Many philosophers are skeptical about the scientific value of the concept of biological information. However, several have recently proposed a more positive view of ascribing information as an exercise in scientific modeling. I argue for an alternative role: guiding empirical data collection for the sake of theorizing about the evolution of semantics. I clarify and expand on Bergstrom and Rosvall’s suggestion of taking a “diagnostic” approach that defines biological information operationally as a procedure for collecting empirical cases. The more recent modeling-based accounts still perpetuate a theory-centric view of scientific concepts, which motivated philosophers’ misplaced skepticism in the first place.

Recent efforts to argue that nonepistemic values have a legitimate role to play in assessing scientific models, theories, and hypotheses typically either reject the distinction between epistemic and nonepistemic values or incorporate nonepistemic values only as a secondary consideration for resolving epistemic uncertainty. Given that scientific representations can legitimately be evaluated not only based on their fit with the world but also with respect to their fit with the needs of their users, we show in two case studies that nonepistemic values can play a legitimate role as factors that override epistemic considerations in assessing scientific representations for practical purposes.

There is not a unique inductive skeptical position; there are grades of inductive skepticism. There is nothing much to say about complete skepticism, but some more restricted skeptical positions may be profitably analyzed.

Decision making typically requires judgments about causal relations: we need to know the causal effects of our actions and the causal relevance of various environmental factors. We investigate how several individuals’ causal judgments can be aggregated into collective causal judgments. First, we consider the aggregation of causal judgments via the aggregation of probabilistic judgments and identify the limitations of this approach. We then explore the possibility of aggregating causal judgments independently of probabilistic ones. Formally, we introduce the problem of causal-network aggregation. Finally, we revisit the aggregation of probabilistic judgments when this is constrained by prior aggregation of qualitative causal judgments.

This essay advocates a “functional” approach to causation and causal reasoning: these are to be understood in terms of the goals and purposes of causal thinking. This approach is distinguished from accounts based on metaphysical considerations or on reconstruction of “intuitions.”

Neuroeconomics is a research program founded on the thesis that cognitive and neurobiological data constitute evidence for answering economic questions. I employ confirmation theory in order to reject arguments both for and against neuroeconomics. I also emphasize that some arguments for neuroeconomics will not convince the skeptics because these arguments make a contentious assumption: economics aims for predictions and deep explanations of choices in general. I then argue for neuroeconomics by appealing to a much more restrictive (and thereby skeptic-friendly) characterization of the aims of economics.

The use of the Law of Likelihood (LL) as a general tool for assessing rival hypotheses has been criticized for its ambiguous treatment of background information. The LL endorses radically different answers depending on what information is designated as background versus evidence. I argue that once one distinguishes between two questions about evidentiary support, the ambiguity vanishes. I demonstrate this resolution by applying it to a debate over the status of the ‘fine-tuning argument’.

Frank has recently argued for an information-theoretic interpretation of natural selection. This interpretation is based on the identification of a measure related to the Malthusian parameter (for population change) with the Jeffreys divergence between the present allelic distribution of the population and that distribution in the next generation. It is pointed out in this analysis that this identification only holds if the mean fitness of the population is a constant, that is, there is no selection. This problem is used to argue for the superiority of the standard dynamical interpretation of natural selection over its information-theoretic counterpart.

Experiments are commonly thought to have epistemic privilege over simulations. Two ideas underpin this belief: first, experiments generate greater inferential power than simulations, and second, simulations cannot surprise us the way experiments can. In this article I argue that neither of these claims is true of experiments versus simulations in general. We should give up the common practice of resting in-principle judgments about the epistemic value of cases of scientific inquiry on whether we classify those cases as experiments or simulations, per se. To the extent that either methodology puts researchers in a privileged epistemic position, this is context sensitive.

This article discusses the dual interpretation of the Jeffreys-Lindley paradox associated with Bayesian posterior probabilities and Bayes factors, both as a differentiation between frequentist and Bayesian statistics and as a pointer to the difficulty of using improper priors while testing. I stress the considerable impact of this paradox on the foundations of both classical and Bayesian statistics. While assessing existing resolutions of the paradox, I focus on a critical viewpoint of the paradox discussed by Spanos in Philosophy of Science.

This paper clarifies the historical nature of species by showing that species are path-dependent entities. A species’ identity is not determined by its intrinsic properties or its origin, but by its unique evolutionary path. Seeing that species are path-dependent entities has three implications: it shows that origin essentialism is mistaken, it rebuts two challenges to the species-are-historical-entities thesis, and it demonstrates that the identity of a species during speciation depends on future events.

The sensitive dependence on initial conditions (SDIC) associated with nonlinear models imposes limitations on the models’ predictive power. We draw attention to an additional limitation than has been underappreciated, namely, structural model error (SME). A model has SME if the model dynamics differ from the dynamics in the target system. If a nonlinear model has only the slightest SME, then its ability to generate decision-relevant predictions is compromised. Given a perfect model, we can take the effects of SDIC into account by substituting probabilistic predictions for point predictions. This route is foreclosed in the case of SME, which puts us in a worse epistemic situation than SDIC.

The strategy of updating credences by minimizing the relative entropy has been questioned by many authors, most strongly by means of the Judy Benjamin puzzle. I present a new analysis of Judy Benjamin–like forms of new information and defend the thesis that in general the rational posterior is indeterminate, meaning that a family of posterior credence functions rather than a single one is the rational response when that type of information becomes available. The proposed thesis extends naturally to all cases in which new information is traditionally handled by minimizing the relative entropy.

When evaluating the success of causal theories of explanation the focus has typically been on the legitimacy of causal relations and on putative examples of explanations that we cannot capture in causal terms. Here I motivate the existence of a third kind of problem: the difficulty of accounting for explanatory disputes. Moreover, I argue that this problem remains even if the first two are settled and that it threatens to undercut one of the central motivations for causal accounts of explanation, namely, the causal account of the directionality of scientific explanation.

For a population to undergo evolution by natural selection, it is assumed that the constituents of the population form parent-offspring lineages, that is, that they must reproduce. I challenge this assumption by dividing the notion of reproduction into two subprocesses, that is, multiplication and inheritance, that produce parent-offspring lineages between the parts of a population, and I show that their population-level roles, generation and memory, respectively, can be effected by processes that do not rely on such local-level lineages. I further argue that these two population-level processes, not local parent-offspring lineages, are necessary conditions for a population to undergo Darwinian evolution.

This article discusses minimal model explanations, which we argue are distinct from various causal, mechanical, difference-making, and so on, strategies prominent in the philosophical literature. We contend that what accounts for the explanatory power of these models is not that they have certain features in common with real systems. Rather, the models are explanatory because of a story about why a class of systems will all display the same large-scale behavior because the details that distinguish them are irrelevant. This story explains patterns across extremely diverse systems and shows how minimal models can be used to understand real systems.

Historical sciences like evolutionary biology reconstruct past events by using the traces that the past has bequeathed to the present. Markov chain theory entails that the passage of time reduces the amount of information that the present provides about the past. Here we use a Moran process framework to show that some evolutionary processes destroy information faster than others. Our results connect with Darwin’s principle that adaptive similarities provide scant evidence of common ancestry whereas neutral and deleterious similarities do better. We also describe how the branching in phylogenetic trees affects the information that the present supplies about the past.

There is a venerable position in the philosophy of space and time that holds that the geometry of spacetime is conventional, provided one is willing to postulate a “universal force field.” Here we ask a more focused question, inspired by this literature: in the context of our best classical theories of space and time, if one understands “force” in the standard way, can one accommodate different geometries by postulating a new force field? We argue that the answer depends on one’s theory. In Newtonian gravitation the answer is yes; in relativity theory, it is no.

Some philosophers respond to Leibniz’s “shift” argument against absolute space by appealing to antihaecceitism about possible worlds, using David Lewis’s counterpart theory. But separated from Lewis’s distinctive system, it is difficult to understand what this doctrine amounts to or how it bears on the Leibnizian argument. In fact, the best way of making sense of the relevant kind of antihaecceitism concedes the main point of the Leibnizian argument, pressing us to consider alternative spatiotemporal metaphysics.