Sexes are normally taken to be fundamental categories in biology—many sexually reproducing organisms fall under the categories of female/male. Much research aims at explaining differences between sexes. Sexual selection forms a central framework for explaining “typical” distributions of traits among sexes and explicating circumstances leading to “reversal.” I claim that sexual selection models need not make use of sexes and that sexes lack explanatory significance in such models. I offer a framework of reproductive dimorphism and argue that it is better than that of sexes because it (1) is more compatible with explanations of sexual selection and (2) allows for their greater applicability.

I argue that machine learning (ML) models used in science function as highly idealized toy models. If we treat ML models as a type of highly idealized toy model, then we can deploy standard representational and epistemic strategies from the toy model literature to explain why ML models can still provide epistemic success despite their lack of similarity to their targets.

Scientific disagreements are an important catalyst for scientific progress. But what happens when scientists disagree amid times of crisis, when we need quick yet reliable policy guidance? In this article, we provide a normative account for how scientists facing disagreement in the context of “fast science” should respond and how policy makers should evaluate such disagreement. Starting from an argumentative, pragma-dialectic account of scientific controversies, we argue for the importance of higher-order evidence (HOE), and we specify desiderata for scientifically relevant HOE. We use our account to analyze the controversy about the aerosol transmission of COVID-19.

The framework of Solomonoff prediction assigns prior probability to hypotheses inversely proportional to their Kolmogorov complexity. There are two well-known problems. First, the Solomonoff prior is relative to a choice of universal Turing machine. Second, the Solomonoff prior is not computable. However, there are responses to both problems. Different Solomonoff priors converge with more and more data. Further, there are computable approximations to the Solomonoff prior. I argue that there is a tension between these two responses. This is because computable approximations to Solomonoff prediction do not always converge.

The science of the 19th and early 20th century permitted the view that all human experience is subject to the deterministic laws of physics. Reality was conformable with these laws, and the laws could be used to designate what is real.

The first part deals with the problem of the external form of ostensive definition. It is concluded that the definition statement is not complete. The proper form of this statement is not a sentence, but a sentential function, namely a sentential function of the type: “IIx [N(x) = x is in the respect R and in the degree D such as A, B ... and not such as K, L ...]” where “N” stands for the term being defined.

Thus the ostensive definition informs about the criteria of applicability of the defined term in a partial way only, and the rest must be supplied by the addressee for whom the given definition was destined.

In the second part the conditions are analysed on which depends the possibility of solving that problem, and consequently the conditions on which depend the informational value and the efficacy of ostensive definition. The concluding remarks deal with the properties of the terms introduced by the ostensive method.

In Science, Truth, and Democracy, Philip Kitcher challenges the view that science has a single, context-independent, goal, and that the pursuit of this goal is essentially immune from moral critique. He substitutes a context-dependent account of science's goal, and shows that this account subjects science to moral evaluation. I argue that Kitcher's approach must be modified, as his account of science ultimately must be explicated in terms of moral concepts. I attempt, therefore, to give an account of science's goal that is free of direct moral entanglements but still makes this goal context-dependent and leaves the choice of which projects to pursue subject to moral scrutiny.

I explore the evolution of strategies in an Augmented Stag Hunt game that adds a punishing strategy to the ordinary Stag Hunt strategies of cooperating, which aims for optimality, and defecting, which “plays it safe.” Cooperating weakly dominates punishing and defecting is the unique evolutionarily stable strategy (ESS). Nevertheless, for a wide class of Augmented Stag Hunts, polymorphic strategies combining punishing and cooperating collectively have greater attracting power for replicator dynamics than that of the ESS. The analysis here lends theoretical support to the altruistic punishment hypothesis in the social sciences.

The writer of this paper is not an expert on probability from either the mathematical or the philosophical side, but a theoretical physicist forced by the exigencies of work in his own field to make his peace with the concept of probability. The present contribution is a sequel to remarks on two different types of probability in a recent paper on the relation between statistical mechanics and thermodynamics.

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.

This paper introduces a new equilibrium concept for normal form games called dependency equilibrium; it is defined, exemplified, and compared with Nash and correlated equilibria in Sections 2–4. Its philosophical motive is to rationalize cooperation in the one shot prisoners’ dilemma. A brief discussion of its meaningfulness in Section 5 concludes the paper.

The controversy which has raged between those who accept Aristotelian Logic and those who reject it, seems to continue. It is, I believe, advisable to consider the arguments on both sides. Modern Logicians for the most part insist that Aristotelian Logic breaks down, i.e. it is not true for all meanings of the terms. On the other hand there are those who insist on the validity of Aristotelian Logic claiming that those values for which it fails are not permissible values. But there is one who stands almost entirely alone in his position—namely Professor H. B. Smith—who maintains that Aristotelian Logic breaks down because and only because its interpretation into Boolean terms has been accomplished by means of a definition that is not forced upon us and which, since it gives up desirable principles, is to be rejected.

I analyze the two main theses of Helmholtz's “The Applicability of the Axioms to the Physical World,” in which he argued that the axioms of Euclidean geometry are not, as his neo-Kantian opponents had argued, binding on any experience of the external world. This required two argumentative steps: 1) a new account of the structure of our representations which was consistent both with the experience of our (for him) Euclidean world and with experience of a non-Euclidean one, and 2) a demonstration of why geometric propositions are essentially connected to material and temporal aspects of experience. The effect of Helmholtz's discussion is to throw into relief an intermediate category of metrological objects—objects which are required for the properly theoretical activity of doing physical science (in this sense, a priori requirements for doing science), all while being recognizably contingent aspects of experience.

Computer scientists have provided a distinct justification of Occam’s Razor. Using the probably approximately correct framework, they provide a theorem that they claim demonstrates that we should favor simpler hypotheses. The argument relies on a philosophical interpretation of the theorem. I argue that the standard interpretation of the result in the literature is misguided and that a better reading does not, in fact, support Occam’s Razor at all. To this end, I state and prove a very similar theorem that, if interpreted the same way, would justify the contradictory Anti-Occam’s Razor—the principle that we should favor more complex hypotheses.

Physics presents us with a symphony of natural constants: $G$, ℏ, $c$, and so forth. Up to this point, constants have received comparatively little philosophical attention. In this article, I provide an account of dimensionful constants, in particular the gravitational constant. I propose that they represent interquantity structure in the form of relations between quantities with different dimensions. I use this account of $G$ to settle a debate over whether mass scalings are symmetries of Newtonian gravitation. I argue that they are not, but only if we interpret mass anti-quidditistically. This is analogous to anti-haecceitism in the presence of spacetime symmetries.

The concept of scaffold is widespread in science and increasingly common in evolutionary biology. While this concept figures in causal explanations, it is not clear what scaffolds are and what role they play in those explanations. Here we present evolutionary scaffolding explanation as a distinct type of explanatory strategy, distinguishing it from other types of evolutionary explanation. By doing so, we clarify the meaning of “scaffold” as a causal concept and its potential contribution to accounts of evolutionary novelty and major transitions.

Agent-based models (ABMs) are widely used to study how individual interactions shape collective behaviors. Critics argue that ABMs are often too simplistic to capture real-world complexities. We address this by integrating artificial neural networks into ABMs, focusing on enhancing the Hegselmann–Krause (HK) model. By using multilayer perceptrons as agents, we create more realistic ABMs that better reflect actual agents. This approach yields multiple models, as core elements of the HK model can be defined in various ways. We conduct two computational studies to compare these models with each other and with traditional individual-learning paradigms.

Describing a physical system in idealized terms involves making claims about the system that we know to be literally false. Because of this, it is not clear how calculations involving idealizations can generate justified belief and explain facts about the world. I argue that this puzzling aspect of idealizations cannot be explained away by talking about approximations, as is often supposed. I develop a different account of how justified beliefs and explanations can be generated from idealized descriptions of physical systems. My account involves a type of contextualism about the truth of mathematical descriptions of physical systems.