Against Conventional Wisdom

Conventional wisdom has it that truth is always evaluated using our actual linguistic conventions, even when considering counterfactual scenarios in which different conventions are adopted. This principle has been invoked in a number of philosophical arguments, including Kripke’s defense of the necessity of identity and Lewy’s objection to modal conventionalism. But it is false. It fails in the presence of what Einheuser (2006) calls c-monsters, or convention-shifting expressions (on analogy with Kaplan’s monsters, or context-shifting expressions). We show that c-monsters naturally arise in contexts, such as metalinguistic negotiations, where speakers entertain alternative conventions. We develop an expressivist theory—inspired by Barker (2002) and MacFarlane (2016) on vague predications and Einheuser (2006) on counterconventionals—to model these shifts in convention. Using this framework, we reassess the philosophical arguments that invoked the conventional wisdom

Reasoning about strategies and rational play in dynamic games

We discuss a number of conceptual issues that arise in attempting to capture, in dynamic games, the notion that there is "common understanding" among the players that they are all rational.Belief revision, common belief, counterfactual, dynamic game, model of a game, rationality

Leveraging Contextual Counterfactuals Toward Belief Calibration

Beliefs and values are increasingly being incorporated into our AI systems through alignment processes, such as carefully curating data collection principles or regularizing the loss function used for training. However, the meta-alignment problem is that these human beliefs are diverse and not aligned across populations; furthermore, the implicit strength of each belief may not be well calibrated even among humans, especially when trying to generalize across contexts. Specifically, in high regret situations, we observe that contextual counterfactuals and recourse costs are particularly important in updating a decision maker's beliefs and the strengths to which such beliefs are held. Therefore, we argue that including counterfactuals is key to an accurate calibration of beliefs during alignment. To do this, we first segment belief diversity into two categories: subjectivity (across individuals within a population) and epistemic uncertainty (within an individual across different contexts). By leveraging our notion of epistemic uncertainty, we introduce `the belief calibration cycle' framework to more holistically calibrate this diversity of beliefs with context-driven counterfactual reasoning by using a multi-objective optimization. We empirically apply our framework for finding a Pareto frontier of clustered optimal belief strengths that generalize across different contexts, demonstrating its efficacy on a toy dataset for credit decisions.Comment: ICML (International Conference on Machine Learning) Workshop on Counterfactuals in Minds and Machines, 202

Constraining credences

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Linguistics and Philosophy, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 101-108).This dissertation is about ways in which our rational credences are constrained: by norms governing our opinions about counterfactuals, by the opinions of other agents, and by our own previous opinions. In Chapter 1, I discuss ordinary language judgments about sequences of counterfactuals, and then discuss intuitions about norms governing our credence in counterfactuals. I argue that in both cases, a good theory of our judgments calls for a static semantics on which counterfactuals have substantive truth conditions, such as the variably strict conditional semantic theories given in STALNAKER 1968 and LEWIS 1973a. In particular, I demonstrate that given plausible assumptions, norms governing our credences about objective chances entail intuitive norms governing our opinions about counterfactuals. I argue that my pragmatic accounts of our intuitions dominate semantic theories given by VON FINTEL 2001, GILLIES 2007, and EDGINGTON 2008. In Chapter 2, I state constraints on what credence constitutes a perfect compromise between agents who have different credences in a proposition. It is sometimes taken for granted that disagreeing agents achieve a perfect compromise by splitting the difference in their credences. In this chapter, I develop and defend an alternative strategy for perfect compromise, according to which agents perfectly compromise by coordinating on the credences that they collectively most prefer, given their purely epistemic values. In Chapter 3, I say how your past credences should constrain your present credences.(cont.) In particular, I develop a procedure for rationally updating your credences in de se propositions, or sets of centered worlds. I argue that in forming an updated credence distribution, you must first use information you recall from your previous self to form a hypothetical credence distribution, and then change this hypothetical distribution to reflect information you have genuinely learned as time has passed. In making this proposal precise, I argue that your recalling information from your previous self resembles a familiar process: agents' gaining information from each other through ordinary communication.by Sarah Moss.Ph.D

Counterfactuals 2.0 Logic, Truth Conditions, and Probability

The present thesis focuses on counterfactuals. Specifically, we will address new questions and open problems that arise for the standard semantic accounts of counterfactual conditionals. The first four chapters deal with the Lewisian semantic account of counterfactuals. On a technical level, we contribute by providing an equivalent algebraic semantics for Lewis' variably strict conditional logics, which is notably absent in the literature. We introduce a new kind of algebra and differentiate between local and global versions of each of Lewis' variably strict conditional logics. We study the algebraic properties of Lewis' logics and the structure theory of our newly introduced algebras. Additionally, we employ a new algebraic construction, based on the framework of Boolean algebras of conditionals, to provide an alternative semantics for Lewisian counterfactual conditionals. This semantic account allows us to establish new truth conditions for Lewisian counterfactuals, implying that Lewisian counterfactuals are definable conditionals, and each counterfactual can be characterized as a modality of a corresponding probabilistic conditional. We further extend these results by demonstrating that each Lewisian counterfactual can also be characterized as a modality of the corresponding Stalnaker conditional. The resulting formal semantic framework is much more expressive than the standard one and, in addition to providing new truth conditions for counterfactuals, it also allows us to define a new class of conditional logics falling into the broader framework of weak logics. On the philosophical side, we argue that our results shed new light on the understanding of Lewisian counterfactuals and prompt a conceptual shift in this field: Lewisian counterfactual dependence can be understood as a modality of probabilistic conditional dependence or Stalnakerian conditional dependence. In other words, whether a counterfactual connection occurs between A and B depends on whether it is "necessary" for a Stalnakerian/probabilistic dependence to occur between A and B. We also propose some ways to interpret the kind of necessity involved in this interpretation. The remaining two chapters deal with the probability of counterfactuals. We provide an answer to the question of how we can characterize the probability that a Lewisian counterfactual is true, which is an open problem in the literature. We show that the probability of a Lewisian counterfactual can be characterized in terms of belief functions from Dempster-Shafer theory of evidence, which are a super-additive generalization of standard probability. We define an updating procedure for belief functions based on the imaging procedure and show that the probability of a counterfactual A > B amounts to the belief function of B imaged on A. This characterization strongly relies on the logical results we proved in the previous chapters. Moreover, we also solve an open problem concerning the procedure to assign a probability to complex counterfactuals in the framework of causal modelling semantics. A limitation of causal modelling semantics is that it cannot account for the probability of counterfactuals with disjunctive antecedents. Drawing on the same previous works, we define a new procedure to assign a probability to counterfactuals with disjunctive antecedents in the framework of causal modelling semantics. We also argue that our procedure is satisfactory in that it yields meaningful results and adheres to some conceptually intuitive constraints one may want to impose when computing the probability of counterfactuals