Kin selection is a core aspect of social evolution theory, but a small number of critics have recently challenged it. Here I address these criticisms and show that kin selection remains an important explanation for much (though not all) social evolution. I show how many of the criticisms rest on historical idiosyncrasies of the way the field happened to develop, rather than on the real logic and evidence.

Hamilton’s theory of inclusive fitness is a widely used framework for studying the evolution of social behavior, but controversy surrounds its status. Hamilton originally derived his famous rb > c rule for the spread of a social gene by assuming additivity of costs and benefits. However, it has recently been argued that the additivity assumption can be dispensed with, so long as the −c and b terms are suitably defined, as partial regression coefficients. I argue that this way of generalizing Hamilton’s rule to the nonadditive case, while formally correct, faces conceptual problems.

Recognition of and responsiveness to the behavioral dispositions of others are key features of moral systems for facilitating social cooperation and the mediation of punishment. Here we investigate the coevolutionary possibilities of recognition and conditional social behavior with respect to both altruism and spite. Using two evolutionary models, we find that recognition abilities can support both spite and altruism but that some can only coevolve with spite. These results show that it is essential to consider harmful social behaviors as both a product of and an influence on the core features of our moral systems.

Using evolutionary game theory, I consider how guilt can provide individual fitness benefits to actors both before and after bad behavior. This supplements recent work by philosophers on the evolution of guilt with a more complete picture of the relevant selection pressures.

I pose problems for the views that human nature should be the object of study in the social and behavioral sciences and that a concept of human nature is needed to guide research in these sciences. I proceed by outlining three research programs in the social sciences, each of which confronts aspects of human variation. Next, I present Elizabeth Cashdan and Grant Ramsey’s related characterizations of human nature. I go on to argue that the research methodologies they each draw on are more productive resources for social scientists than their competing characterizations of human nature.

This article examines the adequacy of causal graph theory as a tool for modeling biological phenomena. I argue that the causal graph approach reaches its limits when it comes to modeling biological phenomena that involve complex spatial and chemical-structural relations. Using a case study from molecular biology, I show why causal graph models fail to adequately represent and explain biological phenomena of this kind. The inadequacy of these models is due to their failure to include relevant spatial-structural information in a way that does not render the models nonexplanatory, unmanageable, or inconsistent with basic assumptions of causal graph theory.

Mechanisms play an important role in many sciences when it comes to questions concerning explanation, prediction, and control. Answering such questions in a quantitative way requires a formal representation of mechanisms. Gebharter’s “A Formal Framework for Representing Mechanisms?” suggests to represent mechanisms by means of arrows in an acyclic causal net. In this article we show how this approach can be extended in such a way that it can also be fruitfully applied to mechanisms featuring causal feedback.

Mechanisms are usually viewed as hierarchical, with lower levels of a mechanism influencing, and decomposing, its higher-level behavior. To draw quantitative predictions from a model of a mechanism, the model must capture this hierarchical aspect. Recursive Bayesian networks (RBNs) were put forward by Lorenzo Casini et al. as a means to model mechanistic hierarchies by decomposing variables into their constituting causal networks. The proposal was criticized by Alexander Gebharter. He proposes an alternative formalism, which instead decomposes arrows. Here, I defend RBNs from the criticism and argue that they offer a better representation of mechanistic hierarchies than the rival account.

I examine to what extent accounts of mechanisms based on formal interventionist theories of causality can adequately represent biological mechanisms with complex dynamics. Using a differential equation model for a circadian clock mechanism as an example, I first show that there exists an iterative solution that can be interpreted as a structural causal model. Thus, in principle, it is possible to integrate causal difference-making information with dynamical information. However, the differential equation model itself lacks the right modularity properties for a full integration. A formal mechanistic model will therefore have to leave out either noncausal or causal explanatory relations.

Weak and strong consistency of the Abstract Principal Principle are defined in terms of classical probability measure spaces. It is proved that the Abstract Principal Principle is both weakly and strongly consistent. The Abstract Principal Principle is strengthened by adding a stability requirement to it. Weak and strong consistency of the resulting Stable Abstract Principal Principle are defined. It is shown that the Stable Abstract Principal Principle is weakly consistent. Strong consistency of the Stable Abstract Principal Principle remains an open question.

Rédei and Gyenis suggest that Lewis’s Principal Principle is meaningful only if it satisfies certain consistency conditions: starting from any assignment of credences to some algebra of events, we must always be able to extend our algebra with events as “the value of the objective chance of event E is p” and assign credences to such events in a consistent manner. I show that this extension is possible. However, I also argue that this requirement is unnecessary: the Principal Principle concerns subjective beliefs about objective chance; hence, events concerning those probabilities are meant to be in the algebra initially.

Is there more than one “Curie’s principle”? How far are different formulations legitimate? What are the aspects that make it so scientifically fruitful? This article is devoted to exploring these questions. We begin by discussing Curie’s original 1894 article. Then, we consider the way that the discussion of the principle took shape from early commentators to its modern form. We say why we think that the modern focus on the interstate version of the principle loses sight of some of the most significant applications of the principle. Finally, we address criticisms of the principle put forward by John Norton and Bryan Roberts.

Curie’s principle asserts that every symmetry of a cause manifests as a symmetry of the effect. It can be formulated as a tautology that is vacuous until it is instantiated. However, instantiation requires us to know the correct way to map causal terminology onto the terms of a science. Causal metaphysics has failed to provide a unique, correct way to carry out the mapping. Thus, successful or unsuccessful instantiation merely reflects our freedom of choice in the mapping.

From late 1684 through mid-1685, Isaac Newton turned to developing and refining the conceptual foundations presupposed by his emerging physics. Analysis of his manuscripts from this period reveals that Newton’s understanding of the relativity of acceleration led him to seek a spatiotemporally invariant quantity of matter. He found two such quantities and then designed an experiment to discover their relationship. Interpreting the experiment, however, required distinguishing a new notion of force. Others have recognized the conceptual distinction between inertial and gravitational mass. I show here that Newton clearly saw this distinction and that he provisionally established their equivalence.

I consider two usages of the expression “gauge theory.” On one, a gauge theory is a theory with excess structure; on the other, a gauge theory is any theory appropriately related to classical electromagnetism. I make precise one sense in which one formulation of electromagnetism, the paradigmatic gauge theory on both usages, may be understood to have excess structure and then argue that gauge theories on the second usage, including Yang-Mills theory and general relativity, do not generally have excess structure in this sense.

Gödel’s remarks concerning the ideality of time are examined. In the literature, some of these remarks have been somewhat neglected while others have been heavily criticized. In this article, I propose a clear and defensible sense in which Gödel’s work bears on the question whether there is an objective lapse of time in our world.

We make two points about dualities in string theory. The first point is that the conception of duality, which we will discuss, meshes with two dual theories being ‘gauge related’ in the general philosophical sense of being physically equivalent. The second point is a result about gauge/gravity duality that shows its relation to gauge symmetries to be subtler than one might expect: each of a certain class of gauge symmetries in the gravity theory, that is, diffeomorphisms, is related to a position-dependent symmetry of the gauge theory.

In this article I argue that structure in chemistry is a creature of abstraction: attending selectively to structural similarities, we neglect differences. There are different ways to abstract, so abstraction is interest dependent. So is structure. First, there are two different and mutually irreducible notions of structure in chemistry: bond structure and geometrical structure. Second, structure is relative to scale (of energy, time, and length): the same substance has different structures at different scales, and relationships of structural sameness and difference vary across the scales. However, these facts have no tendency to undermine structure’s claim to reality, or its metaphysical seriousness.

It is widely assumed that mechanistic explanations are causal explanations. Many prominent new mechanists endorse interventionism as the correct analysis of explanatory causal models in biology and other fields. This article argues that interventionism is not entirely satisfactory in this regard. A case study of Jacob and Monod’s operon model shows that at least some important mechanistic explanations in biology present significant contrasts with the interventionist account. This result motivates a more inclusive approach to mechanistic explanation, allowing for noncausal aspects.

What makes a measure of well-being valid? The dominant approach today, construct validation, uses psychometrics to ensure that questionnaires behave in accordance with background knowledge. Our first claim is interpretive—construct validation obeys a coherentist logic that seeks to balance diverse sources of evidence about the construct in question. Our second claim is critical—while in theory this logic is defensible, in practice it does not secure valid measures. We argue that the practice of construct validation in well-being research is theory avoidant, favoring a narrow focus on statistical tests while largely ignoring relevant philosophical considerations.