Sinan Dogramaci

One of the most difficult problems in the foundations of physics is what gives rise to the arrow of time. Since the fundamental dynamical laws of physics are (essentially) symmetric in time, the explanation for time's arrow must come from elsewhere. A promising explanation introduces a special cosmological initial condition, now called the Past Hypothesis: the universe started in a low-entropy state. Unfortunately, in a universe where there are many copies of us (in the distant ''past'' or the distant ''future''), the Past Hypothesis is not enough; we also need to postulate self-locating (de se) probabilities. However, I show that we can similarly use self-locating probabilities to strengthen its rival---the Fluctuation Hypothesis, leading to in-principle empirical underdetermination and radical epistemological skepticism. The underdetermination is robust in the sense that it is not resolved by the usual appeal to 'empirical coherence' or 'simplicity.' That is a serious problem for the vision of providing a completely scientific explanation of time's arrow.

Language models can produce fluent, grammatical text. Nonetheless, some maintain that language models don’t really learn language and also that, even if they did, that would not be informative for the study of human learning and processing. On the other side, there have been claims that the success of LMs obviates the need for studying linguistic theory and structure. We argue that both extremes are wrong. LMs can contribute to fundamental questions about linguistic structure, language processing, and learning. They force us to rethink arguments and ways of thinking that have been foundational in linguistics. While they do not replace linguistic structure and theory, they serve as model systems and working proofs of concept for gradient, usage-based approaches to language. We offer an optimistic take on the relationship between language models and linguistics.

Invoking metaphysical naturalness is “perhaps the most popular proposed solution” to the problem of grue (Hedden in Can J Philos 45:716–743, 2016). Accordingly, Bradley (Mind 129:179–203, 2020) develops a “Lewisian” method for constraining priors based on the syntactic simplicity of descriptions of possible worlds in a language whose predicates correspond to natural properties. The Lewisian method therefore requires a solution to the arguably unsolved problem of measuring syntactic simplicity. But this paper argues that _given_ a solution to this problem, there is a better alternative to the Lewisian method: a novel “Neo-Carnapian” method for constraining priors based on the indistinguishability of experiences.

The paper presents a comprehensive solution to the new riddle of induction. Gruesome induction is blocked because “grue” is not independent of our sampling and observation methods. Before presenting my theory, I critically survey previous versions of what I call the “independence strategy”, tracing the strategy to three different papers from the 1970s by (respectively) Wilkerson, Moreland, and Jackson. Next I critically examine recent approaches by Okasha, Godfrey-Smith, Schramm, and Freitag. All of these approaches have their virtues, but none of them rules out every problem case. Nor do any of them alone explain exactly what goes wrong in all types of gruesome reasoning. My account aims to do both. Learning lessons from my criticisms of previous theories, I argue that there are several crucial types of independence. All of them are needed to rule out every problem case. I close by suggesting that the independence solution can also be adopted by proponents of other approaches—Bayesians, fans of natural kinds, and pragmatists.

What is the source of logical and mathematical truth? This book revitalizes conventionalism as an answer to this question. Conventionalism takes logical and mathematical truth to have their source in linguistic conventions. This was an extremely popular view in the early 20th century, but it was never worked out in detail and is now almost universally rejected in mainstream philosophical circles. Shadows of Syntax is the first book-length treatment and defense of a combined conventionalist theory of logic and mathematics. It argues that our conventions, in the form of syntactic rules of language use, are perfectly suited to explain the truth, necessity, and a priority of logical and mathematical claims, as well as our logical and mathematical knowledge.

I argue that if intentions are what Grice, and most contemporary action theorists, take them to be, they are inessential for acts of speaker meaning. More specifically, my primary aim is to show that the consensus view of speaker meaning is in deep tension with certain plausible, and widely accepted, cognitive constraints on rational intention pertaining to an agent’s assessment of her prospects of achieving her goal. My secondary aim is to offer an initial case for thinking that the best way to resolve this tension is to give up the claim that meaning is fundamentally a matter of intention. In pursuing this strategy, we will see some of the potential benefits of Bratman’s (1987) work on intentions, plans, and practical reasoning for the theory of meaning, including some interesting consequences of his rejection of the so-called ‘Simple View’ on which we intentionally do only those things that we specifically intend to do. Moreover, our discussion will help to illustrate how some familiar tools from decision theory might find new use in discussions of meaning and communication.