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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, and human wellbeing help explain contemporary judg...

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 understand ongoing debates, methodological guidance...

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 calibrated evidence measure will need to count up fr...

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 individuating traits than is currently available. Moreover, ...

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 develop the most accurate accounts of the world. On ...

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, surprisingly, accuracy is not had by a model tout court, but ...

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 mathematical formulations of these shifts, the an...

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 performativity can sometimes be regarded as a mod...

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 overall error, and adding more protected groups increas...

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 decision theory that applies to non-deterministic yet no...

There are two problems in the history and philosophy of genetics that seem to be related. One is the problem of causal selection in cellular and developmental processes. The other is the general approach of seeking genetic explanations, characterized by Ken Waters (2006) as ‘gene centrism.’ I argue that to understand this connection, we must co...

Conflicting accounts of thought insertion share the assumption of realism: that the subject of thought insertion has a thought that corresponds to the description of her thought insertion episode. I argue against realism on the grounds that we should adopt a fictionalist, anti-realist interpretation of first-person thought insertion discourse. I th...

Several philosophers have argued that “constraints” constrain (and thereby explain) by virtue of being modally stronger than ordinary laws of nature. In this way, a constraint applies to all possible systems in a broader (i.e., more inclusive) sense of “possible” than the sense in play when we say that the ordinary laws of nature apply to a...

The precautionary principle is often put forward as potentially useful guide to avoiding catastrophe under conditions of uncertainty. But finding an adequate formulation of the principle runs into a problem when needed precautionary measures also have potentially catastrophic consequences – the imperative to avoid catastrophe appears to recommend...

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 illustrations from the psychology of reasoning and tools from...

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 output of climate models should typically be assessed...

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 information's relevance may block one from rationally up...

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 which may (eventually) speak to the role of EBM+ in ph...

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 are. The present paper argues that possibilism is...

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 animals about their reproductive choices. The stabilit...

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 impact of sample sizes on collective exploration d...

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 about the same domain of inquiry into which the model ...

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 phenomena of interest. This is particularly true wh...

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 biological systems. In this paper, I consider t...

Philosophers of science and scholars in science and technology studies have recently focused their attention on civic participation in research processes, commonly described as ``citizen/participatory science'' (Irwin 1995, Curtis 2018). This paper provides evidence from an exemplary case of such participation in applied fields of science and analy...

The astrophysical media are extremely (if not impossible) difficult to probe. Their chemical characterization can only be done through the analysis of the emission spectra registered by telescopes. Since the lines composing the spectra represent the emission energy between quantified and variously populated energy levels of a specific chemical spec...

Extant accounts of trust in science focus on reconciling scientific and public value judgments, but neglect the challenge of learning audience values. I argue that for scientific experts to be epistemically trustworthy, they should adopt a cooperative approach to learning about the values of their audience. A cooperative approach, in which expert a...

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 particular explanatoriness. These help show that e...

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 world through scientific explanation. The answ...

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 particular, we argue that counterexamples to the standar...

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 (2002), and Glasgow (2009)—have argued that, in U...

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 selection is a metaphysically emergent causal process....

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 limitation of the metrics system is that it lacks a...

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 hence explain conservation laws. In this paper, I arg...

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 `bad lot' argument, revolving instead around the c...

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 worthwhile scientific finding that the workings of the...

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 explanations are powerful to the extent that the ...

Scientism has recently experienced a resurgence of interest in philosophy. One version of scientism often defended is ontological scientism—the view that any kind or property not mentioned in the theories of science has only a subordinate, secondary kind of reality. It is worth noting that a dominant tradition in the history of philosophy of scie...

It has been argued that climate modeling can be partially characterized as exhibiting ontic competitive pluralism (i.e., that models compete for truth in some sense). I argue that (1) because climate models are all of the same model-type, they are not ontic competitors; instead (2) they compete in terms of local skill. Counterintuitively, locally p...

The technology of Machine Learning (ML), arguably, is one of the most significant general purpose technologies of our age. The appealing promise of machine learning is that it can take a given large corpus of “raw” data packaged up into a “dataset”, learn and discover various patterns, and derive putatively objective and reliable conclusion...