Justification logics are constructive analogues of modal logics. They are often used as epistemic logics, particularly as models of evidentialist justification. However, in this role, justification (and modal) logics are defective insofar as they represent justification with a necessity-like operator, whereas actual evidentialist justification is usually probabilistic. This paper first examines and rejects extant candidates for solving this problem: Milnikel’s Logic of Uncertain Justifications, Ghari’s Hájek–Pavelka-Style Justification Logics and a version of probabilistic (...) justification logic developed by Kokkinis et al. It then proposes a new solution to the problem in the form of a justification logic that incorporates the essential features of both a fuzzy logic and a probabilistic logic. (shrink)

Recent work by Peijnenburg, Atkinson, and Herzberg suggests that infinitists who accept a probabilistic construal of justification can overcome significant challenges to their position by attending to mathematical treatments of infinite probabilistic regresses. In this essay, it is argued that care must be taken when assessing the significance of these formal results. Though valuable lessons can be drawn from these mathematical exercises (many of which are not disputed here), the essay argues that it is entirely unclear that (...) the form of infinitism that results meets a basic requirement: namely, providing an account of infinite chains of propositions qua reasons made available to agents. (shrink)

The aim of the paper is to develop general criteria of argumentative validity and adequacy for probabilistic arguments on the basis of the epistemological approach to argumentation. In this approach, as in most other approaches to argumentation, proabilistic arguments have been neglected somewhat. Nonetheless, criteria for several special types of probabilistic arguments have been developed, in particular by Richard Feldman and Christoph Lumer. In the first part (sects. 2-5) the epistemological basis of probabilistic arguments is discussed. With (...) regard to the philosophical interpretation of probabilities a new subjectivist, epistemic interpretation is proposed, which identifies probabilities with tendencies of evidence (sect. 2). After drawing the conclusions of this interpretation with respect to the syntactic features of the probability concept, e.g. one variable referring to the data base (sect. 3), the justification of basic probabilities (priors) by judgements of relative frequency (sect. 4) and the justification of derivative probabilities by means of the probability calculus are explained (sect. 5). The core of the paper is the definition of '(argumentatively) valid derivative probabilistic arguments', which provides exact conditions for epistemically good probabilistic arguments, together with conditions for the adequate use of such arguments for the aim of rationally convincing an addressee (sect. 6). Finally, some measures for improving the applicability of probabilistic reasoning are proposed (sect. 7). (shrink)

On explanationist accounts of genealogical debunking, roughly, a belief is debunked when its explanation is not suitably related to its content. We argue that explanationism cannot accommodate cases in which beliefs are explained by factors unrelated to their contents but are nonetheless independently justified. Justification-specific versions of explanationism face an iteration of the problem. The best account of debunking is a probabilistic account according to which subject S’s justification J for their belief that P is debunked when (...) S learns that J is no more likely to be true on the hypothesis that P than on the hypothesis that ¬P. The probabilistic criterion is fully general, applying not only to cases where the learned undercutting defeater is a proposition about our beliefs or other mental states but to any case of undercutting defeat, providing the grounds for a debunking argument against the existence of a special, metacognitive debunking principle. (shrink)

To understand something involves some sort of commitment to a set of propositions comprising an account of the understood phenomenon. Some take this commitment to be a species of belief; others, such as Elgin and I, take it to be a kind of cognitive policy. This paper takes a step back from debates about the nature of understanding and asks when this commitment involved in understanding is epistemically appropriate, or ‘acceptable’ in Elgin’s terminology. In particular, appealing to lessons from the (...) lottery and preface paradoxes, it is argued that this type of commitment is sometimes acceptable even when it would be rational to assign arbitrarily low probabilities to the relevant propositions. This strongly suggests that the relevant type of commitment is sometimes acceptable in the absence of epistemic justification for belief, which in turn implies that understanding does not require justification in the traditional sense. The paper goes on to develop a new probabilistic model of acceptability, based on the idea that the maximally informative accounts of the understood phenomenon should be optimally probable. Interestingly, this probabilistic model ends up being similar in important ways to Elgin’s proposal to analyze the acceptability of such commitments in terms of ‘reflective equilibrium’. (shrink)

Some random processes, like a series of coin flips, can produce outcomes that seem particularly remarkable or striking. This paper explores an epistemic puzzle that arises when thinking about these outcomes and asking what, if anything, we can justifiably believe about them. The puzzle has no obvious solution, and any theory of epistemic justification will need to contend with it sooner or later. The puzzle proves especially useful for bringing out the differences between three prominent theories; the probabilist theory, (...) the normic theory and a theory recently defended by Goodman and Salow. (shrink)

An agent’s belief in a proposition, E0, is justified by an infinite regress of deferred justification just in case the belief that E0 is justified, and the justification for believing E0 proceeds from an infinite sequence of propositions, E0, E1, E2, etc., where, for all n ≥ 0, En+1 serves as the justification for En. In a number of recent articles, Atkinson and Peijnenburg claim to give examples where a belief is justified by an infinite regress of (...) deferred justification. I argue here that there is no reason to regard Atkinson and Peijnenburg’s examples as cases where a belief is so justified. My argument is supported by careful consideration of the grounds upon which relevant beliefs are held within Atkinson and Peijnenburg’s examples. (shrink)

There is a long-standing debate in epistemology on the structure of justification. Some recent work in formal epistemology promises to shed some new light on that debate. I have in mind here some recent work by David Atkinson and Jeanne Peijnenburg, hereafter “A&P”, on infinite regresses of probabilistic support. A&P show that there are probability distributions defined over an infinite set of propositions {\ such that \ is probabilistically supported by \ for all i and \ has a (...) high probability. Let this result be “APR”. A&P oftentimes write as though they believe that APR runs counter to foundationalism. This makes sense, since there is some prima facie plausibility in the idea that APR runs counter to foundationalism, and since some prominent foundationalists argue for theses inconsistent with APR. I argue, though, that in fact APR does not run counter to foundationalism. I further argue that there is a place in foundationalism for infinite regresses of probabilistic support. (shrink)

In ‘A reductio of coherentism’ (Analysis 67, 2007) Tom Stoneham offers a novel argument against epistemological coherentism. ‘On the face of it’, he writes, ‘the argument gives a conclusive reductio ad absurdum of any coherence theory of justification. But that cannot be right, can it?’ (p. 254). It could be right, but it isn’t. I argue that coherentists need not accept the central premises of Stoneham’s argument and that, even if these premises were acceptable and true, Stoneham’s reductio would (...) not follow. (shrink)

Our main aims in this paper is to discuss and criticise the core thesis of a position that has become known as phenomenal conservatism. According to this thesis, its seeming to one that p provides enough justification for a belief in p to be prima facie justified (a thesis we label Standard Phenomenal Conservatism). This thesis captures the special kind of epistemic import that seemings are claimed to have. To get clearer on this thesis, we embed it, first, in (...) a probabilistic framework in which updating on new evidence happens by Bayesian conditionalization, and second, a framework in which updating happens by Jeffrey conditionalization. We spell out problems for both views, and then generalize some of these to non-probabilistic frameworks. The main theme of our discussion is that the epistemic import of a seeming (or experience) should depend on its content in a plethora of ways that phenomenal conservatism is insensitive to. (shrink)

Epistemic Utility Theory is often identified with the project of *axiology-first epistemology*—the project of vindicating norms of epistemic rationality purely in terms of epistemic value. One of the central goals of axiology-first epistemology is to provide a justification of the central norm of Bayesian epistemology, Probabilism. The first part of this paper presents a new challenge to axiology first epistemology: I argue that in order to justify Probabilism in purely axiological terms, proponents of axiology first epistemology need to justify (...) a claim about epistemic value—what I label ‘Downwards Propriety’—much stronger than any they have offered justification. The second part of this paper offers an argument that this challenge cannot be met: that there is no hope for providing a purely axiological justification of Downwards Propriety, at least given widely accepted assumptions about epistemic value. (shrink)

Why should we make our beliefs consistent or, more generally, probabilistically coherent? That it will prevent sure losses in betting and that it will maximize one’s chances of having accurate beliefs are popular answers. However, these justifications are self-centered, focused on the consequences of our coherence for ourselves. I argue that incoherence has consequences for others because it is liable to mislead others, to false beliefs about one’s beliefs and false expectations about one’s behavior. I argue that the moral obligation (...) of truthfulness thus constrains us to either conform to the logic our audience assumes we use, educate them in a new logic, or give notice that one will do neither. This does not show that probabilistic coherence is uniquely suited to making truthful communication possible, but I argue that classical probabilistic coherence is superior to other logics for maximizing efficiency in communication. (shrink)

Crispin Wright maintains that the architecture of perceptual justification is such that we can acquire justification for our perceptual beliefs only if we have antecedent justification for ruling out any sceptical alternative. Wright contends that this principle doesn’t elicit scepticism, for we are non-evidentially entitled to accept the negation of any sceptical alternative. Sebastiano Moruzzi has challenged Wright’s contention by arguing that since our non-evidential entitlements don’t remove the epistemic risk of our perceptual beliefs, they don’t actually (...) enable us to acquire justification for these beliefs. In this paper I show that Wright’s responses to Moruzzi are ineffective and that Moruzzi’s argument is validated by probabilistic reasoning. I also suggest that Wright cannot answer Moruzzi’s challenge without weakening the support available for his conception of the architecture of perceptual justification. (shrink)

In times of populist mistrust towards experts, it is important and the aim of the paper to ascertain the rationality of arguments from expert opinion and to reconstruct their rational foundations as well as to determine their limits. The foundational approach chosen is probabilistic. However, there are at least three correct probabilistic reconstructions of such argumentations: statistical inferences, Bayesian updating, and interpretive arguments. To solve this competition problem, the paper proposes a recourse to the arguments' justification strengths (...) achievable in the respective situation. (shrink)

Crispin Wright’s entitlement theory holds that we have non-evidential justification for accepting propositions of a general type––which Wright calls “cornerstones”––that enables us to acquire justification for believing other propositions––those that we take to be true on the grounds of ordinary evidence. Entitlement theory is meant by Wright to deliver a forceful response to the sceptic who argues that we cannot justify ordinary beliefs. I initially focus on strategic entitlement, which is one of the types of entitlement that Wright (...) has described in more detail. I suggest that it is dubious that we are strategically entitled to accept cornerstones. After this, I focus on entitlement in general. I contend that, in important cases, non-evidential justification for accepting cornerstones cannot secure evidential justification for believing ordinary propositions. My argument rests on a probabilistic regimentation of the so-called “leaching problem”. (shrink)

This chapter is concerned with the distinction that most contemporary epistemologists express by distinguishing between “propositional” and “doxastic” justification. The goal is to develop an account of this distinction that applies, not just to full or outright beliefs, but also to partial credences—and indeed, in principle, to attitudes of all kinds. The standard way of explaining this distinction, in terms of the “basing relation”, is criticized, and an alternative account—the “virtue manifestation” account—is proposed in its place. This account has (...) a certain kinship with the epistemological approach that is known as “reliabilism”, but it also has certain key differences—above all, it is compatible with an internalist view of rationality. (shrink)

Abstract The opening section outlines probabilism in the 20th century philosophy and shortly discusses the major accomplishments of Polish probabilist thinkers. A concise characterization of Bayesianism as the major recent form of probabilism follows. It builds upon the core personalist version of Bayesianism towards more objectively oriented versions thereof. The problem of a priori probability is shortly discussed. A tentative characterization of Kazimierz Ajdukiewicz’s standpoint regarding the inductive inference is cast in Bayesian terms. His objections against it presented in Pragmatic (...) Logic are presented. His 1958 paper on justification of non-deductive inference, as amply demonstrated by K. Szaniawski and I. Niiniluoto, extends his earlier Baysian position from 1928 monograph. In the closing section Ajdukiewicz’s standpoint is presented as a characteristically pragmatist and empiricist version of Bayesianism, which remains an unexplored and stimulating position. (shrink)

La philosophie contemporaine connaît une demi-douzaine de théories de la causalité. À l'époque de Kant et de Hume leur nombre a été moindre, à l'avenir on peut s'attendre à ce que leur nombre continue d'augmenter. Parmi les affirmations faites par ces théories sur la nature de la causalité, certaines sont compatibles entre elles, mais beaucoup ne le sont pas. Par conséquent, ou bien quelques-unes de ces théories sont fausses, ou bien elles ne portent pas sur le même objet. Dans ce (...) dernier cas, il y aurait plusieurs genres de causalité : différentes théories diraient quelque chose de correct à propos de différents genres de causalité. Dans cette situation, il apparaît judicieux de commencer par poser deux questions méta-théoriques : quelle est la tâche d'une théorie de la causalité, et quelles sont les données dont une telle théorie doit tenir compte ? Dans une portion considérable de la littérature philosophique sur la causalité, on cherche en vain une réponse claire à la question de savoir si le but de la théorie est l'explication du concept de causalité sous-jacent à notre pratique effective de jugement, ou au contraire sa révision. Cela va souvent de pair avec l'absence d'une justification du fait que la relation que la théorie se propose de définir peut bien être appelée relation causale. Certaines théories de la causalité hautement élaborées sur le plan technique, comme par exemple certaines variantes probabilistes de la théorie de la régularité où les théories plus récentes du transfert, donnent parfois l'impression que c'est la physique qui détient le monopole de définition en ce qui concerne la nature de la relation causale. Un représentant de la théorie du transfert affirme avec un pathos scientiste inébranlable „que la science physique a découvert la nature de la relation causale dans un large ensemble de cas“. Mais comment les sciences de la nature pourraient-elles découvrir quelque chose de ce genre ? La clarification de l'essence de la relation causale est une tâche philosophique par excellence qui relève plus particulièrement de la métaphysique. Le fait qu'il soit souvent nécessaire, au cours d'une recherche métaphysique, de faire appel à des connaissances scientifiques, ne change rien à l'affaire. Il s'agit d'une tache de métaphysique descriptive, que Strawson a caractérisée comme l'entreprise qui consiste à dégager les traits les plus généraux de la structure effective de notre pensée sur le monde. L'idiome causal est profondément ancré dans les langues naturelles ; et le concept de causalité que nous possédons effectivement se reflète dans notre pratique de jugements causaux. Cela ne signifie pas qu’il soit possible d'extraire directement de cette pratique un concept cohérent de causalité. Notre pratique de jugement possède de nombreux aspects et n'est pas dépourvue d'éléments douteux. Pour des besoins philosophiques, notre pratique effective de jugement a besoin d'une certaine discipline ; c'est en ce sens que je parle de la pratique éclairée de jugement causal. Je considère que c'est notre pratique éclairée de jugement causal qui fournit les données dont la théorie de la causalité doit tenir compte ; et la tâche primordiale d'une telle théorie consiste à indiquer les conditions de vérité de cas non controversés d'énoncés causaux singuliers. [...] . (shrink)

Philosophical reflection on racial profiling tends to take one of two forms. The first sees it as an example of ‘statistical discrimination,’ (SD), raising the question of when, if ever, probabilistic generalisations about group behaviour or characteristics can be used to judge particular individuals.(Applbaum 2014; Harcourt 2004; Hellman, 2014; Risse and Zeckhauser 2004; Risse 2007; Lippert-Rasmussen 2006; Lippert-Rasmussen 2007; Lippert-Rasmussen 2014) . This approach treats racial profiling as one example amongst many others of a general problem in egalitarian political (...) philosophy, occasioned by the fact that treating people as equals does not always require, or permit, us to treat them the same. The second form is concerned with how racial profiling illuminates the nature, justification, and reproduction of hierarchies of power and privilege based on skin colour and morphology. This form of reflection on racial profiling is therefore less about the justification for judging people based on the characteristics of the group to which they (appear to) belong, and more concerned with the specific ways in which the association of racialized minorities – and, in particular, black people – with crime, contributes to, and reflects, racial inequality, and oppression.(Kennedy 1998; Zack, 2015; Lever, 2005; Lever 2007). Both approaches to profiling have much to recommend them and, taken together, they form an essential component of the political philosophy of race. The statistical approach has the merits of linking racial profiling, as practice, to a body of other practices that generate and justify inequalities based on factors other than race, but it typically offers little by way of insight into the role of racial profiling itself in sustaining racial inequality and injustice. The racial construction approach, for obvious reasons, is rather better at the latter task, but its insights tend to come at the price of a broader understanding of the ways in which inequality is reproduced and justified, or of the ethical dilemmas raised by our competing claims to security. As we will see, insights from both approaches can be synthesized to clarify what, if anything, is wrong with racial profiling and what broader conclusions for equality and security follow from the study of profiling. (shrink)

The "myside bias'' in evaluating arguments is an empirically well-confirmed phenomenon that consists of overweighting arguments that endorse one's beliefs or attack alternative beliefs while underweighting arguments that attack one's beliefs or defend alternative beliefs. This paper makes two contributions: First, it proposes a probabilistic model that adequately captures three salient features of myside bias in argument evaluation. Second, it provides a Bayesian justification of this model, thus showing that myside bias has a rational Bayesian explanation under certain (...) conditions. (shrink)

The chapter explains why evolutionary genetics – a mathematical body of theory developed since the 1910s – eventually got to deal with culture: the frequency dynamics of genes like “the lactase gene” in populations cannot be correctly modeled without including social transmission. While the body of theory requires specific justifications, for example meticulous legitimations of describing culture in terms of traits, the body of theory is an immensely valuable scientific instrument, not only for its modeling power but also for the (...) amount of work that has been necessary to build, maintain, and expand it. A brief history of evolutionary genetics is told to demonstrate such patrimony, and to emphasize the importance and accumulation of statistical knowledge therein. The probabilistic nature of genotypes, phenogenotypes and population phenomena is also touched upon. Although evolutionary genetics is actually composed by distinct and partially independent traditions, the most important mathematical object of evolutionary genetics is the Mendelian space, and evolutionary genetics is mostly the daring study of trajectories of alleles in a population that explores that space. The ‘body’ is scientific wealth that can be invested in studying every situation that happens to turn out suitable to be modeled as a Mendelian population, or as a modified Mendelian population, or as a population of continuously varying individuals with an underlying Mendelian basis. Mathematical tinkering and justification are two halves of the mutual adjustment between the body of theory and the new domain of culture. Some works in current literature overstate justification, misrepresenting the relationship between body of theory and domain, and hindering interdisciplinary dialogue. (shrink)

I review recent empirical findings on knowledge attributions in lottery cases and report a new experiment that advances our understanding of the topic. The main novel finding is that people deny knowledge in lottery cases because of an underlying qualitative difference in how they process probabilistic information. “Outside” information is generic and pertains to a base rate within a population. “Inside” information is specific and pertains to a particular item’s propensity. When an agent receives information that 99% of all (...) lottery tickets lose (outside information), people judge that she does not know that her ticket will lose. By contrast, when an agent receives information that her specific ticket is 99% likely to lose (inside information), people judge that she knows that her ticket will lose. Despite this difference in knowledge judgments, people rate the likelihood of her ticket losing the exact same in both cases (i.e. 99%). The results shed light on other factors affecting knowledge judgments in lottery cases, including formulaic expression and participants’ own estimation of whether it is true that the ticket will lose. The results also undermine previous hypotheses offered for knowledge denial in lottery cases, including the hypotheses that people deny knowledge because they either deny justification or acknowledge a chance for error. (shrink)

This paper defines the form of prior knowledge that is required for sound inferences by analogy and single-instance generalizations, in both logical and probabilistic reasoning. In the logical case, the first order determination rule defined in Davies (1985) is shown to solve both the justification and non-redundancy problems for analogical inference. The statistical analogue of determination that is put forward is termed 'uniformity'. Based on the semantics of determination and uniformity, a third notion of "relevance" is defined, both (...) logically and probabilistically. The statistical relevance of one function in determining another is put forward as a way of defining the value of information: The statistical relevance of a function F to a function G is the absolute value of the change in one's information about the value of G afforded by specifying the value of F. This theory provides normative justifications for conclusions projected by analogy from one case to another, and for generalization from an instance to a rule. The soundness of such conclusions, in either the logical or the probabilistic case, can be identified with the extent to which the corresponding criteria (determination and uniformity) actually hold for the features being related. (shrink)

This book tells the story of modern ethics, namely the story of a discourse that, after the Renaissance, went through a methodological revolution giving birth to Grotius’s and Pufendorf’s new science of natural law, leaving room for two centuries of explorations of the possible developments and implications of this new paradigm, up to the crisis of the Eighties of the eighteenth century, a crisis that carried a kind of mitosis, the act of birth of both basic paradigms of the two (...) following centuries: Kantian ethics and utilitarianism. The new science of natural law carried a fresh start for ethics, resulting from a mixture of the Old and the New. It was, as suggested by Schneewind, an attempt at rescuing the content of Scholastic and Stoic doctrines on a new methodological basis. The former was the claim of existence of objective and universal moral laws; the latter was the self-aware attempt at justifying a minimal kernel of such laws facing skeptical doubt. What Bentham and Kant did was precisely carrying this strategy further on, even if restructuring it each of them around one out of two alternative basic claims. The nineteenth- and twentieth-century critics of the Enlightenment attacked both not on their alleged failure in carrying out their own projects, but precisely on having adopted Grotius’s and Pufendorf’s project. What counter-enlightenment has been unable to spell out is which alternative project could be carried out facing the modern condition of pluralism, while on the contrary, if we takes a closer look at developments in twentieth-century ethics or at on-going discussions on practical issues, we might feel inclined to believe that Grotius’s and Pufendorf’s project is as up-to-date as ever. -/- Table of Contents -/- Preface I. Fathers of the Reformation and Schoolmen 1.1. Luther: passive justice and the good deeds; 1.2. Calvin: voluntarism and predestination; 1.3. Baroque Scholasticism; 1.4. Casuistry and Institutiones morales -/- II Neo-Platonists, neo-Stoics, neo-Sceptics 2.1. Aristotelian, neo-Platonic, neo-Epicurean and neo-Cynic Humanists; 2.2. Oeconomica and the art of living; 2.3. Neo-Stoics; 2.4. Neo-Sceptics; 2.5. Moralistic literature -/- III Neo-Augustinians 3.l. The Jansenists on natura lapsa, sufficient grace, pure love; 3.2. Nicole on the impossibility of self-knowledge; 3.3. Nicole on self-love and charity; 3.4. Nicole against civic virtue, for Christian civility; 3.5. Malebranche on general laws and necessary evil; 3.6. Malebranche on Neo-Augustinianism and Platonism. -/- IV Grotius, Pufendorf and the new moral science 4.1. Grotius against Aristotle and the sceptics; 4.2. Mersenne and Gassendi; 4.3. Descartes on ethics as the last branch of philosophy’s tree; 4.4. Hobbes on scepticism and the new moral science; 4.5. Spinoza on the new moral science as a descriptive science;4.6. Locke on voluntarism and probabilism; 4.7. Pufendorf on natural law as an exact science; 4.8. Pufendorf on physical and moral entities; 10. Pufendorf on self-preservation -/- V The empiricist version of the new moral science: from Cumberland to Paley 5.1. Cumberland against Hobbesian voluntarism; 5.2. Cumberland and theological consequentialism; 5.3. Cumberland on universal benevolence and self-love; 5.4. Shaftesbury on the moral sense; 5.5. Hutcheson on natural law and moral faculties; 5.6. Gay, Brown, Paley and theological consequentialism. -/- VI The rationalist version of the new moral science: from Cudworth to Price 6.1. The Cambridge Platonists; 6.2. Shaftesbury on the moral sense; 6.3. Butler and a third way between voluntarism and scepticism; 6.4. Price and the rational character of moral truths; -/- VII Leibniz’s compromise between the new moral science and Aristotelianism 1.Leibniz against voluntarism; 2.Leibniz against the division between the physical and the moral good; 3.Leibniz on la place d’autrui and theological consequentialism; 4.Thomasius, Wolff, Crusius -/- VIII French eighteenth-century philosophers without the new moral science 8.1. The genealogy of our ideas of virtue and vice; 8.2. Maupertuis and moral arithmetic 8.3. The philosophes and the harmony of interests; 8.4. Rousseau on corruption, self-love, and virtue; 8.5. Sade on the merits of vice -/- IX Experimental moral science: Hume and Adam Smith 9.1. Mandeville’s paradox; 9.2. Hutcheson on the law of nature and moral faculties; 9.3. Hume on experimental moral philosophy and the intermediate principles; 9.4. Hume’s Law; 9.5. Hume on the fellow-feeling; 9.6. Hume on natural and artificial virtues and disinterested pleasure for utility; 9.7. Adam Smith’s anti-realist metaethics; 9.8. Adam Smith on self-deception and the paradox of happiness; 9.9. Adam Smith on sympathy and the impartial spectator; 9.10. Adam Smith on the twofold criterion for moral judgement and its paradox; 9.11. Reid on the refutation of scepticism and the self-evidence of duty -/- X Kantian ethics 10.1. Kantian metaethics: moral epistemology; 10.2. Kantian metaethics: moral ontology; 10.3. Kantian metaethics: moral psychology; 10.4. Kantian normative ethics; 10.5. Kant on the impracticability of applied ethics; 10.6. Kantian moral anthropology; 10.7. Civilisation and moralisation; 10.8. Theology on a moral basis and the origins of evil; 10.9. Fichte and the transformation of theoretical philosophy into practical philosophy XI Bentham and utilitarianism 11.1. Bentham’s linguistic theory; 11.2. Bentham’s moral ontology, psychology, and theory of action; 11.3. The principle of greatest happiness; 11.4. The critique of religious ethics; 11.5. The new morality; 11.6. Interest and duty; 11.7. Virtues; 11.8. Private ethics and legislation -/- XII Followers of the Enlightenment: liberal Judaism and Liberal Theology 12.1. Mendelssohn; 12.2. Salomon Maimon; 12.3. Haskalā and liberal Judaism; 12.4. Liberal Theology. -/- XIII Counter-Enlighteners 13.1.Romanticism and the fulfilment of individuality as the Summum Bonum; 13.2. Hegel on history as the making of liberty; 13.3. Hegel on the unhappy consciousness and the beautiful soul; 13.4. Hegel on Morality and Sittlichkeit; 13.5. Marx on ideology, alienation, and praxis; 13.6. Schopenhauer on compassion; 13.7. Kierkegaard on faith beyond ethics. -/- XIV Followers of the Enlightenment: intuitionists and utilitarian 14.1 Whewell‘s criticism of utilitarianism; 14.2 Whewell on morality and the philosophy of morality; 14.3 Whewell on the Supreme Norm; 14.4 Whewell on the conflict between duties; 14.5 Mill and the proof of the principle of utility; 14.6 Mill’s eudemonistic utilitarianism; 14.7 Mill on rules -/- XV Followers of the Enlightenment: neo-Kantians and positivists 15.1. French spiritualism; 15.2. Neo-Kantians: the Marburg school; 15.3. Neo-Kantians: the Marburg school; 15.4. Comte’s positivism and the invention of altruism; 15.5. Social Darwinism; 15.6. Wundt and an ethic of humankind -/- XVI Post-enlighteners: Sidgwick 16.1. Criticism of intuitionism; 16.2. On ethical egoism; 16.3. Criticism of utilitarianism -/- XVII Post-enlighteners: Durkheim 17.1. Sociology as physics of customs; 17.2. Morality as physics of customs and as practical science; 17.3. On Kantian ethics and utilitarianism; 17.4. The variability of moralities;17.5. Social solidarity as end and justification of morality; 17.6. Secular morality as “sociodicy”; XVIII Post-enlighteners: Nietzsche 18.1. On the Dionysian; 18.2. On the deconstruction of the world of values 18.3 On the twofold genealogy of moralities; 18.4. On ascetics and nihilism; 18.5. Normative ethics of self-fulfilment -/- Bibliography / Index of names / Index of concepts -/- . (shrink)

I argue that coherence is truth-conducive in that coherence implies an increase in the probability of truth. Central to my argument is a certain principle for transitivity in probabilistic support. I then address a question concerning the truth-conduciveness of coherence as it relates to (something else I argue for) the truth-conduciveness of consistency, and consider how the truth-conduciveness of coherence bears on coherentist theories of justification.

Whereas Bayesians have proposed norms such as probabilism, which requires immediate and permanent certainty in all logical truths, I propose a framework on which credences, including credences in logical truths, are rational because they are based on reasoning that follows plausible rules for the adoption of credences. I argue that my proposed framework has many virtues. In particular, it resolves the problem of logical omniscience.

Inferences from the absence of evidence to something are common in ordinary speech, but when used in scientific argumentations are usually considered deficient or outright false. Yet, as demonstrated here with the help of various examples, archaeologists frequently use inferences and reasoning from absence, often allowing it a status on par with inferences from tangible evidence. This discrepancy has not been examined so far. The article analyses it drawing on philosophical discussions concerning the validity of inference from absence, using (...) class='Hi'>probabilistic models that were originally developed to show that such inferences are weak and inconclusive. The analysis reveals that inference from absence can indeed be justified in many important situations of archaeological research, such as excavations carried out to explore the past existence and time-span of sedentary human habitation. The justification is closely related to the fact that archaeology explores the human past via its material remains. The same analysis points to instances where inference from absence can have comparable validity in other historical sciences, and to research questions in which archaeological inference from absence will be problematic or totally unwarranted. (shrink)

Can perceptual experiences be states of uncertainty? We might expect them to be, if the perceptual processes from which they're generated, as well as the behaviors they help produce, take account of probabilistic information. Yet it has long been presumed that perceptual experiences purport to tell us about our environment, without hedging or qualifying. Against this long-standing view, I argue that perceptual experiences may well occasionally be states of uncertainty, but that they are never probabilistically structured. I criticize a (...) powerful line of reasoning that we should expect perceptual experience to be probabilistic, given their interfaces with unconscious probabilistic information, with behavior responsive to it, and with credences. (shrink)

An aspect of Peirce’s thought that may still be underappreciated is his resistance to what Levi calls _pedigree epistemology_, to the idea that a central focus in epistemology should be the justification of current beliefs. Somewhat more widely appreciated is his rejection of the subjective view of probability. We argue that Peirce’s criticisms of subjectivism, to the extent they grant such a conception of probability is viable at all, revert back to pedigree epistemology. A thoroughgoing rejection of pedigree in (...) the context of probabilistic epistemology, however, _does_ challenge prominent subjectivist responses to the problem of the priors. (shrink)

This paper aims to open a dialogue between philosophers working in decision theory and operations researchers and engineers working on decision-making under deep uncertainty. Specifically, we assess the recommendation to follow a norm of robust satisficing when making decisions under deep uncertainty in the context of decision analyses that rely on the tools of Robust Decision-Making developed by Robert Lempert and colleagues at RAND. We discuss two challenges for robust satisficing: whether the norm might derive its plausibility from an implicit (...) appeal to probabilistic representations of uncertainty of the kind that deep uncertainty is supposed to preclude; and whether there is adequate justification for adopting a satisficing norm, as opposed to an optimizing norm that is sensitive to considerations of robustness. We discuss decision-theoretic and voting-theoretic motivations for robust satisficing, and use these motivations to select among candidate formulations of the robust satisficing norm. (shrink)

The aim of this study is to justify the belief that there are biological normative mechanisms that fulfill non-trivial causal roles in the explanations (as formulated by researchers) of actions and behaviors present in specific systems. One example of such mechanisms is the predictive mechanisms described and explained by predictive processing (hereinafter PP), which (1) guide actions and (2) shape causal transitions between states that have specific content and fulfillment conditions (e.g. mental states). Therefore, I am guided by a specific (...) theoretical goal associated with the need to indicate those conditions that should be met by the non-trivial theory of normative mechanisms and the specific models proposed by PP supporters. In this work, I use classical philosophical methods, such as conceptual analysis and critical reflection. I also analyze selected studies in the field of cognitive science, cognitive psychology, neurology, information theory and biology in terms of the methodology, argumentation and language used, in accordance with their theoretical importance for the issues discussed in this study. In this sense, the research presented here is interdisciplinary. My research framework is informed by the mechanistic model of explanation, which defines the necessary and sufficient conditions for explaining a given phenomenon. The research methods I chose are therefore related to the problems that I intend to solve. In the introductory chapter, “The concept of predictive processing”, I discuss the nature of PP as well as its main assumptions and theses. I also highlight the key concepts and distinctions for this research framework. Many authors argue that PP is a contemporary version of Kantianism and is exposed to objections similar to those made against the approach of Immanuel Kant. I discuss this thesis and show that it is only in a very general sense that the PP framework is neo-Kantian. Here we are not dealing with transcendental deduction nor with the application of transcendental arguments. I argue that PP is based on reverse engineering and abduction inferences. In the second part of this chapter, I respond to the objection formulated by Dan Zahavi, who directly accuses this research framework of anti-realistic consequences. I demonstrate that the position of internalism, present in the so-called conservative PP, does not imply anti-realism, and that, due to the explanatory role played in it by structural representations directed at real patterns, it is justified to claim that PP is realistic. In this way, I show that PP is a non-trivial research framework, having its subject, specific methods and its own epistemic status. Finally, I discuss positions classified as the socalled radical PP. In the chapter “Predictive processing as a Bayesian explanatory model” I justify the thesis according to which PP offers Bayesian modeling. Many researchers claim that the brain is an implemented statistical probabilistic network that is an approximation of the Bayesian 7 rule. In practice, this means that all cognitive processes are to apply Bayes' rule and can be described in terms of probability distributions. Such a solution arouses objections among many researchers and is the subject of wide criticism. The purpose of this chapter is to justify the thesis that Bayesian PP is a non-trivial research framework. For this purpose, I argue that it explains certain phenomena not only at the computational level described by David Marr, but also at the level of algorithms and implementation. Later in this chapter I demonstrate that PP is normative modeling. Proponents of the use of Bayesian models in psychology or decision theory argue that they are normative because they allow the formulation of formal rules of action that show what needs to be done to make a given action optimal. Critics of this approach emphasize that such thinking about the normativity of Bayesian modeling is unjustified and that science should shift from prescriptive to descriptive positions. In a polemic with Shira Elqayam and Jonathan Evans (2011), I show that the division they propose into prescriptivism and Bayesian descriptivism is apparent, because, as I argue, there are two forms of prescriptivism, i.e. the weak and the strong. I argue that the weak version is epistemic and can lead to anti-realism, while the strong version is ontic and allows one to justify realism in relation to Bayesian models. I argue that a weak version of prescriptivism is valid for PP. It allows us to adopt anti-realism in relation to PP. In practice, this means that you can explain phenomena using Bayes' rule. This does not, however, imply that they are Bayesian in nature. However, the full justification of realism in relation to the Bayesian PP presupposes the adoption of strong prescriptivism. This position assumes that phenomena are explained by Bayesian rule because they are Bayesian as such. If they are Bayesian in nature, then they should be explained using Bayesian modeling. This thesis will be substantiated in the chapters “Normative functions and mechanisms in the context of predictive processing” and “Normative mechanisms and actions in predictive processing”. In the chapter “The Free Energy Principle in predictive processing”, I discuss the Free Energy Principle (hereinafter FEP) formulated by Karl Friston and some of its implications. According to this principle, all biological systems (defined in terms of Markov blankets) minimize the free energy of their internal states in order to maintain homeostasis. Some researchers believe that PP is a special case of applying this principle to cognition, and that predictive mechanisms are homeostatic mechanisms that minimize free energy. The discussion of FEP is important due to the fact that some authors consider it to be important for explanatory purposes and normative. If this is the case, then FEP turns out to be crucial in explaining normative predictive mechanisms and, in general, any normative biological mechanisms. To define the explanatory possibilities of this principle, I refer to the discussion of its supporters on the issue they define as the problem of continuity and discontinuity between life and mind. A critical analysis of this discussion and the additional arguments I have formulated have allowed me to revise the explanatory ambitions of FEP. I also reject the belief that this principle is necessary to explain the nature of predictive mechanisms. I argue that the principle formulated and defended by Friston is an important research heuristic for PP analysis. 8 In the chapter “Normative functions and mechanisms in predictive processing”, I start my analyzes by formulating an answer to the question about the normative nature of homeostatic mechanisms. I demonstrate that predictive mechanisms are not homeostatic. I defend the view that a full explanation of normative mechanisms presupposes an explanation of normative functions. I discuss the most important proposals for understanding the normativity of a function, both from a systemic and teleosemantic perspective. I conclude that the non-trivial concept of a function must meet two requirements which I define as explanatory and normative. I show that none of the theories I have invoked satisfactorily meets both of these requirements. Instead, I propose a model of normativity based on Bickhard's account, but supplemented by a mechanistic perspective. I argue that a function is normative when: (1) it allows one to explain the dysfunction of a given mechanism; (2) it contributes to the maintenance of the organism's stability by shaping and limiting possible relations, processes and behaviors of a given system; and when (3) (according to the representational and predictive functions) it enables explaining the attribution of logical values of certain representations / predictions. In such an approach, a mechanism is normative when it performs certain normative functions and when it is constitutive for a specific action or behavior, despite the fact that for some reason it cannot realize it either currently or in the long-term. Such an understanding of the normativity of mechanisms presupposes the acceptance of the epistemic hypothesis. I argue that this hypothesis is not cognitively satisfactory, and therefore the ontic hypothesis should be justified, which is directly related to adopting the position of ontic prescriptivism. For this reason, referring to the mechanistic theory of scientific explanations, I formulate an ontical interpretation of the concept of a normative mechanism. According to this approach, a mechanism or a function is normative when they perform such and such causal roles in explaining certain actions and behaviors. With regard to the normative properties of predictive mechanisms and functions, this means that they are the causes of specific actions an organism carries out in the environment. In this way, I justify the necessity of accepting the ontic hypothesis and rejecting the epistemic hypothesis. The fifth chapter, “Normative mechanisms and actions in predictive processing”, is devoted to the dark room problem and the related exploration-exploitation trade-off. A dark room is the state that an agent could be in if it minimized the sum of all potential prediction errors. I demonstrate that, in accordance with the basic assumption of PP about the need for continuous and long-term minimization of prediction errors, such a state should be desirable for the agent. Is it really so? Many authors believe it is not. I argue that the test of the value of PP is the possibility of a non-trivial solution of this problem, which can be reduced to the choice between active and uncertainty-increasing exploration and safe and easily predictable exploitation. I show that the solution proposed by PP supporters present in the literature does not enable a fully satisfactory explanation of this dilemma. Then I defend the position according to which the full explanation of the normative mechanisms, and, subsequently, the solution to the dilemma of exploration and exploitation, involves reference to the existence of constraints present in the environment. The constraints 9 include elements of the environment that make a given mechanism not only causal but also normative. They are therefore key to explaining the predictive mechanisms. They do not only play the role of the context in which the mechanism is implemented, but, above all, are its constitutive component. I argue that the full explanation of the role of constraints in normative predictive mechanisms presupposes the integration of individual models of specific cognitive phenomena, because only the mechanistic integration of PP with other models allows for a non-trivial explanation of the nature of normative predictive mechanisms that would have a strong explanatory value. The explanatory monism present in many approaches to PP makes it impossible to solve the problem of the dark room. Later in this chapter, I argue that the Bayesian PP is normative not because it enables the formulation of such and such rules of action, but because the predictive mechanisms themselves are normative. They are normative because they condition the choice of such and such actions by agents. In this way, I justify the hypothesis that normative mechanisms make it possible to explain the phenomenon of agent motivation, which is crucial for solving the dark room problem. In the last part of the chapter, I formulate the hypothesis of distributed normativity, which assumes that the normative nature of certain mechanisms, functions or objects is determined by the relations into which these mechanisms, functions or objects enter. This means that what is normative (in the primary sense) is the relational structure that constitutes the normativity of specific items included in it. I suggest that this hypothesis opens up many areas of research and makes it possible to rethink many problems. In the “Conclusion”, I summarize the results of my research and indicate further research perspectives. (shrink)

The article critically discusses Walton’s (and co-authors’) argument scheme approach to good argumentation. Four characteristics of Walton’s approach are presented: 1. Argument schemes provide normative requirements. 2. These schemata are enthymematic. 3. There are associated critical questions. 4. The method is inductive, abstracting schemata from groups of similar arguments. Four adequacy conditions are applied to these characteristics: AC1: effectiveness in achieving the epistemic goal of obtaining and communicating justified acceptable opinions; AC2: completeness in capturing the good argument types; AC3: efficiency (...) in achieving the goals; AC4: justification of the argument schemes. The discussion reveals weaknesses in Walton’s account, including they are neither effective nor truly justified. A better alternative is an epistemological approach based on epistemological principles. (shrink)

If the goal of statistical analysis is to form justified credences based on data, then an account of the foundations of statistics should explain what makes credences justified. I present a new account called statistical reliabilism (SR), on which credences resulting from a statistical analysis are justified (relative to alternatives) when they are in a sense closest, on average, to the corresponding objective probabilities. This places (SR) in the same vein as recent work on the reliabilist justification of credences (...) generally [Dunn, 2015, Tang, 2016, Pettigrew, 2018], but it has the advantage of being action-guiding in that knowledge of objective probabilities is not required to identify the best-justified available credences. The price is that justification is relativized to a specific class of candidate objective probabilities, and to a particular choice of reliability measure. On the other hand, I show that (SR) has welcome implications for frequentist-Bayesian reconciliation, including a clarification of the use of priors; complemen- tarity between probabilist and fallibilist [Gelman and Shalizi, 2013, Mayo, 2018] approaches towards statistical foundations; and the justification of credences outside of formal statistical settings. Regarding the latter, I demonstrate how the insights of statistics may be used to amend other reliabilist accounts so as to render them action-guiding. I close by discussing new possible research directions for epistemologists and statisticians (and other applied users of probability) raised by the (SR) framework. (shrink)

Suppose several individuals (e.g., experts on a panel) each assign probabilities to some events. How can these individual probability assignments be aggregated into a single collective probability assignment? This article reviews several proposed solutions to this problem. We focus on three salient proposals: linear pooling (the weighted or unweighted linear averaging of probabilities), geometric pooling (the weighted or unweighted geometric averaging of probabilities), and multiplicative pooling (where probabilities are multiplied rather than averaged). We present axiomatic characterisations of each class of (...) pooling functions (most of them classic, but one new) and argue that linear pooling can be justified procedurally, but not epistemically, while the other two pooling methods can be justified epistemically. The choice between them, in turn, depends on whether the individuals' probability assignments are based on shared information or on private information. We conclude by mentioning a number of other pooling methods. (shrink)

How can different individuals' probability assignments to some events be aggregated into a collective probability assignment? Classic results on this problem assume that the set of relevant events -- the agenda -- is a sigma-algebra and is thus closed under disjunction (union) and conjunction (intersection). We drop this demanding assumption and explore probabilistic opinion pooling on general agendas. One might be interested in the probability of rain and that of an interest-rate increase, but not in the probability of rain (...) or an interest-rate increase. We characterize linear pooling and neutral pooling for general agendas, with classic results as special cases for agendas that are sigma-algebras. As an illustrative application, we also consider probabilistic preference aggregation. Finally, we compare our results with existing results on binary judgment aggregation and Arrovian preference aggregation. This paper is the first of two self-contained, but technically related companion papers inspired by binary judgment-aggregation theory. (shrink)

Are probabilism and special relativity compatible? Dieks argues that they are. But the possible universe he specifies, designed to exemplify both probabilism and special relativity, either incorporates a universal “now”, or amounts to a many world universe, or fails to have any one definite overall Minkowskian-type space-time structure. Probabilism and special relativity appear to be incompatible after all. What is at issue is not whether “the flow of time” can be reconciled with special relativity, but rather whether explicitly probabilistic (...) versions of quantum theory should be rejected because of incompatibility with special relativity. (shrink)

In this paper I expound an argument which seems to establish that probabilism and special relativity are incompatible. I examine the argument critically, and consider its implications for interpretative problems of quantum theory, and for theoretical physics as a whole.

The epistemic modal auxiliaries must and might are vehicles for expressing the force with which a proposition follows from some body of evidence or information. Standard approaches model these operators using quantificational modal logic, but probabilistic approaches are becoming increasingly influential. According to a traditional view, must is a maximally strong epistemic operator and might is a bare possibility one. A competing account—popular amongst proponents of a probabilisitic turn—says that, given a body of evidence, must \ entails that \\) (...) is high but non-maximal and might \ that \\) is significantly greater than 0. Drawing on several observations concerning the behavior of must, might and similar epistemic operators in evidential contexts, deductive inferences, downplaying and retractions scenarios, and expressions of epistemic tension, I argue that those two influential accounts have systematic descriptive shortcomings. To better make sense of their complex behavior, I propose instead a broadly Kratzerian account according to which must \ entails that \ = 1\) and might \ that \ > 0\), given a body of evidence and a set of normality assumptions about the world. From this perspective, must and might are vehicles for expressing a common mode of reasoning whereby we draw inferences from specific bits of evidence against a rich set of background assumptions—some of which we represent as defeasible—which capture our general expectations about the world. I will show that the predictions of this Kratzerian account can be substantially refined once it is combined with a specific yet independently motivated ‘grammatical’ approach to the computation of scalar implicatures. Finally, I discuss some implications of these results for more general discussions concerning the empirical and theoretical motivation to adopt a probabilisitic semantic framework. (shrink)

Conceptions of acceptability-based moral or political justification take it that authoritative acceptability constitutes, or contributes to, validity, or justification. There is no agreement as to what bar for authoritativeness such justification may employ. The paper engages the issue in relation to (i) the level of idealization that a bar for authoritativeness, ψ, imparts to a standard of acceptability-based justification, S, and (ii) the degree of discursive purchase of the discursive standing that S accords to people when (...) it builds ψ. I argue that (i) and (ii) are interdependent: high idealization values entail low discursive purchase, while high degrees of purchase require low idealization values. I then distinguish between alethic conceptions of justification that prioritize ends that commit to high idealization values, and recognitive conceptions that favour high discursive purchase. On this basis, I argue for a moderately recognitivist constraint on idealization. To render the recognitive discursive minimum available to relevant people at the site of justification, S should set ψ low enough so that it is a genuine option for actual people to reject relevant views in ways that S recognizes as authoritative. (shrink)

In mathematics, any form of probabilistic proof obtained through the application of a probabilistic method is not considered as a legitimate way of gaining mathematical knowledge. In a series of papers, Don Fallis has defended the thesis that there are no epistemic reasons justifying mathematicians’ rejection of probabilistic proofs. This paper identifies such an epistemic reason. More specifically, it is argued here that if one adopts a conception of mathematical knowledge in which an epistemic subject can know (...) a mathematical proposition based solely on a probabilistic proof, one is then forced to admit that such an epistemic subject can know several lottery propositions based solely on probabilistic evidence. Insofar as knowledge of lottery propositions on the basis of probabilistic evidence alone is denied by the vast majority of epistemologists, it is concluded that this constitutes an epistemic reason for rejecting probabilistic proofs as a means of acquiring mathematical knowledge. (shrink)

The best and most popular argument for probabilism is the accuracy-dominance argument, which purports to show that alethic considerations alone support the view that an agent’s degrees of belief should always obey the axioms of probability. I argue that extant versions of the accuracy-dominance argument face a problem. In order for the mathematics of the argument to function as advertised, we must assume that every omniscient credence function is classically consistent; there can be no worlds in the set of dominance-relevant (...) worlds that obey some other logic. This restriction cannot be motivated on alethic grounds unless we’re also willing to accept that rationality requires belief in every metaphysical necessity, as the distinction between a priori logical necessities and a posteriori metaphysical ones is not an alethic distinction. To justify the restriction to classically consistent worlds, non-alethic motivation is required. And thus, if there is a version of the accuracy-dominance argument in support of probabilism, it isn’t one that is grounded in alethic considerations alone. (shrink)

How can different individuals' probability functions on a given sigma-algebra of events be aggregated into a collective probability function? Classic approaches to this problem often require 'event-wise independence': the collective probability for each event should depend only on the individuals' probabilities for that event. In practice, however, some events may be 'basic' and others 'derivative', so that it makes sense first to aggregate the probabilities for the former and then to let these constrain the probabilities for the latter. We formalize (...) this idea by introducing a 'premise-based' approach to probabilistic opinion pooling, and show that, under a variety of assumptions, it leads to linear or neutral opinion pooling on the 'premises'. This paper is the second of two self-contained, but technically related companion papers inspired by binary judgment-aggregation theory. (shrink)

The explanatory role of natural selection is one of the long-term debates in evolutionary biology. Nevertheless, the consensus has been slippery because conceptual confusions and the absence of a unified, formal causal model that integrates different explanatory scopes of natural selection. In this study we attempt to examine two questions: (i) What can the theory of natural selection explain? and (ii) Is there a causal or explanatory model that integrates all natural selection explananda? For the first question, we argue that (...) five explananda have been assigned to the theory of natural selection and that four of them may be actually considered explananda of natural selection. For the second question, we claim that a probabilistic conception of causality and the statistical relevance concept of explanation are both good models for understanding the explanatory role of natural selection. We review the biological and philosophical disputes about the explanatory role of natural selection and formalize some explananda in probabilistic terms using classical results from population genetics. Most of these explananda have been discussed in philosophical terms but some of them have been mixed up and confused. We analyze and set the limits of these problems. (shrink)

I put forward a micro realistic, probabilistic version of quantum theory, which specifies the precise nature of quantum entities thus solving the quantum wave/particle dilemma, and which both reproduces the empirical success of orthodox quantum theory, and yields predictions that differ from orthodox quantum theory for as yet unperformed experiments.

We often have some reason to do actions insofar as they promote outcomes or states of affairs, such as the satisfaction of a desire. But what is it to promote an outcome? I defend a new version of 'probabilism about promotion'. According to Minimal Probabilistic Promotion, we promote some outcome when we make that outcome more likely than it would have been if we had done something (anything) else. This makes promotion easy and reasons cheap.

We investigate a basic probabilistic dynamic semantics for a fragment containing conditionals, probability operators, modals, and attitude verbs, with the aim of shedding light on the prospects for adding probabilistic structure to models of the conversational common ground.

Actual causes - e.g. Suzy's being exposed to asbestos - often bring about their effects - e.g. Suzy's suffering mesothelioma - probabilistically. I use probabilistic causal models to tackle one of the thornier difficulties for traditional accounts of probabilistic actual causation: namely probabilistic preemption.

Real human agents, even when they are rational by everyday standards, sometimes assign different credences to objectively equivalent statements, such as ‘Orwell is a writer’ and ‘E.A. Blair is a writer’, or credences less than 1 to necessarily true statements, such as not-yet-proven theorems of arithmetic. Anna Mahtani calls this the phenomenon of ‘opacity’. Opaque credences seem probabilistically incoherent, which goes against a key modelling assumption of probability theory. I sketch a modelling strategy for capturing opaque credence assignments without abandoning (...) probabilistic coherence. I draw on ideas from judgement-aggregation theory, where we face similar challenges of defining the ‘objects of judgement’. (shrink)

Sven Rosenkranz’s Justification as Ignorance shows how a strongly internalist conception of justification can be derived from a strongly externalist conception of knowledge, given an identification of justification with second-order ignorance and a set of structural principles concerning knowing and being in a position to know. Among these principles is an epistemic analogue of the Geach modal schema which states that one is always in a position to know that one doesn’t know p or in a position (...) to know that one doesn’t know not-p. Even suitably refined, the principle faces a range of counterexamples in which it misleadingly and persistently seems to one that one knows whether p without it seeming to one that one knows p nor that one knows not-p. These cases also threaten the book’s case for the luminosity of second-order ignorance, which is in turn central to its derivation of strongly internalist principles of justification. (shrink)

According to a standard assumption in epistemology, if one only partially believes that p , then one cannot thereby have knowledge that p. For example, if one only partially believes that that it is raining outside, one cannot know that it is raining outside; and if one only partially believes that it is likely that it will rain outside, one cannot know that it is likely that it will rain outside. Many epistemologists will agree that epistemic agents are capable of (...) partial beliefs in addition to full beliefs and that partial beliefs can be epistemically assessed along some dimensions. However, it has been generally assumed that such doxastic attitudes cannot possibly amount to knowledge. In Probabilistic Knowledge, Moss challenges this standard assumption and provides a formidable defense of the claim that probabilistic beliefs—a class of doxastic attitudes including credences and degrees of beliefs—can amount to knowledge too. Call this the probabilistic knowledge claim . Throughout the book, Moss goes to great lengths to show that probabilistic knowledge can be fruitfully applied to a variety of debates in epistemology and beyond. My goal in this essay is to explore a further application for probabilistic knowledge. I want to look at the role of probabilistic knowledge within a “knowledge-centered” psychology—a kind of psychology that assigns knowledge a central stage in explanations of intentional behavior. My suggestion is that Moss’s notion of probabilistic knowledge considerably helps further both a knowledge-centered psychology and a broadly intellectualist picture of action and know-how that naturally goes along with it. At the same time, though, it raises some interesting issues about the notion of explanation afforded by the resulting psychology. (shrink)