve the problem in its epistemic guise. I argue that a probabilisticapproach to information-sensitivity gives a general solution to the problem.Keywords: deontic modals, miners puzzle, epistemic ‘should’, probability.

Highlights

  • Kolodny and MacFarlane introduced the infamous miners problem to the literature on deontic modals

  • I show that this semantic puzzle runs deeper than previously thought: there are epistemic analogues of Kolodny and MacFarlane’s case and they have a variety of upshots for our understanding of the problem

  • Miners cases motivate not just a more expressive semantics and the use of orderings based on measure-theoretic notions like expected utility and probability in our semantics for ‘ought’ and ‘should’

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Summary

Introduction

Kolodny and MacFarlane introduced the infamous miners problem to the literature on deontic modals. I show that this semantic puzzle runs deeper than previously thought: there are epistemic analogues of Kolodny and MacFarlane’s case and they have a variety of upshots for our understanding of the problem. Miners cases motivate not just a more expressive semantics and the use of orderings based on measure-theoretic notions like expected utility and probability in our semantics for ‘ought’ and ‘should’. I show in section 3 that epistemic miners cases pose a major stumbling block for responses that try to avoid appealing either to informationsensitivity or measure-theoretic tools. I give an emendation of the classic semantics that can access probabilistic orderings and is sensitive to conditionalisation

The problem
Information and the classical theory
The need for information-sensitivity
What is at stake
A pragmatic solution
A non-probabilistic solution?
The case
Against context-shifting
Against moderate conservativity
A solution
Implementation
Findings
Conclusion
Full Text
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