The precautionary principle is invoked in a number of important personal and policy-related decision contexts. Peterson shows that certain ways of making the principle precise are inconsistent with other criteria of decision making. Some object that the results do not apply to cases of deep uncertainty or value incommensurability, which are alleged to be in the principle’s wheelhouse. First, I show that Peterson’s impossibility results can be generalized considerably to cover cases of both deep uncertainty and incommensurability. Second, I contrast an alternative way of giving voice to the precautionary impulse.

This article first poses a skeptical challenge to clinical trials in medicine. The efficacy of treatments is measured against placebo, but placebo responses are not constant. They fluctuate with demographic variables, and they seem to be increasing over time. We therefore find ourselves measuring with the equivalent of what Wittgenstein termed an “elastic ruler.”

I then propose a “skeptical solution” to the problem. Elastic rulers are suitable tools for measuring dynamic, floating networks of values, like foreign currency exchanges. We can assuage the skeptical concerns by understanding clinical trials in this way; I suggest several practical guidelines for doing so.

In this paper I offer a model-theoretic interpretation of Autonomy Theory as defended by Moreno, Mossio, Montévil, and Bich. I address accusations that Autonomy Theory is excessively liberal, such as those made by Garson (2017), arguing that these misunderstand the role of strategic abstractions and generalizations in theory construction. Conceiving of closure of constraints as a model-building effort that emphasizes generality—in the spirit of Levins (1966)—also clarifies its potential for application in empirical contexts.

A widespread view has it that qualitative research in the social sciences is of poor quality because it lacks effective debiasing methods. In this paper, I zoom in on researcher bias in qualitative data collection. First, I provide a brief outline of qualitative data collection and develop a suitable account of bias. On that basis, I argue that qualitative researchers may mitigate researcher bias through the combined use of two strategies that capitalize on main characteristics of qualitative data collection. Lastly, I defend the two debiasing strategies against possible objections to their effectiveness.

Traditional wisdom dictates that statistical model outputs are estimates, not measurements. Despite this, statistical models are employed as measurement instruments in the social sciences. In this article, I scrutinize the use of a specific model—the logit model—for psychological measurement. Given the adoption of a criterion for measurement that I call comparability, I show that the logit model fails to yield measurements due to properties that follow from its fixed residual variance.

The article reevaluates the distinction between experiment and observation. It is first argued that to get clear on what role observation plays in the generation of scientific knowledge, we need to distinguish “experiential observation” as a concept closely connected to experience from “observation” in a technical sense and from “field observation” as a concept that reasonably contrasts with “experiment.” It is then argued that observation construed as field observation can enjoy systematic epistemic advantages over experiment, contrary to appearances.

Governments and social scientists are increasingly developing machine learning methods to automate the process of identifying terrorists in real time and predict future attacks. However, current operationalizations of “terrorist”’ in artificial intelligence are difficult to justify given three issues that remain neglected: insufficient construct legitimacy, insufficient criterion validity, and insufficient construct validity. I conclude that machine learning methods should be at most used for the identification of singular individuals deemed terrorists and not for identifying possible terrorists from some more general class, nor to predict terrorist attacks more broadly, given intolerably high risks that result from such approaches.

We examine whether the “privileged coordinates” of a geometric space encode its “amount of structure.” In doing so, we compare this coordinate approach to comparing amounts of structure to the more familiar automorphism approach. We first show that on a natural understanding of the former, it faces one of the same well-known problems as the latter. We then capture a precise sense in which the two approaches are closely related to one another, and we conclude by discussing whether they might still prove useful in cases of philosophical interest, despite their shortcomings.

Tarja Knuuttila and Mary Morgan recently challenged the widespread understanding that deidealization is no more than a simple process of relaxing assumptions to build increasingly more realistic models. They submit that, in practice, processes of model deidealization are diverse and complex and thus warrant more explicit scrutiny. Drawing on a case from economics, I extend their proposal by showing how narratives, as additional representational forms, can assume a crucial role in deidealizing mathematical models. I thereby propose to consider that processes of model deidealization are not necessarily exhausted by processes in which one mathematical model is replaced with another one.

The perception/action model posits distinct streams of visual processing for perception and online motor guidance. This model is apparently supported by experiments showing that visual illusions affect action tasks less than perception. In recent years, however, critics have argued against both the validity of these experiments and their support (irrespective of their validity) for the perception/action model. In this article, I reexamine this psychophysical evidence. I argue that it strongly supports the existence of distinct representations for “perception” and “action” but only moderately supports the existence of distinct systems generating those representations.

This article presents a novel sense in which theoretical structure has been preserved across the transition from classical to quantum physics. I import mathematical tools from category theory that have been used for structural comparisons in the context of theoretical equivalence and apply these tools to new situations involving theory change. The structural preservation takes the form of a categorical equivalence between categories of models of classical and quantum physics. I situate the significance of this structural preservation in terms of prospects for theory construction in quantum physics.

This article distinguishes causal modeling, metaphysical, epistemic, and modeling reasons for variable/parameter choice. I argue in favor of justifying variable/parameter choices by appealing to modeling reasons concerning the limitations of the available measurements, experimental data, modeling techniques, and modeling frameworks. I use this “tyranny of availability” to identify normative criteria for variable/parameter choice that apply across most scientific modeling contexts and investigate their metaphysical and epistemological implications.

We characterize four fruitful and underappreciated epistemic roles played by the concept of an individualized niche in contemporary biology, utilizing results of a qualitative empirical study conducted within an interdisciplinary biological research center. We argue that the individualized niche concept (1) shapes the research agenda of the center, (2) facilitates explaining core phenomena related to interindividual differences, (3) helps with managing individual-level causal complexity, and (4) promotes integrating local knowledge from ecology, evolutionary biology, behavioral biology, and other biological fields. We thereby also challenge arguments that the niche concept is superfluous in ecology.

There has been increased attention on how scientific communities should respond to spurious dissent. One proposed method is to hide such dissent by preventing its publication. To investigate this, I computationally model the epistemic effects of hiding dissenting evidence on scientific communities. I find that it is typically epistemically harmful to hide dissent, even when there exists an agent purposefully producing biased dissent. However, hiding dissent also allows for quicker correct epistemic consensus among scientists. Quicker consensus may be important when policy decisions must be made quickly, such as during a pandemic, suggesting times when hiding dissent may be useful.

In this article I issue a challenge to what I call the Independence Thesis of Theory Assessment (ITTA). According to ITTA, the evidence for (or against) a theory must be assessed independently from the theory explaining the evidence. I argue that ITTA is undermined by cases of evidential uncertainty, in which scientists have been guided by the explanatory power of their theories in the assessment of the evidence. Instead, I argue, these cases speak in favor of a model of theory assessment in which explanatory power may indeed contribute to the stabilization of the evidential basis.

Bayesian confirmation does not generally agglomerate over conjunction. That is, whenever a piece of evidence $E$ confirms two hypotheses ${H_1}$ and ${H_2}$ individually, it does not follow that $E$ also confirms them conjunctively. Here, I present a condition under which the latter does follow from the former. But this new condition reveals a surprising fact: Bayesian confirmation agglomerates over conjunction whenever the evidence in question also confirms that both target hypotheses are false.

Where do values live in thought? A straightforward answer is that we (or our brains) make decisions using explicit value representations which are our values. Recent work applying reinforcement learning to decision-making and planning suggests that, more specifically, we may represent both the instrumental expected value of actions as well as the intrinsic reward of outcomes. In this paper, I argue that identifying value with either of these representations is incomplete. For agents such as humans and other animals, there is another place where reward can be located in thought: the division of the space of possibilities or “state space.”

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.

In general relativity, the strong equivalence principle is underpinned by a geometrical account of fields on spacetime, by which all fields and bodies probe the same geometry. This geometric account implies that the parallel transport of all spacetime tensors and spinors is dictated by a single affine connection. No similar account of gauge theory is put forward by standard textbooks, which use principal bundles to coordinate the parallel transport of different, interacting particles. Nonetheless, here I argue that gauge theory does afford such a geometric account, obviating the need for principal bundles.