Asian journal of philosophy最新文献

Rawls famously argues that the parties in the original position would agree upon the two principles of justice. Among other things, these principles guarantee equal political liberty—that is, democracy—as a requirement of justice. We argue on the contrary that the parties have reason to reject this requirement. As we show, by Rawls’ own lights, the parties would be greatly concerned to mitigate existential risk. But it is doubtful whether democracy always minimizes such risk. Indeed, no one currently knows which political systems would. Consequently, the parties—and we ourselves—have reason to reject democracy as a requirement of justice in favor of political experimentalism, a general approach to political justice which rules in at least some non-democratic political systems which might minimize existential risk.

According to approximate Bayesianism, Bayesian norms are ideal norms worthy of approximation for non-ideal agents. This paper discusses one potential challenge for approximate Bayesianism: in non-transparent learning situations—situations where the agent does not learn what they have or have not learnt—it is unclear that the Bayesian norms are worth satisfying, let alone approximating. I discuss two replies to this challenge and find neither satisfactory. I suggest that what transpires is a general tension between approximate Bayesianism and the possibility of “non-ideal” epistemic situations.

I make the case that what gets called epistemic paternalism isn’t correctly labelled as such. This mislabelling is problematic for two reasons. First, paternalism in general faces strong challenges to its permissibility. Second, the scope for action of epistemic paternalism is somewhat narrow given the typical concerns of applied epistemology. Having clarified epistemic paternalism and discussed the above considerations, this paper introduces epistemic benevolence. The case is made that the epistemic benevolence-based approach can avoid some of the strong challenges that epistemic paternalism faces and is often more suitable for the concerns of applied epistemology than a paternalist approach. To make these points, first, I make the case for a particular analysis of the epistemic paternalist act and, second, I explain epistemically benevolent acts as acts motivated by a concern for the epistemic welfare of another agent or agents. Given this characterisation, I argue that epistemic benevolence includes epistemic paternalism but extends beyond epistemic paternalism. I consider an objection to epistemic benevolence, which motivates laissez faire epistemic liberalism, a rival theoretical approach. Outlining this alternative serves to provide a further theoretical option relevant for work in applied epistemology. I offer some considerations as to why epistemic benevolence approach should still be favoured.

This paper is a brief comment on JeeLoo Liu’s interpretation of classical Chinese philosophy, especially Wang Yangming’s (1472–1529) views, in terms of pragmatist metaphysics primarily drawn from William James and Hilary Putnam. Liu’s reading of Wang Yangming both in the context of the Chinese tradition and in relation to pragmatism is most welcome as a novel contribution to comparative and intercultural philosophy. However, in such comparisons, we also have to be careful to avoid anachronistically attributing, e.g., Kantian and/or pragmatist ideas to earlier thinkers coming from a very different cultural context.

I discuss the epistemic status of algorithmic predictions in the legal realm. My main claim is that algorithmic predictions do not give us knowledge, not even probabilistic knowledge. The situation, however, is relevantly different from the one in which we find ourselves at the time of assessing statistical evidence in general, and it is rather related to the fact that algorithmic fairness in legal contexts is essentially undetermined. In the light of this, we have to settle for justified beliefs and justified credences. I end by drawing some morals for the Knowledge Norm of action.

Compersion is an affective state commonly discussed in the context of consensually non-monogamous relationships. It is typically described as a positive emotional reaction to one’s partner’s enjoying time and/or intimacy with another person, sort of ‘the opposite of jealousy’. Recent years have seen an increased interest in this seemingly startling emotion. Part of what makes understanding compersion so difficult is the mononormative expectations of our culture. We suggest that a non-Western, in particular Buddhist, perspective might be more helpful to understand that love and/or intimacy need not be an affair between two people only. We approach compersion through a Buddhist lens based on the ‘four immeasurables’, i.e. non-egocentric states that Buddhists take to promote well-being, and their ‘near enemies’, i.e. states which are easily conflated with them, but egocentric and harmful. Our goal is not to formulate a definition of compersion, nor to raise a normative bar for anyone who feels compersion, but to describe important facets of it that stand out more clearly against a Buddhist background than they might otherwise do. Such an approach not only enriches our understanding of compersion but contributes to people’s flourishing in all kinds of relationships and shows that non-monogamous relationships might be compatible with some forms of Buddhist practice.

It is widely held that transparency incentivizes good behavior. Though that may be, sometimes, there are tradeoffs here: transparency incentivizes people to conceal genuine reasons for action and instead manufacture insincere reasons for public consumption. The evidence for this comes from moral psychology and economics: when people are observed, they acquire an incentive to make more deontological and intuitive moral judgments than they would otherwise. In contrast, transparency incentivizes politicians and leaders to make more consequentialist and calculated moral judgments than they would otherwise. Transparency incentivizes people to foster distinct (inauthentic) moral identities—one personal and one public—or to make moral judgments based on reputational reasons that sometimes diverge from the moral facts. In either case, transparency can be morally and politically corrupting.

This article argues that heuristics play a key role in philosophy, in generating both our verdicts on proposed counterexamples to philosophical theories and philosophical paradoxes. Heuristics are efficient ways of answering questions, quick and easy to use, but imperfectly reliable. They have been studied by psychologists and cognitive scientists such as Gigerenzer and Kahneman, but their relevance to philosophical methodology has not been properly recognized. Several heuristics are discussed at length. The persistence heuristic can be summarized in the slogan ‘Small changes don’t matter’. Without it, updating would present an intractable problem for both natural and artificial intelligence. But our reliance on the persistence heuristic also makes us vulnerable to paradoxes of vagueness. Disquotational heuristics of various kinds are considered. They play central roles in our ascriptions of truth, falsity, and belief, but they also generate semantic paradoxes such as the Liar and Frege puzzles about coreference. The use of an additive heuristic for combining reasons is also discussed. Our reliance on fallible heuristics in philosophy does not make philosophical knowledge impossible, just as our reliance on fallible heuristics in perception does not make perceptual knowledge impossible. Nevertheless, it should motivate us to take a more critical attitude to our data. By identifying and analyzing the heuristics on which we rely, we may be able to work out where they make us most vulnerable to error.

I’ll argue that one particular argument of Nāgārjuna’s against causation in the Mūlamadhyamakakārikā deserves careful consideration from the perspective of contemporary western metaphysics. To show why this is the case, I’ll offer an interpretation of the key passages which differs from at least one popular reading. I’ll then aim to show that a whole swathe of metaphysical views about causation are problematic in light of Nāgārjuna’s argument, so interpreted. I’ll conclude, however, that one contemporary view in metaphysics has the means to respond to this argument: Ontic Structural Realism.

Jun Otsuka’s excellent book, Thinking about Statistics - the Philosophical Foundations (Otsuka 2023) is mostly organized around the idea that different statistical approaches can be illuminated by linking them to different ideas in general epistemology. Otsuka connects Bayesianism to internalism and foundationalism, frequentism to reliabilism, and the Akaike Information Criterion in model selection theory to instrumentalism. This useful mapping doesn’t cover all the interesting ideas he presents. His discussions of causal inference and machine learning are philosophically insightful, as is his idea that statisticians embrace an assumption that is similar to Hume’s Principle of the Uniformity of Nature. I discuss these topics in what follows, sometimes disagreeing with details while at other times adding ideas that complement those presented in the book.