A blog about causation, chance, credence, and choice.

Predictable Poverty in Sequential Decision Problems

Wells (forthcoming) has an really nice example of a sequential decision problem in which an evidential decision theorist will end up predictably poorer than a causal decision theorist. Wells thinks that this case shows that we should reject evidential decision theory. I agree that we should reject evidential decision theory, but I don’t think that a proponent of CDT should use Wells’s case to argue for this conclusion.

The reason is that there are sequential decision problems in which a causal decision theorist will end up predictably poorer than an evidential decision theorist, even when both the causal decision theorist and the evidential decision theorist face this decision problem in the same circumstances. If predictable relative poverty like this gives a sufficient reason to reject EDT, then it likewise gives a sufficient reason to reject CDT. (I think we should tollens—though I won’t be making that case here.)

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Manage the News and Value Improvements

This weekend, I’ve been re-thinking some presentational issues with the variant of causal decision theory I develop in this paper (call it ‘manage the improvement news’, or ‘MIN’). Here, I’m going to begin by presenting a general framework for thinking about decision theory (lifted almost entirely from this excellent paper from Konek & Levinstein). Within this framework, we can understand evidential decision theory (EDT) as claiming that the deontic value of an act is given by 1) the act’s evidential impact on 2) axiological value. Standard causal decision theory (CDT) rejects the first component (1) of this theory of deontological value. It denies that you should ‘manage the news’ by incorporating the evidential impact of your selecting the act. MIN, in contrast, retains the first component of EDT’s theory of deontological value but rejects the second (2). It says that the deontic value of an act is given by 1) the act’s evidential impact on 2) its improvement value. MIN thereby agrees with EDT, against CDT, that you should incorporate the evidence provided by an act’s performance into your evaluation of the act. But it disagrees about the underlying evaluation which is to be informed by this evidence.

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The Role of Interventions in Causal Decision Theory

Causal Bayes Nets provide a nice formal representation of the world’s causal and probabilistic structure, and so it is natural to want formulate causal decision theory (CDT) in terms of causal Bayes nets. Lots of good work has been done on this front—see, in particular, Meek and Glymour (1994), Pearl (2000, chapter 4), Hitchcock (2016), and Stern (2017). Central to these formulations of CDT is the distinction between a probability function which has been conditioned on an act A’s performance and a probability function which has been updated on an intervention bringing about A’s performance.

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