We present a dynamic model of the evolution of communication in a Lewis signaling game while systematically varying the degree of common interest between sender and receiver. We show that the level of common interest between sender and receiver is strongly predictive of the amount of information transferred between them. We also discuss a set of rare but interesting cases in which common interest is almost entirely absent, yet substantial information transfer persists in a “cheap talk” regime, and offer a diagnosis of how this may arise.

Neuroscience constrains psychology, but stating these constraints with precision is not simple. Here I consider whether mechanistic analysis provides a useful way to integrate models of cognitive and neural structure. Recent evidence suggests that cognitive systems map onto overlapping, distributed networks of brain regions. These highly entangled networks often depart from stereotypical mechanistic behaviors. While this casts doubt on the prospects for classical mechanistic integration of psychology and neuroscience, I argue that it does not impugn a realistic interpretation of either type of model. Cognitive and neural models may depict different, but equally real, causal structures within the mind/brain.

Debates on the contingency of history have largely focused on the history of life. This article targets the supposed contingency of human history. It does not defend a global claim about the overall contingency of history. Rather, it aims to identify and explain the difference between robust and fragile historical trajectories. It does so by considering a set of contrasting cases and identifying critical differences among the cases. The analysis shows that one important source of contingency is the historical emergence of command-and-control institutions; one important source of robustness is the existence of population-level processes structured by relatively stable institutions.

In a deterministically evolving world, the usefulness of nontrivial probabilities can seem mysterious. I use the ‘Mentaculus’ machinery developed by David Albert and Barry Loewer to show how all probabilities in such a world can be derived from a single, initial chance event. I go on to argue that this is the only genuine chance event. Perhaps surprisingly, we have good evidence of its existence and nature. I argue that the existence of this chance event justifies our epistemic reliance on nontrivial probabilities.

This article investigates and resolves the question whether gauge symmetry can display analogs of the famous Galileo’s ship scenario. In doing so, it builds on and clarifies the work of Greaves and Wallace (“Empirical Consequences of Symmetries”) on this subject.

We provide an explicit taxonomy of legitimate kinds of abstraction within constitutive explanation. We argue that abstraction is an inherent aspect of adequate mechanistic explanation. Mechanistic explanations—even ideally complete ones—typically involve many kinds of abstraction and therefore do not require maximal detail. Some kinds of abstraction play the ontic role of identifying the specific complex components, subsets of causal powers, and organizational relations that produce a suitably general phenomenon. Therefore, abstract constitutive explanations are both legitimate and mechanistic.

Color constancy is a foundational and yet puzzling phenomenon. Standard appearance invariantism is threatened by the psychophysical matching argument, which is taken to favor variantism. This argument, however, is inconclusive. The data at best support a pluralist view: color constancy is sometimes variantist, sometimes invariantist. I add another potential explanation of these data, complex invariantism, which adopts an atypical six-dimensional model of color appearance. Finally, I prospect for a unifying conception of constancy among two neglected notions: discriminatory color constancy and relational color constancy. The former arguably marks a common core capacity that is present across widely differing viewing contexts.

Michael and MacLeod’s paper on theories of reference for intentional concepts addresses neglected connections between theories of reference and Theory of Mind debates. Unfortunately, their paper neither shows the negative effects of descriptivism on theories of reference for intentional concepts nor provides an adequate picture of how the sort of theory they advocate might explain either the reference of intentional concepts or the puzzles of development on which they focus. In this article, I give reasons to think that the prospects are dim for applying causal approaches to the major topics raised by Michael and MacLeod.

A number of models of general relativity seem to contain “holes” that are thought to be “physically unreasonable.” One seeks a condition to rule out these models. We examine a number of possibilities already in use. We then introduce a new condition: epistemic hole-freeness. Epistemic hole-freeness is not just a new condition—it is new in kind. In particular, it does not presuppose a distinction between space-times that are “physically reasonable” and those that are not.

This article highlights the limitations of typicality accounts of thermodynamic behavior so as to promote an alternative line of research: understanding and accounting for the success of the techniques and equations physicists use to model the behavior of systems that begin away from equilibrium. This article also takes steps in this promising direction. It examines a technique commonly used to model the behavior of an important kind of system: a Brownian particle that has been introduced to an isolated fluid at equilibrium. It also accounts for the success of the model, by identifying and grounding the technique’s key assumptions.

In this article we argue for the importance of studying interdisciplinary collaborations by focusing on the role that good choice and design of model-building frameworks and strategies can play overcoming the inherent difficulties of collaborative research. We provide an empirical study of particular collaborations between economists and ecologists in resource economics. We discuss various features of how models are put together for interdisciplinary collaboration in these cases and show how the use of a coupled-model framework in this case to coordinate and combine background models from ecology and economics provided particular collaborative affordances and clear collaborative gain.

Floridi’s Theory of Strongly Semantic Information posits the Veridicality Thesis (i.e., information is true). One motivation is that it can serve as a foundation for information-based epistemology being an alternative to the tripartite theory of knowledge. However, the Veridicality thesis is false, if ‘information’ is to play an explanatory role in human cognition. Another motivation is avoiding the so-called Bar-Hillel/Carnap paradox (i.e., any contradiction is maximally informative). But this paradox only seems paradoxical, if (a) ‘information’ and ‘informativeness’ are synonymous, (b) logic is a theory of inference, or (c) validity suffices for rational inference; a, b, and c are false.

The canonical formalism of general relativity affords a particularly interesting characterization of the infamous hole argument. It also provides a natural formalism in which to relate the hole argument to the problem of time in classical and quantum gravity. Conceptual and formal inadequacies within the representative language of canonical gravity will be shown to be at the heart of both the canonical hole argument and the problem of time. Interesting and fruitful work at the interface of physics and philosophy relates to the challenge of resolving such inadequacies.

Network analysis is increasingly used to discover and represent the organization of complex systems. Focusing on examples from neuroscience in particular, I argue that whether network models explain, how they explain, and how much they explain cannot be answered for network models generally but must be answered by specifying an explanandum, by addressing how the model is applied to the system, and by specifying which kinds of relations count as explanatory.

I explain and assess here Huygens’s concept of relative motion. I show that it allows him to ground most of the Law of Inertia and also to explain rotation. Thereby his concept obviates the need for Newton’s absolute space. Thus, his account is a powerful foundation for mechanics, although not without some tension.

The orthodox characterization of spontaneous symmetry breaking (SSB) in statistical mechanics appeals to novel properties of systems with infinite degrees of freedom, namely, the existence of multiple equilibrium states. This raises the same puzzles about the status of the thermodynamic limit fueling recent debates about phase transitions. I argue that there are prospects of explaining the success of the standard approach to SSB in terms of the properties of large finite systems. Consequently, despite initial appearances, the need to account for SSB phenomena does not offer decisive support to claims about the explanatory and representational indispensability of the thermodynamic limit.

Here we address four objections raised by Julien Deonna, John Michael, and Francesca Fardo against a recent account of empathy for pain (by Frédérique de Vignemont and Tania Singer and Vignemont and Pierre Jacob). First, to what extent must the empathizer share her target’s affective state? Second, how can one interpret neuroscientific findings on vicarious pain in light of recent results challenging the notion of a pain matrix? Third, can one offer a simpler account of how empathy makes one aware of another’s emotion? Finally, to what extent can this account of empathy for pain be generalized to empathy for emotions?

Network neuroscience provides a systems approach to the study of the brain and enables the examination of interactions measured at different temporal and spatial scales. We review current methods to quantify the structure of brain networks and compare that structure across different clinical cohorts, cognitive states, and subjects. We further introduce the emerging mathematical concept of multilayer networks and describe the advantages of this approach to model changing brain dynamics over time. We conclude by offering several concrete examples of how multilayer network approaches to neuroimaging data provide novel insights into brain structure and evolving function.