Gallow (2023) spells out an interventionist theory of counterfactuals that promises to preserve two apparently incompatible intuitions.
Suppose the laws of nature are deterministic. What would have happened if you had chosen some act that you didn't actually choose? The two apparently incompatible intuitions are:
(A1) Had you chosen differently, no law of nature would have been violated.
(A2) Had you chosen differently, the initial conditions of the universe would not have been changed.
Rejecting one of these intuitions is widely thought to spell trouble for Causal Decision Theory. Gallow argues that they can both be respected. I'll explain how. Then I'll explain why I'm not convinced.
A lot of rather technical papers on conditionals have come out in recent years. Let's have a look at one of them: Kocurek (2022).
The paper investigates Al Hajek's argument (e.g. in Hájek (2021)) that "chance undermines would". It begins with a neat observation.
A few thoughts on Sher (2019), which I found advertised in Nair (2021).
This (long and rich) paper presents a formal model of reasons and their weight, with the aim of clarifying how different reasons for or against an act combine.
Sher's guiding idea is to measure the weight by which a reason supports an act in terms of the effect that coming to know the reason would have on the act's desirability.
Many experiences have phenomenal properties: there is something it is like to have them. A puzzling fact about these properties is that we appear to know about them in a special, direct fashion: we are "acquainted" with the phenomenal properties of our experiences. Another, related puzzle is that we appear to know about these properties with absolute certainty: if you have an experience as of looking at a red wall, you can conclusively rule out the possibility that you have an experience as of looking at a green wall.
In Schwarz (2018), I put forward a tentative explanation of these facts. I argued that it would be useful for an agent in a world like ours to have a credence function defined over a space that includes special "imaginary" propositions that are causally tied to stimulations of their sense organs in such a way that any given stimulation makes the agent certain of a corresponding imaginary proposition. What we conceptualise as propositions about phenomenal properties (of our experience), I argued, might be such imaginary propositions.
I have revised the lecture notes for my "Belief, Desire, and Rational Choice" course, making lots of small improvements (I hope) here and there. The revised notes are here, and the LaTeX source is on github.
Often there are many reasons for and against a certain act or belief. How do these reasons combine to an overall reason? Nair (2021) tries to give an answer.
Nair's starting point is a little more specific. Nair intuits that there are cases in which two equally strong reasons combine to a reason that is twice as strong as the individual reasons. In other cases, however, the combined reason is just as strong as the individual reasons, or even weaker.
To make sense this, we need to explain (1) how strengths of reason can be represented numerically, and (2) under what conditions the strengths of different reasons add up.
When I recently taught Newcomb's Problem in an undergraduate class, opinions were – of course – divided. Some students were one-boxers, some were two-boxers. But few of the one-boxers were EDTers. I hadn't realised this in earlier years. Many of them agreed, after some back and forth, that their reasoning also supports one-boxing in a variation of Newcomb's Problem in which both boxes are transparent. In this version of the case, EDT says that you should two-box.
The argument students gave in support of one-boxing is that committing to one-boxing would make it likely that a million dollars is put into the opaque box.
This line of thought is most convincing if we assume that you know in advance that you will face Newcomb's Problem, before the prediction is made. It is uncontroversial that if you can commit yourself to one-boxing at this point, then you should do it.
By "committing", I mean what Arntzenius, Elga, and Hawthorne (2004) call "binding". By committing yourself to one-box, you would effectively turn your future self into an automaton that is sure to one-box. Your future self would no longer make a decision, based on their information and goals at the time. They would simply execute your plan.
Isaacs and Russell (2023) proposes a new way of thinking about evidence and updating.
The standard Bayesian picture of updating assumes that an agent has some ("prior") credence function Cr and then receive some (total) new evidence E. The agent then needs to update Cr in light of E, perhaps by conditionalizing on E. There is no room, in this picture, for doubts about E. The evidence is taken on board with absolute certainty.
The standard picture thereby assumes that the agent's cognitive system is perfectly sensitive to a certain aspect of the world: if E is true, the agent is certain to update on E; if E is false, the agent is certain to not update on E.
Internalism about justification is often supported by intuitions about cases. Srinivasan (2020) argues that these intuitions can't be trusted, because there are analogous cases in which they go in the opposite direction. I'll explain why I'm not convinced.
I should say that I'm not sure what this debate is about. Are we talking about some pre-theoretic folk concept of justification? Or about a concept that plays some important theoretical role? Srinivasan acknowledges (in footnote 10) that there might not be a single, precise folk concept of justification. I agree. To clarify her topic, she says that she is interested in the kind of justification that is a precondition for knowledge. This doesn't really help me. I think that 'knowledge' is context-dependent, and that it sometimes means no more than 'true belief'. There is no interesting justification condition that is present in every case of knowledge.
In a comment on an old blog post, a person called "D" brought up a nice puzzle for Causal Decision Theory. Here's (my version of) the scenario.
You have just taken over as one of three pilots on a spaceship that is on its way to Betelgeuse. The spaceship's flight operations are largely automatised. The only input needed from the pilots is the destination. At present, the only available destinations are Betelgeuse and a service station on a nearby moon. (Other destinations could not be safely reached with current fuel levels.)