Much of Stephen Jay Gould’s legacy is dominated by his views on the contingency of evolutionary history expressed in his classic Wonderful Life. However, Gould also campaigned relentlessly for a “nomothetic” paleontology. How do these commitments hang together? I argue that Gould’s conception of science and natural law combined with his commitment to contingency to produce an evolutionary science centered around the formulation of higher-level evolutionary laws.

A long-running dispute concerns which adaptation-related explananda natural selection can be said to explain. (The issue is conceptual—not empirical—and orthogonal adaptationism.) At issue are explananda of the form: why a given individual organism has a given adaptation rather than that same individual having another trait. It is broadly agreed that one must be ready to back up a “no” answer with an appropriate theory of trans-world identity for individuals. I argue, against the conventional wisdom, that the same is true for a “yes” answer. My conclusion recasts the landscape and opens the door to a potential resolution.

Kaplan and Craver claim that all explanations in neuroscience appeal to mechanisms. They extend this view to the use of mathematical models in neuroscience and propose a constraint such models must meet in order to be explanatory. I analyze a mathematical model used to provide explanations in dynamical systems neuroscience and indicate how this explanation cannot be accommodated by the mechanist framework. I argue that this explanation is well characterized by Batterman’s account of minimal model explanations and that it demonstrates how relationships between explanatory models in neuroscience and the systems they represent is more complex than has been appreciated.

The concordance of results that are “robust” across multiple scientific modalities is widely considered to play a critical role in the epistemology of science. But what should we make of those cases where such multimodal evidence is discordant? Jacob Stegenga has recently argued that robustness is “worse than useless” in these cases, suggesting that “different kinds of evidence cannot be combined in a coherent way.” In this article I respond to this critique and illustrate the critical methodological role that robustness plays as an aim of scientific inquiry.

The discovery of the Higgs particle required a signal of 5σ significance. The rigid application of that condition is a convention that disregards more specific aspects of the given experiment. In particular, it does not account for the characteristics of the look elsewhere effect in the individual experimental context. The paper relates this aspect of data analysis to the question as to what extent theoretical reasoning should be admitted to play a role in the assessment of the significance of empirical data.

Jonah N. Schupbach and Jan Sprenger have proposed conditions of adequacy for measures of explanatory power. They derive and defend a measure of explanatory power satisfying their conditions of adequacy. This article furthers the development of their measure. The requirement that the measure be multidimensional analytic (in the sense of the mathematics of several complex variables) is avoided. Several proofs are simplified, and gaps in proofs are filled.

In a recent article, Carlos Santana shows that in common interest signaling games when signals are costly and when receivers can observe contextual environmental cues, ambiguous signaling strategies outperform precise ones and can, as a result, evolve. I show that if one assumes a realistic structure on the state space of a common interest signaling game, ambiguous strategies can be explained without appeal to contextual cues. I conclude by arguing that there are multiple types of cases of payoff-beneficial ambiguity, some of which are better explained by Santana’s models and some of which are better explained by models presented here.

In a recent paper, Sahotra Sarkar compares the Standard Dynamical interpretation of natural selection with the Information-Theoretic interpretation from Steven A. Frank. I address Sarkar’s three arguments against Frank’s interpretation. I show that Sarkar’s major argument that a key component of Frank’s account “does not have any natural biological interpretation” is premised on a contradiction stemming from a mathematical error. Consequently, Sarkar’s major argument is unsound. I also address Sarkar’s claim that a central equation in Frank’s interpretation is dynamically insufficient and his claim that Frank’s interpretation uses nonintuitive parameters to simply rewrite familiar results.