"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)

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)

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.

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)

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)

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)

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)

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)

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 ...

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)

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)

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)

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)

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 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)

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)

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

Scientific articles exemplify standard functional units constraining argumentative structures. Severe space limitations demand every paragraph and illustration contribute to establishing the paper's claims. Philosophical testing and confirmation models should take into account each paragraph, table, and illustration. Hypothetico-Deductive, Bayesian Inductive, and Inference-to-the-Best-Explanation models do not, garbling the logic of papers. Micro-analysis of the fundamental paper in plate tectonics reveals an argumentative structure commonplace in science but ignored by standard philosophical accounts that cannot be dismissed as mere rhetorical embellishment. Papers with (...) illustrations often display a second argumentative structure differing from the text's. Constraints on adequate testing and confirmation analyses are adduced. “Experiments are about the assembly of persuasive arguments, ones that will stand up in court. … The task at hand is to capture the building-up of a persuasive argument about the world even in the absence of the logician's certainty.”—Galison, How Experiments End, 277. (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)

There is a natural objection to the epistemic coherence of Bas van Fraassen’s use of a distinction between the observable and unobservable in his constructive empiricism, an objection that has been raised with particular clarity by Alan Musgrave. We outline Musgrave’s objection, and then consider how one might interpret and evaluate van Fraassen’s response. According to the constructive empiricist, observability for us is measured with respect to the epistemic limits of human beings qua measuring devices, limitations ‘which will be described (...) in detail in the final physics and biology’ (van Fraassen 1980: 17). In order for the constructive empiricist to determine what counts as observable, he will have to appeal to our best scientific theories of light, human physiology, and so forth. To put the same point in a slightly more abstract way, in order to draw a distinction between observable and unobservable entities, the constructive empiricist needs to use his best scientific theory of observability – call it T* – to tell him the identity of the observable entities. This raises an interesting difficulty. Constructive empiricism is the view that ‘science aims to give us theories that are empirically adequate; and acceptance of a theory involves as belief only that it is empirically adequate’ (van Fraassen 1980:12). When he accepts a theory, the constructive empiricist only believes the statements of his scientific theories that are about observable entities. Thus, in order to know which statements of a scientific theory to believe, the constructive empiricist needs to know which statements of that theory are about observable entities. In particular then, the constructive empiricist only believes the statements of his theory of observability T* that are about observable entities. Therefore, in order to know which statements of T* he can believe, the constructive empiricist needs to know which statements of T* are about observable entities. However, it is T* that tells the constructive empiricist what counts as an observable entity: the constructive empiricist therefore needs to use T* to tell him which statements of T* he can believe. The fact that the distinction drawn by T* must also apply to itself is not an immediate cause for alarm.. (shrink)

1. 1.1. Suffering is bad. This is why, ceteris paribus, there are strong moral reasons to prevent suffering. Moreover, typically, those moral reasons are stronger when the amount of suffering at st...

Illusionism about consciousness entails that phenomenal consciousness doesn’t exist. The distribution question concerns the distribution of consciousness in the animal kingdom. Skepticism of animal consciousness is the view that few or no kinds of animals possess consciousness. Thus, illusionism seems to imply a skeptical view on the distribution question. However, I argue that illusionism and skepticism of animal consciousness are actually orthogonal to each other. If illusionism is true, then phenomenal consciousness does not ground intrinsic value so that the non-existence (...) of phenomenal consciousness would not have ethical implications. In this case, the adequate reaction is to reformulate the distribution question in terms of the quasi-phenomenal functional features associated with the illusion of phenomenal consciousness which do ground intrinsic value. Hence, either illusionism is false or the distribution question does not concern phenomenal consciousness. In any case, illusionism does not directly tell us anything about the distribution question. However, since illusionism forces us to revise our traditional conception of consciousness, it indirectly affects research on animal consciousness by questioning that consciousness is a unified and an all-or-nothing property. (shrink)

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 ...

How can one build AI systems such that they pursue the goals their designers want them to pursue? This is the alignment problem. Numerous authors have raised concerns that, as research advances and systems become more powerful over time, misalignment might lead to catastrophic outcomes, perhaps even to the extinction or permanent disempowerment of humanity. In this paper, I analyze the severity of this risk based on current instances of misalignment. More specifically, I argue that contemporary large language models and (...) game-playing agents are sometimes misaligned. These cases suggest that misalignment tends to have a variety of features: misalignment can be hard to detect, predict and remedy, it does not depend on a specific architecture or training paradigm, it tends to diminish a system’s usefulness and it is the default outcome of creating AI via machine learning. Subsequently, based on these features, I show that the risk of AI alignment magnifies with respect to more capable systems. Not only might more capable systems cause more harm when misaligned, aligning them should be expected to be more difficult than aligning current AI. (shrink)

It is shown that many different problems have the same degree of unsolvability. Among these problems are: THE INDUCTIVE INFERENCE PROBLEM. Infer in the limit an index for a recursive function f presented as f(0), f(1), f(2),.... THE RECURSIVE INDEX PROBLEM. Decide in the limit if i is the index of a total recursive function. THE ZERO NONVARIANT PROBLEM. Decide in the limit if a recursive function f presented as f(0), f(1), f(2),... has value unequal to zero for infinitely many (...) arguments. Finally, it is shown that these unsolvable problems are strictly easier than the halting problem. (shrink)

We statisticians, with our specific concern for uncertainty, are even more liable than other practical men to encounter philosophy, whether we like it or not. For my part, I like it comparatively well. That is why the honor of opening this session of discussion has come to me, though my background makes my knowledge and idiom somewhat different from your own.

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

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)

Art interprets the visible world, physics charts its unseen workings--making the two realms seem completely opposed. But in Art & Physics, Leonard Shlain tracks their breakthroughs side by side throughout history to reveal an astonishing correlation of visions. From teh classical Greek sculptors to Andy Warhol and Jasper Johns, and from Aristotle to Einstein, aritsts have foreshadowed the discoveries of scientists, such as when Money and Cezanne intuited the coming upheaval in physics that Einstein would initiate. In this lively (...) and colorful narrative, Leonard Shlain explores how artistic breakthroughs could have prefigured the visionary insights of physicists on so many occasions throughtout history. Provacative and original, Art & Physics is a seamless integration of the romance of art and the drama of science...and exhilarating history of ideas. (shrink)

In this book, Leonard Talmy proposes that a single linguistic/cognitive system, targeting, underlies two domains of linguistic reference, those termed anaphora and deixis. Talmy argues that language engages the same cognitive system to single out referents whether they are speech-internal or speech-external. Talmy explains the targeting system in this way: as a speaker communicates with a hearer, her attention is on an object to which she wishes to refer; this is her target. To get the hearer's attention on it (...) as well, she uses a trigger -- a word such as this, that, here, there, or now. The trigger initiates a three-stage process in the hearer: he seeks cues of ten distinct categories; uses these cues to determine the target; and then maps the concept of the target gleaned from the cues back onto the trigger to integrate it into the speaker's sentence, achieving comprehension. The whole interaction, Talmy explains, rests on a coordination of the speaker's and hearer's cognitive processing. The process is the same whether the referent is anaphoric or deictic. Talmy presents and analyzes the ten categories of cues, and examines sequences in targeting, including the steps by which interaction leads to joint attention. A glossary defines the new terms in the argument. (shrink)