Papers by Milo Phillips-Brown
Mind, 2020
Decision theory and folk psychology both purport to represent the same phenomena: our belief-like... more Decision theory and folk psychology both purport to represent the same phenomena: our belief-like and desire- and preference-like states. They also purport to do the same work with these representations: explain and predict our actions. But they do so with different sets of concepts. There's much at stake in whether one of these two sets of concepts can be accounted for with the other. Without such an account, we'd have two competing representations and systems of prediction and explanation, a dubious dualism. Folk psychology structures our daily lives and has proven fruitful in the study of mind and ethics, while decision theory is pervasive in various disciplines, including the quantitative social sciences, especially economics, and philosophy. My interest is in accounting for folk psychology with decision theory -- in particular, for believe and wanting, which decision theory omits. Many have attempted this task for belief. (The Lockean Thesis says that there is such an account.) I take up the parallel task for wanting, which has received far less attention. I propose necessary and sufficient conditions, stated in terms of decision theory, for when you're truly said to want; I give an analogue of the Lockean Thesis for wanting. My account is an alternative to orthodox accounts that link wanting to preference (e.g. Stalnaker (1984), Lewis (1986)), which I argue are false. I argue further that want ascriptions are context-sensitive. My account explains this context-sensitivity, makes sense of conflicting desires, and accommodates phenomena that motivate traditional theses on which 'want' has multiple senses (e.g. all-things-considered vs. pro tanto).
Semantics and Pragmatics, 2019
'If you want to go to Harlem, you have to take the A train' doesn't look special. Yet a compositi... more 'If you want to go to Harlem, you have to take the A train' doesn't look special. Yet a compositional account of its meaning, and the meaning of anankastic conditionals more generally, has proven an enigma. Semanticists have responded by assigning anankastics a unique status, distinguishing them from ordinary indicative conditionals. Condoravdi & Lauer (2016) maintain instead that "anankastic condi-tionals are just conditionals." I argue that Condoravdi and Lauer don't give a general solution to a well-known problem: the problem of conflicting goals. They rely on a special, "effective preference" interpretation for want on which an agent cannot want two things that conflict with her beliefs. A general solution, though, requires that the goals cannot conflict with the facts. Condoravdi and Lauer's view fails. Yet they show, I believe, that previous accounts fail too. Anankastic conditionals are still a mystery.
Sinn und Bedeutung 21, 2018
I want to see the concert, but I don't want to take the long drive. Both of these desire ascripti... more I want to see the concert, but I don't want to take the long drive. Both of these desire ascriptions are true, even though I believe I'll see the concert if and only if I take the drive. Yet they, and strongly conflicting desire ascriptions more generally, are predicted incompatible by the standard semantics, given two standard constraints. There are two proposed solutions. I argue that both face problems because they misunderstand how what we believe influences what we desire. I then sketch my own solution: a coarse-worlds semantics that captures the extent to which belief influences desire. My semantics models what I call some-things-considered desire. Considering what the concert would be like, but ignoring the drive, I want to see the concert; considering what the drive would be like, but ignoring the concert, I don't want to take the drive.
Drafts by Milo Phillips-Brown
Algorithms wield increasing control over our lives—over the jobs we get, the loans we're granted,... more Algorithms wield increasing control over our lives—over the jobs we get, the loans we're granted, the information we see online. Algorithms can and often do wield their power in a biased way, and much work has been devoted to algorithmic bias. In contrast, algorithmic neutrality has been largely neglected. I investigate algorithmic neutrality, tackling three questions: What is algorithmic neutrality? Is it possible? And when we have it in mind, what can we learn about algorithmic bias?
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Papers by Milo Phillips-Brown
Drafts by Milo Phillips-Brown