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Objective Bayesianism is a methodological theory that is currently applied in statistics, philosophy, artificial intelligence, physics and other sciences. This book develops the formal and philosophical foundations of the theory, at a level accessible to a graduate student with some familiarity with mathematical notation.

§I schematises the evidence for an understanding of ‘know’ and other terms of epistemic appraisal that embodies contextualism or subject-sensitive invariantism, and distinguishes between those two approaches. §II argues that although the cases for contextualism and sensitive invariantism rely on a principle of charity in the interpretation of epistemic claims, neither approach satisfies charity fully, since both attribute metalinguistic errors to speakers. §III provides an equally charitable anti-sceptical insensitive invariantist explanation of much of the same evidence as the result of (...) psychological bias caused by salience effects. §IV suggests that the explanation appears to have implausible consequences about practical reasoning, but also that applications of contextualism or sensitive invariantism to the problem of scepticism have such consequences. §V argues that the inevitable difference between appropriateness and knowledge of appropriateness in practical reasoning, closely related to the difference between knowledge and knowledge of knowledge, explains the apparent implausibility. (shrink)

Can we turn the screw on counter-examples to the KK principle (that if one knows that P, one knows that one knows that P)? The idea is to construct cases in which one knows that P, but the epistemic status for one of the proposition that one knows that P is much worse than just one’s not knowing it. Of course, since knowledge is factive, there can’t be cases in which one knows that P and knows that one doesn’t know (...) that P (we can’t strengthen ¬KKp to K¬Kp)! If we can construct such cases, we may be able to use them to understand some puzzling epistemic phenomena. (shrink)

The paper criticizes epistemological conceptions of analytic or conceptual truth, on which assent to such truths is a necessary condition of understanding them. The critique involves no Quinean scepticism about meaning. Rather, even granted that a paradigmatic candidate for analyticity is synonymy with a logical truth, both the former and the latter can be intelligibly doubted by linguistically competent deviant logicians, who, although mistaken, still constitute counterexamples to the claim that assent is necessary for understanding. There are no analytic or (...) conceptual truths in the epistemological sense. The critique is extended to purportedly analytic inference rules. An alternative account is sketched on which understanding a word is a matter of participation in a linguistic practice, while synonymy and concept identity consist in sameness of truth-conditional semantic properties. Although there are philosophical questions about concepts, the idea that philosophical questions in general are conceptual questions generates only an illusion of insight into philosophical methodology. (shrink)

1. As John Hawthorne and Maria Lasonen-Aarnio appreciate, some of the central issues raised in their ‘Knowledge and Objective Chance’ arise for all but the most extreme theories of knowledge. In a wide range of cases, according to very plausible everyday judgments, we know something about the future, even though, according to quantum mechanics, our belief has a small but nonzero chance (objective probability) of being untrue. In easily constructed examples, we are in that position simultaneously with respect to many (...) different propositions about the future that are equiprobable and probabilistically independent of each other, at least to a reasonable approximation. (shrink)

It is tempting to analyse causality in terms of just one of the indicators of causal relationships, e.g., mechanisms, probabilistic dependencies or independencies, counterfactual conditionals or agency considerations. While such an analysis will surely shed light on some aspect of our concept of cause, it will fail to capture the whole, rather multifarious, notion. So one might instead plump for pluralism: a different analysis for a different occasion. But we do not seem to have lots of different concepts of cause (...) – just one eclectic notion. The resolution of this conundrum, I think, requires us to accept that our causal beliefs are generated by a wide variety of indicators, but to deny that this variety of indicators yields a variety of concepts of cause. This focus on the relation between evidence and causal beliefs leads to what I call epistemic causality. Under this view, certain causal beliefs are appropriate or rational on the basis of observed evidence; our notion of cause can be understood purely in terms of these rational beliefs. Causality, then, is a feature of our epistemic representation of the world, rather than of the world itself. This yields one, multifaceted notion of cause. (shrink)

Kyburg goes half-way towards objective Bayesianism. He accepts that frequencies constrain rational belief to an interval but stops short of isolating an optimal degree of belief within this interval. I examine the case for going the whole hog.

A theory of evidential probability is developed from two assumptions:(1) the evidential probability of a proposition is its probability conditional on the total evidence;(2) one's total evidence is one's total knowledge. Evidential probability is distinguished from both subjective and objective probability. Loss as well as gain of evidence is permitted. Evidential probability is embedded within epistemic logic by means of possible worlds semantics for modal logic; this allows a natural theory of higher-order probability to be developed. In particular, it is (...) emphasized that it is sometimes uncertain which propositions are part of one's total evidence; some surprising implications of this fact are drawn out. (shrink)

In conversations between native speakers, words such as ‘same’ and ‘identical’ do not usually cause much difficulty. We take it for granted that others use them with the same sense as we do. If it is unclear whether numerical or qualitative identity is intended, a brief gloss such as ‘one thing not two’ for the former or ‘exactly alike’ for the latter removes the unclarity. In this paper, numerical identity is intended. A particularly conscientious and logically aware speaker might explain (...) what ‘identical’ means in her. (shrink)

The full-text of this article is not currently available in ORA, but the original publication is available at springerlink.com.

After introducing a range of mechanistic theories of causality and some of the problems they face, I argue that while there is a decisive case against a purely mechanistic analysis, a viable theory of causality must incorporate mechanisms as an ingredient. I describe one way of providing an analysis of causality which reaps the rewards of the mechanistic approach without succumbing to its pitfalls.

Section 1 introduces the category of objective modality, closely related to linguists’ category of circumstantial or dynamic modals, and explains metaphysical modality as its maximal element. Section 2 discusses various kinds of skepticism about modality, as in Hume and recent authors, and argues that it is illmotivated to apply such skepticism to metaphysical modality but not to more restricted objective modalities, including nomic modality. Section 3 suggests that the role of counterfactual conditionals in applications of scientific theories involves an objective (...) modal dimension. Section 4 briefly discusses the role of objective probabilities in scientific theories as exemplifying the scientific study of objective modality. Section 5 summarizes a case study of dynamical systems theory, widely used in natural science, as a mathematical theory whose intended applications are objectively modal, as perspicuously articulated in a language with modal and temporal operators and propositional quantification. State spaces in natural science characterize objective possibilities. Section 6 argues that, although those possibilities are usually more restricted than metaphysical possibility, their scientific study is a partial study of metaphysical possibility too. (shrink)

This chapter argues for the importance of considering conceptual and normative commitments when addressing questions of responsible practice in data-intensive agricultural research and development. We consider genetic gain-focused plant breeding strategies that envision a data-intensive mode of breeding in which genomic, environmental and socio-economic data are mobilised for rapid crop variety development. Focusing on socio-economic data linkage, we examine methods of product profiling and how they accommodate gendered dimensions of breeding in the field. Through a comparison with participatory breeding methods, (...) we argue that the conceptual commitments underpinning current methods of integrating socioeconomic data into calculations of genetic gain can preclude the achievement of key social development goals, and that better engagement with participatory approaches can help address this problem. We conclude by identifying three key avenues towards a data-intensive approach to plant breeding that utilises the diverse sources of relevant evidence available, including socio-economic data, and maximises the chance of developing sustainable and responsible strategies and research practices in this domain: (1) reliable, long-term management of data infrastructures; (2) ongoing critical analysis of the conceptual foundations of specific strategies; and (3) regular transdisciplinary consultations including expertise in the social studies of agricultural science as well as participatory breeding techniques. (shrink)

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