How do we go about weighing evidence, testing hypotheses, and making inferences? According to the model of _Inference to the Best Explanation_, we work out what to infer from the evidence by thinking about what would actually explain that evidence, and we take the ability of a hypothesis to explain the evidence as a sign that the hypothesis is correct. In _Inference to the Best Explanation_, Peter Lipton gives this important and influential idea the development and assessment it deserves. (...) The second edition has been substantially enlarged and reworked, with a new chapter on the relationship between explanation and Bayesianism, and an extension and defence of the account of contrastive explanation. It also includes an expanded defence of the claims that our inferences really are guided by diverse explanatory considerations, and that this pattern of inference can take us towards the truth. This edition of _Inference to the Best Explanation_ has also been updated throughout and includes a new bibliography. (shrink)

"How do we go about weighing evidence, testing hypotheses and making inferences? According to the model of 'inference to the Best explanation', we work out what to inter from the evidence by thinking about what would actually explain that evidence, and we take the ability of a hypothesis to explain the evidence as a sign that the hypothesis is correct. In inference to the Best Explanation, Peter Lipton gives this important and influential idea the development and assessment it deserves." (...) "The second edition has been substantially enlarged and reworked, with a new chapter on the relationship between explanation and Bayesianism, and an extension and defence of the account of contractive explanation. It also includes an expanded defence of the claims that our inferences really are guided by diverse explanatory considerations, and that this pattern of inference can take us towards the truth. This edition of Inference to the Best Explanation has also been updated throughout and incudes a new bibliography."--BOOK JACKET. (shrink)

Classic analysis of the subject and the development of personal probability; one of the greatest controversies in modern statistcal thought.

How do we go about weighing evidence, testing hypotheses, and making inferences? The model of " inference to the best explanation " -- that we infer the hypothesis that would, if correct, provide the best explanation of the available evidence--offers a compelling account of inferences both in science and in ordinary life. Widely cited by epistemologists and philosophers of science, IBE has nonetheless remained little more than a slogan. Now this influential work has been thoroughly revised and updated, and features (...) a new introduction and two new chapters. Inference to the Best Explanation is an unrivaled exposition of a theory of particular interest in the fields both of epistemology and the philosophy of science. (shrink)

Since Mueller’s 1958 article calling Hegelian dialectics a “legend,” it has been fashionable to deny that Hegel used thesis-antithesis-synthesis dialectics. But in truth, Hegel’s Phenomenology of Spirit has 28 dialectics hidden on four outline levels, and The Philosophy of History has 10 more on three outline levels. In Phenomenology’s macrodialectic, Hegel’s nonsupernatural Spirit–all reality, everything in the universe, including man and artificial objects–advances from unconscious + union (thesis) to conscious + separation (antithesis) to a synthesis of conscious (from the antithesis) (...) + union (from the thesis). Previous interpretations of Phenomenology have missed this dialectic: they have assumed that Spirit’s journey begins with consciousness, whereas the journey actually begins in the primordial state of nature, before man arrives and provides Spirit with its Mind (the collective mind of man). Mind then misperceives itself as a multitude of separate alien “objects”–things other than itself. The macrodialectic and all other dialectics are based on Christianity’s Johannine separation-and-return mythology; all Hegelian dialectics separate from a concept in the thesis, going to that something’s opposite (antithesis), and then return to the thesis concept. Thus, in Hegel’s master-and-slave dialectic (God = master, man = slave), man advances from potential + freedom to actual + bondage (religiosity) to actual + freedom (atheism). Only Marx and Tillich understood Hegelian dialectics. Marx’s basic dialectic (one of four) saw history separating from and returning to Communism, going from communal ownership + poverty (primitive communism, or gens) to private ownership + wealth (slavery, feudalism, capitalism) to communal ownership + wealth (final communism). Tillich’s basic dialectic (one of many) separates from and returns to God, advancing from Yes to God + Yes to supernaturalism (theism) to No to God + No to supernaturalism (atheism) to Yes to God + No to supernaturalism (humanism: humanity is the nonsupernatural “God above the God of theism”). (shrink)

[Peter Lipton] From a reliabilist point of view, our inferential practices make us into instruments for determining the truth value of hypotheses where, like all instruments, reliability is a central virtue. I apply this perspective to second-order inductions, the inductive assessments of inductive practices. Such assessments are extremely common, for example whenever we test the reliability of our instruments or our informants. Nevertheless, the inductive assessment of induction has had a bad name ever since David Hume maintained that any (...) attempt to justify induction by means of an inductive argument must beg the question. I will consider how the inductive justification of induction fares from the reliabilist point of view. I will also consider two other well-known arguments that can be construed as inductive assessments of induction. One is the miracle argument, according to which the truth of scientific theories should be inferred as the best explanation of their predictive success; the other is the disaster argument, according to which we should infer that all present and future theories are false on the grounds that all past theories have been found to be false. \\\ [John Worrall] Science seems in some ways to have been remarkably successful. What does this success tell us about the epistemological status of current scientific claims? Peter Lipton considers various meta-inductive arguments each of which start from premises about science's 'track record'. I show that his endorsements of the 'strongest' of these are, on analysis, remarkably weak. I argue that this is a reflection of difficulties within the general epistemological framework that he adopts-that of reliabilism. Finally, I briefly outline the quite different approach that I take to this issue, in the process responding to Lipton's criticisms of the 'pessimistic meta-induction'. (shrink)

From a reliabilist point of view, our inferential practices make us into instruments for determining the truth value of hypotheses where, like all instruments, reliability is a central virtue. I apply this perspective to second-order inductions, the inductive assessments of inductive practices. Such assessments are extremely common, for example whenever we test the reliability of our instruments or our informants. Nevertheless, the inductive assessment of induction has had a bad name ever since David Hume maintained that any attempt to justify (...) induction by means of an inductive argument must beg the question. I will consider how the inductive justification of induction fares from the reliabilist point of view. I will also consider two other well-known arguments that can be construed as inductive assessments of induction. One is the miracle argument, according to which the truth of scientific theories should be inferred as the best explanation of their predictive success; the other is the disaster argument, according to which we should infer that all present and future theories are false on the grounds that all past theories have been found to be false. \\\ [John Worrall] Science seems in some ways to have been remarkably successful. What does this success tell us about the epistemological status of current scientific claims? Peter Lipton considers various meta-inductive arguments each of which start from premises about science's 'track record'. I show that his endorsements of the 'strongest' of these are, on analysis, remarkably weak. I argue that this is a reflection of difficulties within the general epistemological framework that he adopts-that of reliabilism. Finally, I briefly outline the quite different approach that I take to this issue, in the process responding to Lipton's criticisms of the 'pessimistic meta-induction'. (shrink)

Five-year-old children categorized as skilled versus unskilled counters were given verbal estimation and number word comprehension tasks with numerosities 20 – 120. Skilled counters showed a linear relation between number words and nonsymbolic numerosities. Unskilled counters showed the same linear relation for smaller numbers to which they could count, but not for larger number words. Further tasks indicated that unskilled counters failed even to correctly order large number words differing by a 2 : 1 ratio, whereas they performed well on (...) this task with smaller numbers, and performed well on a nonsymbolic ordering task with the same numerosities. These findings provide evidence that large, approximate numerosity representations become linked to number words around the time that children learn to count to those words reliably. (shrink)

The author offers perspectives that can assist healthcare managers in achieving the highest ethical standards as they face their roles as healthcare providers, employers, and community service organizations. He also examines how to comply with relevant laws and regulations, provide high quality patient care with limited resources, and more.

According to an orthodox view, the capacity for conscious experience (sentience) is relevant to the distribution of moral status and value. However, physicalism about consciousness might threaten the normative relevance of sentience. According to the indeterminacy argument, sentience is metaphysically indeterminate while indeterminacy of sentience is incompatible with its normative relevance. According to the introspective argument (by François Kammerer), the unreliability of our conscious introspection undercuts the justification for belief in the normative relevance of consciousness. I defend the normative relevance (...) of sentience against these objections. First, I demonstrate that physicalists only have to concede a limited amount of indeterminacy of sentience. This moderate indeterminacy is in harmony with the role of sentience in determining moral status. Second, I argue that physicalism gives us no reason to expect that introspection is unreliable with respect to the normative relevance of consciousness. (shrink)

Science depends on judgments of the bearing of evidence on theory. Scientists must judge whether an observation or the result of an experiment supports, disconfirms, or is simply irrelevant to a given hypothesis. Similarly, scientists may judge that, given all the available evidence, a hypothesis ought to be accepted as correct or nearly so, rejected as false, or neither. Occasionally, these evidential judgments can be made on deductive grounds. If an experimental result strictly contradicts a hypothesis, then the truth of (...) the data deductively entails the falsity of the hypothesis. In the great majority of cases, however, the connection between evidence and hypothesis is non-demonstrative, or inductive. In particular, this is so whenever a general hypothesis is inferred to be correct on the basis of the available data, since the truth of the data will not deductively entail the truth of the hypothesis. It always remains possible that the hypothesis is false even though the data are correct. (shrink)

Analyzes the meaning expressed in music, the social and psychological sources of meaning, and the methods of musical communication This is a book meant for ...

Many researchers and intellectuals warn about extreme risks from artificial intelligence. However, these warnings typically came without systematic arguments in support. This paper provides an argument that AI will lead to the permanent disempowerment of humanity, e.g. human extinction, by 2100. It rests on four substantive premises which it motivates and defends: first, the speed of advances in AI capability, as well as the capability level current systems have already reached, suggest that it is practically possible to build AI systems (...) capable of disempowering humanity by 2100. Second, due to incentives and coordination problems, if it is possible to build such AI, it will be built. Third, since it appears to be a hard technical problem to build AI which is aligned with the goals of its designers, and many actors might build powerful AI, misaligned powerful AI will be built. Fourth, because disempowering humanity is useful for a large range of misaligned goals, such AI will try to disempower humanity. If AI is capable of disempowering humanity and tries to disempower humanity by 2100, then humanity will be disempowered by 2100. This conclusion has immense moral and prudential significance. (shrink)

This paper considers a central objection to evolutionary epistemology. The objection is that biological and epistemic development are not analogous, since while biological variation is blind, epistemic variation is not. The generation of hypotheses, unlike the generation of genotypes, is not random. We argue that this objection is misguided and show how the central analogy of evolutionary epistemology can be preserved. The core of our reply is that much epistemic variation is indeed directed by heuristics, but these heuristics are analogous (...) to biological preadaptations which account for the evolution of complex organs. We also argue that many of these heuristics or epistemic preadaptations are not innate but were themselves generatedby a process of blind variation and selective retention. (shrink)

According to a causal model of explanation, we explain phenomena by giving their causes or, where the phenomena are themselves causal regularities, we explain them by giving a mechanism linking cause and effect. If we explain why smoking causes cancer, we do not give the cause of this causal connection, but we do give the causal mechanism that makes it. The claim that to explain is to give a cause is not only natural and plausible, but it also avoids many (...) of the objections to other accounts of explanation, such as the views that to explain is to give a reason to believe the phenomenon occurred, to somehow make the phenomenon familiar, or to give a Deductive-Nomological argument. Unlike the reason for belief account, a causal model makes a clear distinction between understanding why a phenomenon occurs and merely knowing that it does, and the model does so in a way that makes understanding unmysterious and objective. Understanding is not some sort of super-knowledge, but simply more knowledge: knowledge of the phenomenon and knowledge of its causal history. A causal model makes it clear how something can explain without itself being explained, and so avoids the regress of whys, since we can know a phenomenon's cause without knowing the cause of the cause. It also accounts for legitimate self-evidencing explanations, explanations where the phenomenon is an essential part of the evidence that the explanation is correct, so the explanation can not supply a non-circular reason for believing the phenomenon occurred. There is no barrier to knowing a cause through its effects and also knowing that it is their cause. The speed of recession of a star explains its observed red-shift, even though the shift is an essential part of the evidence for its speed of recession. The model also avoids the most serious objection to the familiarity view, which is that some phenomena are familiar yet not understood, since a phenomenon can be perfectly familiar, such as the blueness of the sky or the fact that the same side of the moon always faces the earth, even if we do not know its cause. Finally, a causal model avoids many of the objections to the Deductive-Nomological model. Ordinary explanations do not have to meet the requirements of the Deductive-Nomological model, because one does not need to give a law to give a cause, and one does not need to know a law to have good reason to believe that a cause is a cause. As for the notorious over-permissiveness of the Deductive-Nomological model, the reason recession explains red-shift but not conversely, is simply that causes explain effects but not conversely, and the reason a conjunction of laws does not explain its conjuncts is that conjunctions do not cause their conjuncts. (shrink)

Which artificial intelligence (AI) systems are agents? To answer this question, I propose a multidimensional account of agency. According to this account, a system's agency profile is jointly determined by its level of goal-directedness and autonomy as well as is abilities for directly impacting the surrounding world, long-term planning and acting for reasons. Rooted in extant theories of agency, this account enables fine-grained, nuanced comparative characterizations of artificial agency. I show that this account has multiple important virtues and is more (...) informative than alternatives. More speculatively, it may help to illuminate two important emerging questions in AI ethics: 1. Can agency contribute to the moral status of non-human beings, and how? 2. When and why might AI systems exhibit power-seeking behaviour and does this pose an existential risk to humanity? (shrink)

The science of animal consciousness investigates (i) which animal species are conscious (the distribution question) and (ii) how conscious experience differs in detail between species (the quality question). We propose a framework which clearly distinguishes both questions and tackles both of them. This two-tier account distinguishes consciousness along ten dimensions and suggests cognitive capacities which serve as distinct operationalizations for each dimension. The two-tier account achieves three valuable aims: First, it separates strong and weak indicators of the presence of consciousness. (...) Second, these indicators include not only different specific contents but also differences in the way particular contents are processed (by processes of learning, reasoning or abstraction). Third, evidence of consciousness from each dimension can be combined to derive the distinctive multi-dimensional consciousness profile of various species. Thus, the two-tier account shows how the kind of conscious experience of different species can be systematically compared. (shrink)

Science depends on judgments of the bearing of evidence on theory. Scientists must judge whether an observation or the result of an experiment supports, disconfirms, or is simply irrelevant to a given hypothesis. Similarly, scientists may judge that, given all the available evidence, a hypothesis ought to be accepted as correct or nearly so, rejected as false, or neither. Occasionally, these evidential judgments can be make on deductive grounds. If an experimental result strictly contradicts a hypothesis, then the truth of (...) the evidence deductively entails the falsity of the hypothesis. In the great majority of cases, however, the connection between evidence and hypothesis is nondemonstrative or inductive. In particular, this is so whenever a general hypothesis is inferred to be correct on the basis of the available data, since the truth of the data will not deductively entail the truth of the hypothesis. It always remains possible that the hypothesis is false even though the data are correct. (shrink)

Inspired by recent breakthroughs in predictive modeling, practitioners in both industry and government have turned to machine learning with hopes of operationalizing predictions to drive automated decisions. Unfortunately, many social desiderata concerning consequential decisions, such as justice or fairness, have no natural formulation within a purely predictive framework. In efforts to mitigate these problems, researchers have proposed a variety of metrics for quantifying deviations from various statistical parities that we might expect to observe in a fair world and offered a (...) variety of algorithms in attempts to satisfy subsets of these parities or to trade o the degree to which they are satised against utility. In this paper, we connect this approach to fair machine learning to the literature on ideal and non-ideal methodological approaches in political philosophy. The ideal approach requires positing the principles according to which a just world would operate. In the most straightforward application of ideal theory, one supports a proposed policy by arguing that it closes a discrepancy between the real and the perfectly just world. However, by failing to account for the mechanisms by which our non-ideal world arose, the responsibilities of various decision-makers, and the impacts of proposed policies, naive applications of ideal thinking can lead to misguided interventions. In this paper, we demonstrate a connection between the fair machine learning literature and the ideal approach in political philosophy, and argue that the increasingly apparent shortcomings of proposed fair machine learning algorithms reflect broader troubles faced by the ideal approach. We conclude with a critical discussion of the harms of misguided solutions, a reinterpretation of impossibility results, and directions for future research. (shrink)

Most laws are ceteris paribus (cp) laws: they say not that all Fs are G but only that All Fs are G all else being equal. Most philosophical accounts of laws, however, have focused on strict laws. This paper considers how some of the standard philosophical problems about laws change when we switch attention from strict to cp laws and what special problems these laws raise. It is argued that some cp laws do not simply reflect the complexity of the (...) world and the limitations of our minds. Correctly interpreted, they reveal the simplicity that underlies the complexity, a simplicity that it is without our cognitive powers to grasp. (shrink)

Abstract“Force dynamics” refers to a previously neglected semantic category—how entities interact with respect to force. This category includes such concepts as: the exertion of force, resistance to such exertion and the overcoming of such resistance, blockage of a force and the removal of such blockage, and so forth. Force dynamics is a generalization over the traditional linguistic notion of “causative”: it analyzes “causing” into finer primitives and sets it naturally within a framework that also includes “letting,”“hindering,”“helping,” and still further notions. (...) Force dynamics, moreover, appears to be the semantic category that uniquely characterizes the grammatical category of modals, in both their basic and epistemic usages. In addition, on the basis of force dynamic parameters, numerous lexical items fall into systematic semantic patterns, and there exhibit parallelisms between physical and psychosocial reference. Further, from research on the relation of semantic structure to general cognitive structure, it appears that the concepts of force interaction that are encoded within language closely parallel concepts that appear both in early science and in naive physics and psychology. Overall, force dynamics thus emerges as a fundamental notional system that structures conceptual material pertaining to force interaction in a common way across a linguistic range: the physical, psychological, social, inferential, discourse, and mental-model domains of reference and conception. (shrink)

Explores the continuities and discontinuities in the work of Henri Bergson and Gilles Deleuze.

The Postlude, written for this edition, looks back at the predictions made more than twenty-five years ago and speculates about what the coming decades may hold ...

This paper considers how we decide whether to believe what we are told. Inference to the Best Explanation, a popular general account of non-demonstrative reasoning, is applied to this task. The core idea of this application is that we believe what we are told when the truth of what we are told would figure in the best explanation of the fact that we were told it. We believe the fact uttered when it is part of the best explanation of the (...) fact of utterance. Having provided some articulation of this account of testimonial inference, the paper goes on to consider whether the account is informative and whether it is plausible. (shrink)

Which animals have conscious experiences? Many different, diverse and unrelated behaviors and cognitive capacities have been proposed as tests of the presence of consciousness in an animal. It is unclear which of these tests, if any, are valid. To remedy this problem, I develop a list consisting of eight desiderata which can be used to assess putative tests of animal consciousness. These desiderata are based either on detailed analogies between consciousness-linked human behavior and non-human behavior, on theories of consciousness or (...) on methods from human consciousness science. If a test or set of tests satisfies more of these desiderata, passing it provides stronger evidence of consciousness. Moreover, one can design future tests of animal consciousness with the intention of satisfying these desiderata to ensure their evidential strength. (shrink)

Machine learning algorithms are increasingly used to shape high-stake allocations, sparking research efforts to orient algorithm design towards ideals of justice and fairness. In this research on algorithmic fairness, normative theorizing has primarily focused on identification of “ideally fair” target states. In this paper, we argue that this preoccupation with target states in abstraction from the situated dynamics of deployment is misguided. We propose a framework that takes dynamic trajectories as direct objects of moral appraisal, highlighting three respects in which (...) such trajectories can be subject to evaluation in relation to their temporal dynamics, robustness, and representation. (shrink)

_Explores the continuities and discontinuities in the work of Henri Bergson and Gilles Deleuze._.

According to a common view, sentience is necessary and sufficient for moral status. In other words, whether a being has intrinsic moral relevance is determined by its capacity for conscious experience. The _epistemic objection_ derives from our profound uncertainty about sentience. According to this objection, we cannot use sentience as a _criterion_ to ascribe moral status in practice because we won’t know in the foreseeable future which animals and AI systems are sentient while ethical questions regarding the possession of moral (...) status are urgent. Therefore, we need to formulate an alternative criterion. I argue that the epistemic objection is dissolved once one clearly distinguishes between the question what determines moral status and what criterion should be employed in practice to ascribe moral status. Epistemic concerns are irrelevant to the former question and—I will argue—criteria of moral status have inescapably to be based on sentience, if one concedes that sentience determines moral status. It follows that doubts about our epistemic access to sentience cannot be used to motivate an alternative criterion of moral status. If sentience turns out to be unknowable, then moral status is unknowable. However, I briefly advocate against such strong pessimism. (shrink)

Alan Garfinkel (1981) and Bas van Fraassen (1980), among others, have proposed a contrastive theory of explanation, according to which the proper form of an explanatory why-question is not simply "Why P?" but "Why P rather than Q?". Dennis Temple (1988) has argued in this journal that the contrastive explanandum "P rather than Q" is equivalent to the conjunction, "P and not-Q". I show that the contrast is not equivalent to the conjunction, nor to other plausible noncontrastive candidates. I then (...) consider David Lewis's (1986) proposal for the way contrasts determine an explanatory cause, which does not require recasting the contrastive explanandum. Lewis's proposal is found to be unacceptable, but it suggests an improvement that shows contrastive explanations to employ a mechanism of "causal triangulation", similar to Mill's method of difference. (shrink)

The Oxford University Studies in the Enlightenment series, previously known as SVEC (Studies on Voltaire and the Eighteenth Century), has published over 500 peer-reviewed scholarly volumes since 1955 as part of the Voltaire Foundation at the University of Oxford. International in focus, Oxford University Studies in the Enlightenment volumes cover wide-ranging aspects of the eighteenth century and the Enlightenment, from gender studies to political theory, and from economics to visual arts and music, and are published in English or French.

Sensorimotor enactivism is the view that the content and the sensory modality of perceptual experience are determined by implicit knowledge of lawful regularities between bodily movements and patterns of sensory stimulation. A proponent of the explanatory argument for sensorimotor enactivism holds that this view is able to provide an intelligible explanation for why certain material realizers give rise to certain perceptual experiences, while rival accounts cannot close this “explanatory gap”. However, I argue that the notion of the “material realizer” of (...) perceptual experience is ambiguous. On a narrow construal, the explanatory gap cannot be bridged, not even by enactivism. On a wide construal, enactivism gets a grip on the explanatory gap, but to the same extent do established theories of consciousness. Thus, on both horns of the dilemma, the explanatory power of enactivism is not superior to more traditional theories of conscious experience. (shrink)

Numerous authors have expressed concern that advanced artificial intelligence (AI) poses an existential risk to humanity. These authors argue that we might build AI which is vastly intellectually superior to humans (a ‘superintelligence’), and which optimizes for goals that strike us as morally bad, or even irrational. Thus, this argument assumes that a superintelligence might have morally bad goals. However, according to some views, a superintelligence necessarily has morally adequate goals. This might be the case either because abilities for moral (...) reasoning and intelligence mutually depend on each other, or because moral realism and moral internalism are true. I argue that the former argument misconstrues the view that intelligence and goals are independent, and that the latter argument misunderstands the implications of moral internalism. Moreover, the current state of AI research provides additional reasons to think that a superintelligence could have bad goals. (shrink)