This article examines the role existence plays in measurement validity. I argue that existing popular theories of measurement and of validity follow a correspondence framework, which starts by assuming that an entity exists in the real world with certain properties that allow it to be measurable. Drawing on literature from the sociology of measurement, I show that the correspondence framework faces several theoretical and practical challenges. I suggest the validity-first framework of measurement, which starts with a practice-based validation process as the basis for a measurement theory and only posits objective existence when it is scientifically useful to do so.

We provide three innovations to recent debates about whether topological or “network” explanations are a species of mechanistic explanation. First, we more precisely characterize the requirement that all topological explanations are mechanistic explanations and show scientific practice to belie such a requirement. Second, we provide an account that unifies mechanistic and non-mechanistic topological explanations, thereby enriching both the mechanist and autonomist programs by highlighting when and where topological explanations are mechanistic. Third, we defend this view against some powerful mechanist objections. We conclude from this that topological explanations are autonomous from their mechanistic counterparts.

There is a persisting debate about what chemical bonds are and whether they exist. I argue that chemical bonds are real patterns of interactions between subatomic particles. This proposal resolves the problems raised in the context of existing understandings of the chemical bond and provides a novel way to defend the reality of chemical bonds.

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.

Bargaining games have played a prominent role in modeling the evolution of social conventions. Previous models assumed that agents must choose from a predetermined set of strategies. I present a new model of two agents learning in bargaining games in which new strategies must be invented and reinforced. I study the efficiency and fairness of the model outcomes. The outcomes are somewhat efficient, but a significant part of the resource is wasted nonetheless. I implement two forms of forgetting and restrictions to the set of strategies that can be invented.

Recent models of scientific collaboration show that minorities can end up at a disadvantage in bargaining scenarios. However, these models presuppose the existence of social categories. Here, I present a model of scientific collaboration in which inequality arises in the absence of social categories. I assume that all agents are identical except for the position that they occupy in the collaboration network. I show that inequality arises in the absence of social categories. I also show that this is due to the structure of the collaboration network and that similar patterns arise in two real-world collaboration networks.

Developing tools is a crucial aspect of experimental practice, yet most discussions of scientific change traditionally emphasize theoretical over technological change. To elaborate on the role of tools in scientific change, I offer an account that shows how scientists use tools in exploratory experiments to form novel concepts. I apply this account to two cases in neuroscience and show how tool development and concept formation are often intertwined in episodes of tool-driven change. I support this view by proposing common normative principles that specify when exploratory concept formation and tool development succeed (rather than fail) to initiate scientific change.

Various errors can affect scientific code and detecting them is a central concern within computational science. Could formal verification methods, which are now available tools, be widely adopted to guarantee the general reliability of scientific code? After discussing their benefits and drawbacks, we claim that, absent significant changes as regards features like their user-friendliness and versatility, these methods are unlikely to be adopted throughout computational science, beyond certain specific contexts for which they are well-suited. This issue exemplifies the epistemological heterogeneity of computational science: Profoundly different practices can be appropriate to meet the reliability challenge that rises for scientific code.

There has been much debate among scientists and philosophers about what it means for (hypothetical) interventions invoked in causal inference to be “well-defined” and how considerations of this sort should constrain the choice of causal variables. In this article, I propose that an intervention is well-defined just in case the effect of interest is well-defined, and that the intervention can serve as a suitable means to identify that effect. Based on this proposal, I identify several types of ambiguous intervention. Implications for variable choice are discussed using case studies drawn from the sciences.

Many philosophers of science have recently argued that extra-academic participation in scientific knowledge production does not threaten scientific objectivity. Quite the contrary: Citizen science, participatory projects, transdisciplinary research, and other similar endeavours can even increase the objectivity of the research conducted. Simultaneously, researchers working in fields in which such participation is common have expressed worries about various ways in which it can result in biases. In this article I clarify how these arguments and worries can be compared, and how extra-academic participation can both increase and threaten the objectivity of the research conducted.

I develop an account of ecological role functions—the functions of species within ecosystems—that is informed by alternative regime phenomena in ecology. My account is a causal-role theory that includes a counterfactual sensitivity condition. The account tracks and explains a distinction ecologists make between functions and various activities that are not functions. My counterfactual sensitivity condition resolves the liberality problem often attributed to causal-role theories of function while also illuminating the explanatory centrality of role functions within ecology.

The cosmological constant problem stems from treating quantum field theory and general relativity as an effective field theory (EFT). We argue that the problem is a reduction ad absurdum and that one should reject the assumption that general relativity can generically be treated as an EFT. This marks a failure of naturalness and provides an internal signal that EFT methods do not apply in all spacetime domains. We then take an external view, showing that the assumptions for using EFTs are violated in general relativistic domains where Λ is relevant. We highlight some ways forward that do not depend on naturalness.