The anthropic principle (AP) states that “what we can expect to observe must be restricted by the conditions necessary for our presence as observers”. But the phrase “our presence as observers” cannot be uniquely interpreted in the context of the theories within which AP is meant to be understood and applied: namely, for effective theories. We thus describe and defend a reformulation of AP, which we dub the effective observation principle (EOP). EOP describes what we can expect to observe in physical settings by considering our ‘observational situation’ (and not, specifically, ‘observers’)—understood solely in terms of effective theories.

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.

There are myriad techniques industry actors use to shape the public understanding of science. While a naive view might assume these techniques typically involve fraud or outright deception, the truth is more nuanced. This paper analyzes industrial distraction, a common technique where industry actors fund and share research that is accurate, often high-quality, but nonetheless misleading on important matters of fact. This involves reshaping causal understanding of phenomena with distracting information. Using case studies and causal models, we illustrate how this impacts belief and decision making even for rational learners, informing science policy and debates about misleading content.

I discuss the right to participate in science in UNDHR (1948) and ICESCR (1966). My goal is to clarify how fulfilling this right requires engaging with varieties of local knowledges that are too often severed in scientific narratives. I tease out three main varieties of local knowledges and highlight their distinctive features and intersectionalities. I argue that a more careful appreciation of varieties of local knowledges is key for the fulfilment of the right to participate in science and other human rights too, such as e.g. the right to food, right to clean water, and also so-called rights of nature.

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.

Since Cannon, inspired by Bernard’s discussion of the conditions required for free and independent life, introduced the term homeostasis, many have embraced it as the main theoretical principle guiding physiology and medicine. Nonetheless, critics have argued that homeostasis is too limiting and have advanced a variety of alternative concepts such as heterostasis, rheostasis, and allostasis. We argue that the critics target a much narrower understanding of homeostasis put forward by the cyberneticists and that Bernard and Cannon embraced a far broader understanding that can accommodate the alternatives advanced by the critics and provide an integrated theoretical framework for physiology.

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.

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.

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.

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.

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.