The dominant paradigm of experiments in the social and behavioral sciences views an experiment as a test of a theory, where the theory is assumed to generalize beyond the experiment's specific conditions. According to this view, which Alan Newell once characterized as “playing twenty questions with nature,” theory is advanced one experiment at a time, and the integration of disparate findings is assumed to happen via the scientific publishing process. In this article, we argue that the process of (...) integration is at best inefficient, and at worst it does not, in fact, occur. We further show that the challenge of integration cannot be adequately addressed by recently proposed reforms that focus on the reliability and replicability of individual findings, nor simply by conducting more or larger experiments. Rather, the problem arises from the imprecise nature of social and behavioral theories and, consequently, a lack of commensurability across experiments conducted under different conditions. Therefore, researchers must fundamentally rethink how they design experiments and how the experiments relate to theory. We specifically describe an alternative framework, integrative experiment design, which intrinsically promotes commensurability and continuous integration of knowledge. In this paradigm, researchers explicitly map the design space of possible experiments associated with a given research question, embracing many potentially relevant theories rather than focusing on just one. Researchers then iteratively generate theories and test them with experiments explicitly sampled from the design space, allowing results to be integrated across experiments. Given recent methodological and technological developments, we conclude that this approach is feasible and would generate more-reliable, more-cumulative empirical and theoretical knowledge than the current paradigm – and with far greater efficiency. (shrink)

Semantic Engines: An Introduction to Mind Design, John C. Haugeland; Computer Science as Empirical Inquiry: Symbols and Search, Alan Newell and Herbert A. Simon; Complexity and the Study of Artificial and Human Intelligence, Zenon Pylyshyn; A Framework for Representing Knowledge, Marvin Minsky; Artificial Intelligence---A Personal View, David Marr; Artificial Intelligence Meets Natural Stupidity, Drew McDermott; From Micro-Worlds to Knowledge Representation: AI at an Impasse, Hubert L. Dreyfus; Reductionism and the Nature of Psychology, Hilary Putnam; Intentional Systems, Daniel C. (...) Dennett; The Nature and Plausibility of Cognitivism, John C. Haugeland; Minds, Brains, and Programs, John R. Searle; MethodologicalSolipsism Considered as a Research Strategy in Cognitive Psychology, Jerry A. Fodor; The Material Mind, Donald Davidson. (shrink)

An incomplete science begets imperfect models. Nevertheless, the target article advocates for jettisoning deep-learning models with some competency in object recognition for toy models evaluated against a checklist of laboratory findings; an approach which evokes Alan Newell's 20 questions critique. We believe their approach risks incoherency and neglects the most basic test; can the model perform its intended task.

In lieu of an abstract, here is a brief excerpt of the content:The Philosophic Foundations of Mimetic Theory and Cognitive Science(Including Artificial Intelligence)Jean-Pierre Dupuy (bio)In the mid 1970s I discovered at the same time cognitive science and mimetic theory. Being a philosopher with a scientific background, I immediately brought them together and tried to reconceptualize the latter in terms of the former. In a sense, I haven't stopped doing that in the last 45 years. That is why I feel fully (...) at home with the goals pursued by the organizers of this conference.I do not believe it is feasible or even desirable to operate the rapprochement between the two disciplines at a global level. The history of cognitive science is exceedingly involved, and the part played by artificial intelligence in it compared to the part played by the neurosciences has varied greatly over time.1 Only a piecemeal approach makes sense. I present here four short case studies that cover the whole period, from the beginnings in cybernetics to today's artificial intelligence (AI).In order to better situate these studies within a broader frame, I propose the diagram in Figure 1. To comment on it would require more time than available for my whole presentation. Let me just say the following. [End Page 1]It is essential to distinguish between two kinds of AI. The first to bear this name was launched by economist Herbert Simon and computer scientist Allan Newell at a Dartmouth conference in 1956. It was based on the premise that thinking is computing, with this computation bearing on sentences of a "language of thought" implementable in a digital computer. It turns out that this AI(1) was progressively substituted from the 1980s onward by an AI(2), which, to some extent, was a return to a fundamental result obtained by cybernetics in 1943. Neurophysiologist Warren McCulloch and mathematician Walter Pitts had constructed a mathematical model of a network of elementary calculators called "neurons" and explored its properties. The capacity to "think" was one of these. It was not situated at the elementary level, as was the case in AI(1), but it emerged at the collective level from the interactions between the neurons.This model was heavily criticized by the supporters of AI(1),2 and its first implementations, in the notorious "Perceptron" in particular (1957), were pitiful failures. However, the emergence of "second-order cybernetics" under the guidance of cybernetician Heinz von Foerster and the theory of complex, self-organizing systems that it helped develop, in cooperation and rivalry with similar efforts in the physical sciences, enabled the neural network model to come to a second life under different names: formal neurons, deep learning, big data, and the like. Together, they constitute AI(2).The following network of influences wouldn't have been possible without its stem, that is, the boxes containing the names of Kurt Gödel and Alan Turing. The former revolutionized logic by showing that reasoning about arithmetic and arithmetic are one and the same, insofar as it is possible to code in a 1-to-1 fashion propositions about integers (for instance, "7 is a prime number") by means of integers. The latter demonstrated that some mechanisms can self-transcend inasmuch as they generate an ensemble of outputs that cannot be defined mechanistically. Later, John von Neumann, who is credited along with Alan Turing with having invented the digital computer, would use this logical possibility to define complexity: A mechanism is complex if it is capable of generating one more complex than itself. The gate was open for the theories of complex, self-organizing systems to thrive and, by way of consequence, for AI(2) to emerge.fascination with modelsHerbert Simon (1916–2001), winner of the Nobel Prize in economics for his work on what he called "bounded rationality," is generally considered to be one [End Page 2] Click for larger view View full resolutionFigure 1.of the founders of artificial intelligence. Some cognitive scientists still rely on the computer program that he devised with Alan Newell to simulate the creative processes of human thought—a program aptly named "General Problem Solver." The title of one of his principal works, The Sciences of the... (shrink)

Introduction: Natural necessity, being, and becoming -- Accentuate the negative -- Diffracting dialectic -- Opening totality -- Constellating ethics -- Metacritique I : philosophy's primordial failing -- Metacritique II : dialectic and difference -- Conclusion: Natural necessity and the grounds of justice : natural necessity as material meshwork.

The field of artificial intelligence (AI) can be characterized as the investigation of computational systems that exhibit intelligent behavior (including algorithms and models used in these systems). The emphasis is not so much on understanding (human) cognitive processes as on producing models, algorithms, and systems that are capable of apparently intelligent behavior by whatever means available. The idea of AI has had a long history that can be traced all the way back to, for example, Leibniz. The idea was furthered (...) through the development, early in this century, of mathematical logic, cybernetics, and information theory, all of which contributed to AI theorizing. Alan Turing's bold claim that machines can think, advanced in his famous 1950 paper, and the “Turing test” he proposed therein as a means to test the intelligence of machines predated and prompted the formation of AI. Six years later, the Dartmouth conference in 1956, the participants of which included Allen Newell, Herbert Simon, Marvin Minsky, and John McCarthy, inaugurated AI as an academic discipline. This contemporary version of AI was facilitated by the invention and rapidly widening use of digital computers. AI embodies an extremely diverse set of ideas that are often mutually conflicting (as in the case of symbolism versus connectionism), and its relationship with other fields studying cognition (such as psychology and linguistics) has been a close but uneasy one. (shrink)

Gonzales tells Mark Crimmins (1992) that Crimmins knows him under two guises, and that under his other guise Crimmins thinks him an idiot. Knowing his cleverness, but not knowing which guise he has in mind, Crimmins trusts Gonzales but does not know which of his beliefs to revise. He therefore asserts to Gonzales. (FBI) I falsely believe that you are an idiot.

in Probability is the Very Guide of Life: The Philosophical Uses of Chance, eds. Henry Kyburg, Jr. and Mariam Thalos, Open Court. Abridged version in Proceedings of the International Society for Bayesian Analysis 2002.

Confirmation theory is intended to codify the evidential bearing of observations on hypotheses, characterizing relations of inductive “support” and “counter­support” in full generality. The central task is to understand what it means to say that datum E confirms or supports a hypothesis H when E does not logically entail H.

The authors develop an axiomatic theory of nonexistent objects and and give a formal semantics for the language of the theory.

According to the majority of the textbooks, the history of modern, scientific psychology can be tidily encapsulated in the following three stages. Scientific psychology began with a commitment to the study of mind, but based on the method of introspection. Watson rejected introspectionism as both unreliable and effete, and redefined psychology, instead, as the science of behaviour. The cognitive revolution, in turn, replaced the mind as the subject of study, and rejected both behaviourism and a reliance on introspection. This paper (...) argues that all three stages of this history are largely mythical. Introspectionism was never a dominant movement within modern psychology, and the method of introspection never went away. Furthermore, this version of psychology’s history obscures some deep conceptual problems, not least surrounding the modern conception of “behaviour,” that continues to make the scientific study of consciousness seem so weird. (shrink)

This is the text of The Lindley Lecture for 1981, given by Alan Donagan.

This paper examines the relationship between modern theories of microeconomics and macroeconomics and, more generally, it evaluates the prospects of theoretically reducing macroeconomics to microeconomics. Many economists have shown strong interest in providing "microfoundations" for macroeconomics and much of their work is germane to the issue of theoretical reduction. Especially relevant is the work that has been done on what is called The Problem of Aggregation. On some accounts, The Problem of Aggregation just is the problem of reducing macroeconomics to (...) microeconomics. I show how to separate these problems and then try to determine to what extent particular kinds of solutions to The Problem of Aggregation succeed in reducing macroeconomics to microeconomics as well. I argue that reduction is not possible by this means given the current state of microeconomics. I also describe how reduction may be possible by means of (dis)aggregation if microeconomics is supplemented in a certain way with the results of experimental research on individual economic agents. (shrink)

Should a philosophical account of perceptual knowledge accord a justificatory role to sensory experiences? This discussion raises problems for an affirmative answer and sets out an alternative account on which justified belief is conceived as well-founded belief and well-foundedness is taken to depend on knowledge. A key part of the discussion draws on a conception of perceptual-recognitional abilities to account for how perception gives rise both to perceptual knowledge and to well-founded belief.

Since 1945, there have been two waves of Anglo-American writing on Hegel's political thought. The first defended it against works portraying Hegel as an apologist of Prussian reaction and a theorist of totalitarian nationalism. The second presented Hegel as a civic humanist critic of liberalism in the tradition of Rousseau. The first suppressed elements of Hegel's thought that challenge liberalism's individualistic premises; the second downplayed Hegel's theism. This book recovers what was lost in each wave. It restores aspects of Hegel's (...) political thought unsettling to liberal beliefs, yet that lead to a state more liberal than Locke's and Kant's, which retain authoritarian elements. It also scrutinizes Hegel's claim to have justified theism to rational insight, hence to have made it conformable to Enlightenment standards of admissible public discourse. And it seeks to show how, for Hegel, the wholeness unique to divinity is realizable among humans without concession or compromise and what role philosophy must play in its final achievement. Lastly, we are shown what form Hegel's philosophy can take in a world not yet prepared for his science. Here is Hegel's political thought undistorted. (shrink)

It is often noted, by philosophers concerned with being, that problems arise for the articulation of being in English from the fact that the infinitive “to be” often cannot—without enormous awkwardness—be used to translate such counterpart infinitives as the Greek einai, the Latin esse, and the German Sein. Hence, to translate two distinct terms from those other languages—einai and to on, esse and ens, Sein and Seiende—English must often make do with the single term “being.” The term “being” is indeed (...) ambiguous. As a first step toward disambiguating it—and a first step in my rearticulation of being—I introduce the technical term “be-er.” This term is comparable to such terms as “runner,” “swimmer,” “writer,” and “philosopher.” According to the structural-systematic philosophy (SSP), systematic philosophies require theories of be-ers—the SSP calls such theories “ontologies”—but they also require theories of being. Virtually throughout the history of philosophy, the status of theories of be-ers is far clearer than the status of theories of being. Philosophers disagree about which theory of be-ers is best, but it is at least relatively clear what they are disagreeing about. With theories of being, on the other hand, there is so little clarity about what a theory of being that is not a theory of be-ers could or should be that the majority of philosophers—including virtually all analytic philosophers—do not recognize developing theories of being as a genuine task for philosophy. (shrink)

Expectations play a major role in ‘driving’ biotechnology research and development. However, their ethical significance has been largely overlooked. This article examines the dynamics and ethics of expectations surrounding biotechnologies, focusing on biobanks and the promise of personalised medicines. It explores the personal and social implications of expectations, especially where technologies fail to eventuate. The article identifies the claims and practices that support the expectations pertaining to biotechnologies and some of the factors that work against the fulfilment of predicted innovations. (...) It is argued that the role of expectations in shaping thinking and action needs to be taken seriously by those who are concerned about the ethical implications of biotechnologies. (shrink)

In 1964, the American Medical Association invited liberal theologian Abraham Joshua Heschel to address its annual meeting in a program entitled “The Patient as a Person” [1]. Unsurprisingly, in light of Heschel’s reputation for outspokenness, he launched a jeremiad against physicians, claiming: “The admiration for medical science is increasing, the respect for its practitioners is decreasing. The depreciation of the image of the doctor is bound to disseminate disenchantment and to affect the state of medicine itself” [1, p. 35]. Heschel’s (...) reference to “disenchantment” suggests that he may have been familiar with the work, or at least the outlook, of sociologist Max Weber, whose 1917 address “Science as a Vocation” portrays the modern world as disenchanted by the progress of rationalism. Heschel’s life’s vocation had been to uncover the inner meaning of religious faith and to translate that faith into principled action. Heschel saw disenchantment not as an inescapable aspect of modern life but rather as the byproduct of physicians’ conscious choices to seek worldly success and material comfort. Yet, because of their privileged position as witnesses to human vulnerability, physicians possess an obligation to develop their own personhood, to re-enchant medicine, and through medicine to spark a positive transformation in all of modern life. As Heschel says, “The doctor must realize the supreme nobility of his vocation, to cultivate a taste for the pleasures of the soul. … The doctor is a major source of moral energy affecting the spiritual texture and substance of the entire society” [1, pp. 34, 38]. While Heschel’s conception of the physician’s role is romanticized and idealized, changes in the organization and practice of medicine have validated his concerns. (shrink)

The concept of missed care refers to an irrefragable truth that required nursing care, which is left undone, occurs in the delivery of health care. As a technical concept, missed care offers nurses the opportunity to articulate a problematic experience. But what are we to make of missed care from an ethical perspective? Can nurses be held morally responsible for missed care? Ethically speaking, it is generally accepted that if a person has a moral obligation to do something, s/he needs (...) to have the capacity to do it. If a person does not have the capacity to fulfil a moral obligation, then s/he cannot be held responsible for failing to do so. This is captured by the “ought implies can” (OIC) principle. This paper brings the OIC principle to the forefront of the discussion on missed care. It is contended that nurses – qua moral agents – may be discharged from a moral obligation to carry out a required caring act because of some inability that is not of their making and therefore may not be morally responsible for missed care. However, the OIC principle may not be applied to all situations of missed care depending on their causes. In addition, following in the thought of Sapontzis (The Southern Journal of Philosophy, 1991, XXIX, 383–393) it is contended that when an original obligation to deliver a required act of care cannot be fulfilled, other obligations may be generated as summed up in the “Principle of Making Amends” and the “Principle of Appropriate Feeling.” It is the view of this paper that these further principles could prevent the OIC principle from being used to simply excuse omissions of care from a normative standard and could support the view that nurses continue to have alternative obligations to make amends and to respond with appropriate feelings to missed care. (shrink)

Panni les aspects remarquables de l'Aristarque (1644) de Roberval, on relève la répétition fréquente dans le texte de l'abréviation« P.N.E.M.». Ces lettres signifient « pondere, numero et mensura ». Ces mots sont tirés du Livre de la Sagesse, XI, 20: « Pondere, mensura, numero Deus omnia fecit » (Vulgate). Ce verset est cité chez beaucoup d'auteurs qui veulent louer Dieu Géomètre. Cependant, Roberval n'est nullement pieux. Il s'agit donc ici de savoir pourquoi il se sert de « P.N.E.M.» dans son (...) texte. Roberval se montre-t-il ainsi solidaire de ses confrères mathématiciens du Collège royal, dont le verset serait la devise? (shrink)