Several authors have argued that causes differ in the degree to which they are ‘specific’ to their effects. Woodward has used this idea to enrich his influential interventionist theory of causal explanation. Here we propose a way to measure causal specificity using tools from information theory. We show that the specificity of a causal variable is not well defined without a probability distribution over the states of that variable. We demonstrate the tractability and interest of our proposed measure by measuring the specificity of coding DNA and other factors in a simple model of the production of mRNA.

Using Gebharter’s representation, we consider aspects of the problem of discovering the structure of unmeasured submechanisms when the variables in those submechanisms have not been measured. Exploiting an early insight of Sober’s, we provide a correct algorithm for identifying latent, endogenous structure—submechanisms—for a restricted class of structures. The algorithm can be merged with other methods for discovering causal relations among unmeasured variables, and feedback relations between measured variables and unobserved causes can sometimes be learned.

In a recent paper, Belot argues that Bayesians are epistemologically flawed because they believe with probability 1 that they will learn the truth about observational propositions in the limit. While Belot’s considerations suggest that this result should be interpreted with some care, the concerns he raises can largely be defused by putting convergence to the truth in the context of learning from an arbitrarily large but finite number of observations.

Chance Neutrality is the thesis that, conditional on some proposition being true (or being false), its chance of being true should be a matter of practical indifference. The aim of this article is to examine whether Chance Neutrality is a requirement of rationality. We prove that given Chance Neutrality, the Principal Principle entails a thesis called Linearity; the centerpiece of von Neumann and Morgenstern’s expected utility theory. With this in mind, we argue that the Principal Principle is a requirement of practical rationality but that Linearity is not and, hence, that Chance Neutrality is not rationally required.

In this article, I consider what sorts of chance credence norms can be justified by appeal to the idea that ideal credences should line up with the chances. I argue that the Principal Principle cannot be so justified but that an alternative norm, the Temporal Principle—which maintains that an agent’s credence in a proposition ϕ, conditional on the temporal proposition that says that the chance of ϕ is x, should be x—can be so justified.

One of the major puzzles in evolutionary theory is how communication and information transfer are possible when the interests of those involved conflict. Perfect information transfer seems inevitable if there are physical constraints, which limit the signal repertoire of an individual, effectively making bluffing an impossibility. This, I argue, is incorrect. Unfakeable signals by no means guarantee information transfer. I demonstrate the existence of a so-called pooling equilibrium and discuss why the traditional argument for perfect information transfer (Frank’s full disclosure principle) does not hold in all cases. Additionally, I demonstrate that deception is possible at equilibrium despite the fact that signals are impossible to fake.

Epistemic accounts of scientific collaboration usually assume that, one way or another, two heads really are more than twice better than one. We show that this hypothesis is unduly strong. We present a deliberately crude model with unfavorable hypotheses. We show that, even then, when the priority rule is applied, large differences in successfulness can emerge from small differences in efficiency, with sometimes increasing marginal returns. We emphasize that success is sensitive to the structure of competing communities. Our results suggest that purely epistemic explanations of the efficiency of collaborations are less plausible but have much more powerful socioepistemic versions.

Macroeconomists overwhelmingly believe that macroeconomics requires microfoundations, typically understood as a strong eliminativist reductionism. Microfoundations aims to recover intentionality. In the face of technical and data constraints macroeconomists typically employ a representative-agent model, in which a single agent solves the microeconomic optimization problem for the whole economy, and take it to be microfoundationally adequate. The characteristic argument for the representative-agent model holds that the possibility of the sequential elaboration of the model to cover any number of individual agents justifies treating the policy conclusions of the single-agent model as practically relevant. This eschatological justification is examined and rejected.

In this discussion note, we explain how to relax some of the standard assumptions made in Garber-style solutions to the Problem of Old Evidence. The result is a more general and explanatory Bayesian approach.