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I develop a constructivist account of the ecosystem health concept. I argue that plausible naturalist accounts of ecosystem health are unsuccessful—they do not accurately track and explain common judgments about ecosystem health. I show that specific values pertaining to aesthetics, authenticity, ...

In this article I briefly introduce a Hybrid Theory of Induction (HTI) and illustrate the kind of work it can do for us. I do so by exploring a particular kind of causal inference in comparative group studies from the perspective of the HTI. I show how the HTI provides a useful common framework to u...

Statistical analysis is often used to evaluate the strength of evidence for or against scientific hypotheses. Here we consider evidence measurement from the point of view of representational measurement theory, focusing in particular on the 0-points of measurement scales. We argue that a properly ca...

Adaptationism is often taken to be the thesis that most traits are adaptations. In order to assess this thesis, it seems we must be able to establish either an exhaustive set of all traits or a representative sample of this set. Either task requires a more systematic and principled way of individuat...

We study the epistemic success of different data collection strategies. We develop a computational multi-agent model of the scientific process that jointly formalizes its core aspects: data collection, data explanation, and social learning. We find that agents who choose new experiments at random de...

Causal models provide a promising framework for analyzing actual causation. Such analyses must include how a model should map onto the world. While universally endorsed that a model must be accurate – saying only true things – the implications of this aren’t explored. I argue that, surprisingl...

Shifts are a well-known feature of the literature on spacetime symmetries. Recently, discussions have focused on so-called dynamic shifts, which by analogy with static and kinematic shifts enact arbitrary linear accelerations of all matter (as well as a change in the gravitational potential). But in...

Like Austin’s “performatives”, some models are used not merely to represent, but also to change their targets. This paper argues that Austin’s analysis can inform model evaluation: if models are evaluated with respect to whether they are adequate for particular purposes (Parker 2020), and if...

Notions of fair machine learning that seek to control various kinds of error across protected groups generally are cast as constrained optimization problems over a fixed model class. For all such problems, tradeoffs arise: asking for various kinds of technical fairness requires compromising on overa...

If you can’t or don’t want to ascribe probabilities to the consequences of your actions, classic causal decision theory won’t let you reap the undeniable benefits of causal reasoning for decision making. The following theory fixes this problem. I explain why it’s good to have a causal decisi...

Bayesian approaches to human cognition have been extensively advocated in the last decades, but sharp objections have been raised too. We outline a diagnosis of what has gone wrong with prevalent strands of Bayesian cognitive science (pure Bayesian cognitive science), relying on selected illustratio...

It has been argued that possibilistic assessment of climate model output is preferable to probabilistic assessment (Stainforth et al. 2007; Betz 2010, 2015; Katzav 2014; Katzav et al. 2012 and 2021). I aim to articulate a variant of a possibilistic approach to such assessment. On my variant, the out...

Despite efforts to teach agents about their privilege by minimizing cost of information, Kinney & Bright argue risksensitive frameworks like Buchak's allow privileged agents to rationally shield themselves from this costless and relevant information. In response, I show that uncertainty about inform...

We examine arguments for and against the use of mechanistic evidence in assessing treatment efficacy and find that advocates of EBM+ and their critics have largely been talking past each other due to difference in focus. We explore aducanumab for the treatment of Alzheimer’s disease as a case whic...

Climate scientists frequently employ heavily idealized models. How should these models be interpreted? Some philosophers have promoted a possibilist interpretation, where climate models stand in for possible scenarios that could occur, but don't provide information about how probable those scenarios...

Breeds are classifications of domestic animals that share a set of conventional phenotypic traits. We claim that, despite classifying biological entities, animal breeds are social kinds. Breeds originate in a social mechanism (artificial selection) by which humans dominate the agency of certain anim...

The persistent pervasiveness of small studies in empirical fields is regularly deplored in scientific discussions. Taken individually, higher-powered studies are more likely to be truth-conducive. However, are they also beneficial for the wider performance of truth-seeking communities? We study the ...

I draw attention to an under-theorized problem for the application of machine learning models in science, which I call semantic opacity. Semantic opacity occurs when the knowledge needed to translate the output of an unsupervised model into scientific concepts depends on theoretical assumptions abou...

In this paper, I use a number of examples of multiscale modeling in biology to argue that the primary challenge facing these modelers is not how to metaphysically interpret their models, but is instead using various idealizations to bring the available multiscale modeling techniques to bear on the p...

Philosophical discussions of causal faithfulness have been predominantly situated within the social sciences—the traditional domain of application of the causal modeling techniques it attends. Recently, there has been increasing interest in applying such techniques to uncover causal relationships ...

In this essay I examine Roche and Sober’s (R&S) thesis that explanation is evidentially irrelevant, clarify the nodal points of disagreement, and defend explanationism. To do this, I utilize William Lycan’s categories of explanationism (2002) and a distinction between per se explanatoriness and ...

In this paper, I aim to develop a virtue epistemological account of scientific explanation and understanding. In so doing, I build a link between intellectual virtue and scientific explanation through understanding. The central epistemological question I will focus on is how human beings understand ...

The accuracy-first program attempts to ground epistemology in the norm that one’s beliefs should be as accurate as possible, where accuracy is measured using a scoring rule. We argue that considerations of scientific progress suggest that such a monism about epistemic value is untenable. In partic...

Recent work in the metaphysics of race that’s focused on the nature and reality of race as understood in the dominant race talk of current American English speakers—hereafter US race talk—has produced three main categories of race theories. Biological anti-realists—like Appiah (1992), Blum (...

Reciprocal causation is the view that adaptive evolution is a bidirectional process, whereby organisms and environments impinge on each other through cycles of niche construction and natural selection. I argue, however, that reciprocal causation is incompatible with the recent view that natural sele...

How should universities evaluate scientific research? This paper critically assesses the quantitative approach to the evaluation of scientific outputs based on publication metrics. First, I provide an overview of the standard indicators, such as Impact Factor and h-index. Secondly, I show that one l...

It is commonly believed that symmetry principles explain conservation laws. Since conservation laws can be mathematically derived from symmetries and vice versa, the explanatory asymmetry deserves philosophical justification. Marc Lange (2007) claims that symmetries are meta-laws that govern and hen...

I argue that thinking with good reason that a hypothesis $H$ is the best available explanation for some phenomenon does not entail that we are justified in believing $H$. Thus, inference to the best explanation does not in general give us justified belief. My argument is distinct from the so-called ...

Commonly in neuroscientific research today, scientists build models that can perform cognitive capacities and compare their activity with neuronal activity, with the purpose of learning about brain computations. These models are constrained only by the task they must perform. Therefore, it is a wort...

In discussions of the power of causal explanations, one often finds a commitment to two premises. The first is that, all else being equal, a causal explanation is powerful to the extent that it cites the full causal history of why the effect occurred. The second is that, all else being equal, causal...