Epistemic Newcomb Problem and Epistemic Decision Theory

2021 ◽  
Vol 146 ◽  
pp. 95-118
Author(s):  
Jaemin Jung
Mind ◽  
2013 ◽  
Vol 122 (488) ◽  
pp. 915-952 ◽  
Author(s):  
H. Greaves

2020 ◽  
Vol 178 (2) ◽  
pp. 407-426
Author(s):  
Mattias Skipper

1994 ◽  
Vol 24 (2) ◽  
pp. 234-245 ◽  
Author(s):  
T.K. Moon ◽  
S.E. Budge ◽  
W.C. Stirling ◽  
J.B. Thompson

Mind ◽  
2017 ◽  
Vol 128 (509) ◽  
pp. 69-107 ◽  
Author(s):  
Jason Konek ◽  
Benjamin A Levinstein

Author(s):  
Michael Nielsen

AbstractEpistemic decision theory produces arguments with both normative and mathematical premises. I begin by arguing that philosophers should care about whether the mathematical premises (1) are true, (2) are strong, and (3) admit simple proofs. I then discuss a theorem that Briggs and Pettigrew (Noûs 54(1):162–181, 2020) use as a premise in a novel accuracy-dominance argument for conditionalization. I argue that the theorem and its proof can be improved in a number of ways. First, I present a counterexample that shows that one of the theorem’s claims is false. As a result of this, Briggs and Pettigrew’s argument for conditionalization is unsound. I go on to explore how a sound accuracy-dominance argument for conditionalization might be recovered. In the course of doing this, I prove two new theorems that correct and strengthen the result reported by Briggs and Pettigrew. I show how my results can be combined with various normative premises to produce sound arguments for conditionalization. I also show that my results can be used to support normative conclusions that are stronger than the one that Briggs and Pettigrew’s argument supports. Finally, I show that Briggs and Pettigrew’s proofs can be simplified considerably.


2020 ◽  
Vol 43 ◽  
Author(s):  
Peter Dayan

Abstract Bayesian decision theory provides a simple formal elucidation of some of the ways that representation and representational abstraction are involved with, and exploit, both prediction and its rather distant cousin, predictive coding. Both model-free and model-based methods are involved.


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