David Thorstad

Contemporary debate over laws of nature centers around Humean supervenience, the thesis that everything supervenes on the distribution of non-nomic facts. The key ingredient of this thesis is the idea that nomic-like concepts—law, chance, causation, etc.—are expressible in terms of the regularities of non-nomic facts. Inherent to this idea is the tacit conviction that regularities, “constant conjunctions” of non-nomic facts do supervene on the distribution of non-nomic facts. This paper raises a challenge for this conviction. It will be pointed out that the notion of regularity, understood as statistical correlation, has a necessary conceptual component not clearly identified before—I shall call this the “conjunctive relation” of the correlated events. On the other hand, it will be argued that there exists no unambiguous, non-circular way in which this relation could be determined. In this regard, the notion of correlation is similar to that of distant simultaneity where the necessary conceptual component is the one-way speed of light, whose value doesn’t seem to be determined by matters of facts.

Human beings are naturally inclined to inquire – this is much uncontroversial. However, recent philosophical discussions reveal that the nature of inquiry and the principles that should guide it remain subjects of significant debate. Namely, we lack a straightforward answer to foundational questions about what inquiry entails and how it ought to proceed. Rather, contemporary philosophical literature presents a range of contrasting perspectives on the descriptive and normative dimensions of inquiry, making it a topic of ongoing disagreement and controversy. This work engages with key questions arising from this debate: Is the mental state of suspended judgment essential for engaging in inquiry? Does the suspension of judgment exhibit an inquisitive character? What goal does inquiry aim to achieve? And what normative principles govern the rational conduct of our investigations? By addressing these questions, I aim to provide a fresh perspective on the nature and normativity of our inquiries.

True benevolence is, most fundamentally, a desire that the world be better. It is natural and common, however, to frame thinking about benevolence indirectly, in terms of a desire to make a difference to how good the world is. This would be an innocuous shift if desires to make a difference were extensionally equivalent to desires that the world be better. This paper shows that at least on some common ways of making a “desire to make a difference” precise, this extensional equivalence fails. Where it fails, “difference-making preferences” run counter to the ideals of benevolence. In particular, in the context of decision making under uncertainty, coupling a “difference-making” framing in a natural way with risk aversion leads to preferences that violate stochastic dominance, and that lead to a strong form of collective defeat, from the point of view of betterness. Difference-making framings and true benevolence are not strictly mutually inconsistent, but agents seeking to implement true benevolence must take care to avoid the various pitfalls that we outline.

Practical deliberation consists in thinking about what to do. Such deliberation is deemed rational when it conforms to certain normative requirements. What is often ignored is the role that an agent's context can play in so-called ‘failures’ of rationality. In this paper, I use recent cognitive science research investigating the effects of resource-scarcity on decision-making and cognitive function to argue that context plays an important role in determining which norms should structure an agent's deliberation. This evidence undermines the view that the norms of ‘ideal’ rationality are necessary and universal requirements on deliberation. They are a solution to the problems faced by cognitively limited agents in a context of moderate scarcity. In a context of severe scarcity, the problems faced by cognitively limited agents are different and require deliberation structured by different norms. Agents reason rationally when they use the norms best suited to their context and cognitive capacities.

What is the relationship between inquiry and epistemology? Are epistemic norms the norms that guide us as inquirers—as agents in the pursuit of knowledge and understanding? Recently, there has been growing support for what I, following Friedman (Philosophical Review 129(4):501–536, 2020), will call the zetetic turn in epistemology, the view that all epistemic norms are norms of inquiry. This paper investigates the prospects of an inquiry-centered approach to epistemology and develops several motivations for resisting it. First, I argue that the norms of inquiry are most plausibly seen as practical, not as distinctively epistemic. Second, I argue that a zetetically-grounded epistemology is unable to properly account for the rationality of belief. It fails to account for cases where intuitively irrational beliefs promote inquiry, or where intuitively rational beliefs are zetetically useless or counterproductive to inquiry. The main upshot is this: there must be a source of epistemic normativity that isn’t ultimately zetetic.

Deep learning is currently the most prominent and widely successful method in artificial intelligence. Despite having played an active role in earlier artificial intelligence and neural network research, philosophers have been largely silent on this technology so far. This is remarkable, given that deep learning neural networks have blown past predicted upper limits on artificial intelligence performance—recognizing complex objects in natural photographs and defeating world champions in strategy games as complex as Go and chess—yet there remains no universally accepted explanation as to why they work so well. This article provides an introduction to these networks as well as an opinionated guidebook on the philosophical significance of their structure and achievements. It argues that deep learning neural networks differ importantly in their structure and mathematical properties from the shallower neural networks that were the subject of so much philosophical reflection in the 1980s and 1990s. The article then explores several different explanations for their success and ends by proposing three areas of inquiry that would benefit from future engagement by philosophers of mind and science.