Although previous studies have acknowledged the connections between gender and emotional competences, more research is needed on how gender and emotion interact to influence psychosocial risks at work. This paper addresses how gender stereotypes and emotions simultaneously act as psychosocial antecedents of organizational stress. Following the principles of psychological androgyny, we propose that a combination of communion and agency can serve as a preventive factor at work and lead to healthier responses by providing a wider range of emotional competences to (...) deal with organizational demands. Following previous methodological approaches, we include a quantitative review about scientific research on occupational health in the PsycINFO database during the period 1980–2017 from a multidimensional gender perspective that differentiates between studies addressing the topic from either sex, gender or gender identity dimensions. Finally, we propose new analytical directions to deal with psychosocial hazards at work by underscoring some of the complex ways in which gender and emotional competences influence psychosocial risks at work. (shrink)

Bayesian nets are widely used in artificial intelligence as a calculus for causal reasoning, enabling machines to make predictions, perform diagnoses, take decisions and even to discover causal relationships. This book, aimed at researchers and graduate students in computer science, mathematics and philosophy, brings together two important research topics: how to automate reasoning in artificial intelligence, and the nature of causality and probability in philosophy.

Russo and Williamson put forward the following thesis: in order to establish a causal claim in medicine, one normally needs to establish both that the putative cause and putative effect are appropriately correlated and that there is some underlying mechanism that can account for this correlation. I argue that, although the Russo-Williamson thesis conflicts with the tenets of present-day evidence-based medicine, it offers a better causal epistemology than that provided by present-day EBM because it better explains two key aspects of (...) causal discovery. First, the thesis better explains the role of clinical studies in establishing causal claims. Second, it yields a better account of extrapolation. (shrink)

Michael Polanyi articulates two arguments against the view that moral judgment has no proper place in the conduct of political science: Non-judgmental political science cannot understand what it studies; and non-judgmental political science cannot understand the political scientist himself. Evaluation of these arguments not only clarifies important dimensions of Polanyi’s conceptions of understanding and tacit inference, it prompts a reconsideration of the nature of both moral deliberation and moral truth. The encounter with Polanyi demonstrates that non-judgmental political science does indeed (...) fall short of its stated objective. (shrink)

A normative Bayesian theory of deliberation and judgement requires a procedure for merging the evidence of a collection of agents. In order to provide such a procedure, one needs to ask what the evidence is that grounds Bayesian probabilities. After finding fault with several views on the nature of evidence (the views that evidence is knowledge; that evidence is whatever is fully believed; that evidence is observationally set credence; that evidence is information), it is argued that evidence is whatever is (...) rationally taken for granted. This view is shown to have consequences for an account of merging evidence, and it is argued that standard axioms for merging need to be altered somewhat. (shrink)

Among the traditional issues in philosophy that are directly affected by Michael Polanyi's revolutionary epistemology and its related ontology are nominalism and the question of universals. Polanyi's treatment of these matters is particularly fruitful, for it not only clarifies his conceptions of "tacit knowing" and "indwelling" but also illuminates his understanding of truth and reality and introduces us to his views on induction. Such inquiry will also demonstrate a deep affinity between Polanyi's position and that of Charles Sanders Peirce, while (...) at the same time illustrating how Polanyi joins a number of late nineteenth- and early to mid-twentieth-century philosophers in aiming to resolve central problems of .. (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)

Part I of this paper introduces a range of mechanistic theories of causality, including process theories and the complex-systems theories, and some of the problems they face. Part II argues that while there is a decisive case against a purely mechanistic analysis, a viable theory of causality must incorporate mechanisms as an ingredient, and describes one way of providing an analysis of causality which reaps the rewards of the mechanistic approach without succumbing to its pitfalls.

Recent work within psychology demonstrates that unconscious cognition plays a central role in the judgments and actions of individuals. We distinguish between two basic types unconscious social cognition: unconsciousness of the influences on judgments and actions, and unconscious of the mental states that give rise to judgments and actions. Influence unconsciousness is corroborated by strong empirical evidence, but unconscious states are difficult to verify. We discuss procedures aimed at providing conclusive evidence of state unconsciousness, and apply them to recent empirical (...) findings. (shrink)

I put forward several desiderata that a philosophical theory of causality should satisfy: it should account for the objectivity of causality, it should underpin formalisms for causal reasoning, it should admit a viable epistemology, it should be able to cope with the great variety of causal claims that are made, and it should be ontologically parsimonious. I argue that Nancy Cartwright’s dispositional account of causality goes part way towards meeting these criteria but is lacking in important respects. I go on (...) to argue that my epistemic account, which ties causal relationships to an agent’s knowledge and ignorance, performs well in the light of the desiderata. Such an account, I claim, is all we require from a theory of causality. (shrink)

The orthodox view in statistics has it that frequentism and Bayesianism are diametrically opposed—two totally incompatible takes on the problem of statistical inference. This paper argues to the contrary that the two approaches are complementary and need to mesh if probabilistic reasoning is to be carried out correctly.

It was the spring of 2020 when the email came to our inboxes—a memo from our Institutional Review Board informing us that we were approved to begin conducting our pragmatic clinical trial with a wa...

A Theory of Justice, by John Rawls, is widely regarded as the most important twentieth-century work of Anglo-American political philosophy. It transformed the field by offering a compelling alternative to the dominant utilitarian conception of social justice. The argument for this alternative is, however, complicated and often confusing. In this book Jon Mandle carefully reconstructs Rawls's argument, showing that the most common interpretations of it are often mistaken. For example, Rawls does not endorse welfare-state capitalism, and he is not a (...) 'luck egalitarian' as is widely believed. Mandle also explores the relationship between A Theory of Justice and the developments in Rawls's later work, Political Liberalism, as well as discussing some of the most influential criticisms in the secondary literature. His book will be an invaluable guide for anyone seeking to engage with this ground-breaking philosophical work. (shrink)

I present a formalism that combines two methodologies: objective Bayesianism and Bayesian nets. According to objective Bayesianism, an agent’s degrees of belief (i) ought to satisfy the axioms of probability, (ii) ought to satisfy constraints imposed by background knowledge, and (iii) should otherwise be as non-committal as possible (i.e. have maximum entropy). Bayesian nets offer an efficient way of representing and updating probability functions. An objective Bayesian net is a Bayesian net representation of the maximum entropy probability function.

Objective Bayesianism has been criticised for not allowing learning from experience: it is claimed that an agent must give degree of belief ½ to the next raven being black, however many other black ravens have been observed. I argue that this objection can be overcome by appealing to objective Bayesian nets, a formalism for representing objective Bayesian degrees of belief. Under this account, previous observations exert an inductive influence on the next observation. I show how this approach can be used (...) to capture the Johnson-Carnap continuum of inductive methods, as well as the Nix-Paris continuum, and show how inductive influence can be measured. (shrink)

Objective Bayesian probability is often defined over rather simple domains, e.g., finite event spaces or propositional languages. This paper investigates the extension of objective Bayesianism to first-order logical languages. It is argued that the objective Bayesian should choose a probability function, from all those that satisfy constraints imposed by background knowledge, that is closest to a particular frequency-induced probability function which generalises the λ = 0 function of Carnap’s continuum of inductive methods.

This book explores key ideas in Hannah Arendt's work through a study of her friendships with contemporaries: Heidegger, Karl Jaspers, Heinrich Blucher and Mary McCarthy.

This article argues for the importance of distinguishing the form of a theory of happiness from its content. It applies this distinction to ancient ethics, to show that almost all ancient philosophers subscribed to the same basic form or conception of happiness while differing over the details or content of happiness.

Hugo Grotius (1583-1645) [Hugo, Huigh or Hugeianus de Groot] was a towering figure in philosophy, law, political theory and associated fields during the seventeenth century and for hundreds of years afterwards. His work ranged over a wide array of topics, though he is best known to philosophers today for his contributions to the natural law theories of normativity which emerged in the later medieval and early modern periods. This article will attempt to explain his views on the law of nature (...) and related issues while simultaneously providing some broader assessment of his place in the history of ideas. (shrink)

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