Mechanism schemata are one of the least understood parts of MDC’s account of mechanistic explanation. Relatedly, there is a common misconception that there is no place for abstraction in MDC mechanisms. These two problems can be remedied by looking more carefully at what MDC say both in their 2000 paper and elsewhere about schemata and abstraction, and by following up on a comment of Machamer’s indicating that Piaget was the inspiration for schemata. Darden’s work on mechanism discovery reveals an important (...) role for abstract mechanism schemata, not only as concise representations of known mechanisms, but also as hypotheses for how unknown mechanisms might work. Piaget’s schemata likewise both capture the developing child’s current state of knowledge, and serve as templates into which new experiences are fit. If we assume that mechanistic explanations should pick out real difference-makers, and that what makes a difference needn’t always be fine-grained details, we open up an explanatory role for abstract schemata to play. Schemata represent the coarser-grained features of mechanisms that are often among the most important difference-makers. In many contexts they might need to be supplemented with finer grained details in order to give an adequate explanation, but independent of those details, schemata remain explanatory in virtue of being part of the explanation. Detailed accounts of real difference-makers need not be seen as in competition with abstract models or generalizations. They can be combined in mechanistic explanations. (shrink)

Andrew was a desperately premature baby weighing under two pounds. He died after months of "heroic' efforts in an intensive care facility. The story of his short cruel institutionalised life is a case study in the limits and excesses of modern medicine. The night he told us our son Andrew was about to die the doctor who had taken charge of him six months before also told us we were "intellectually tight' that we had "no feelings only thoughts and words (...) and strategies'. We were "bad parents'. As the parents of a five-year-old daughter we knew the love a mother and father feels for children. Yet as Andrew's parents we were used to condemnation and insult. Andrew was a baby born 15 weeks prematurely weighing only 1lb 12oz and in a state of painful deterioration almost from the start. We wanted him to be allowed to die a natural death. Andrew's story is the story of what can happen when a baby becomes hopelessly entrapped in an intensive care unit where the machinery is more sophisticated than the code of law and ethics governing its use. The letter printed below was sent to the administrator and numerous personnel of the hospital that controlled the life and death of our son. The physician-in-chief of that hospital characterised it as a "carefully documented critique'. The letter appear here somewhat edited and abridged and the names of people and institutions have been changed all but our own. It is the personal record of what happened to our baby and to us. (shrink)

When Artificial Intelligence (AI) is applied in decision-making that affects people’s lives, it is now well established that the outcomes can be biased or discriminatory. The question of whether algorithms themselves can be among the sources of bias has been the subject of recent debate among Artificial Intelligence researchers, and scholars who study the social impact of technology. There has been a tendency to focus on examples, where the data set used to train the AI is biased, and denial on (...) the part of some researchers that algorithms can also be biased. Here we illustrate the point that algorithms themselves can be the source of bias with the example of collaborative filtering algorithms for recommendation and search. These algorithms are known to suffer from cold-start, popularity, and homogenizing biases, among others. While these are typically described as statistical biases rather than biases of moral import; in this paper we show that these statistical biases can lead directly to discriminatory outcomes. The intuitive idea is that data points on the margins of distributions of human data tend to correspond to marginalized people. The statistical biases described here have the effect of further marginalizing the already marginal. Biased algorithms for applications such as media recommendations can have significant impact on individuals’ and communities’ access to information and culturally-relevant resources. This source of bias warrants serious attention given the ubiquity of algorithmic decision-making. (shrink)

The past seven centuries of scholarly attention to and debate over the Roman de la Rose bear strong witness to the fact that the allegorical figure Faus Semblant presents us with an interpretive crux—one of many such in the poem—that we are not likely to resolve in the coming centuries. Perhaps it should come as no surprise that a character who so embodies paradox—a profane friar who is openly honest about his intent to deceive—should be so difficult to pin down; (...) it is his singular talent, after all, to dissemble and confuse. The last century has seen a significant increase in criticism centered upon Faus Semblant that seeks to understand what he signifies within the larger allegory of the poem and how he relates to Jean de Meun's purported satire and antifraternalism. But this struggle to understand and explain the character began centuries earlier and, like him, has taken many forms. As early as the thirteenth century, the passage of Jean's poem in which Faus Semblant explains his craft and guile had begun to attract a diverse group of revisers: scribes, who added or deleted passages in order to shape a reading of the poem or avoid offending readers; remanieurs, such as Gui de Mori, who substantially rewrote the passage in efforts to reshape the poem into a more cohesive form; readers, whose marginal inscriptions and notae often accompany the passage; and illustrators , as images of Faus Semblant are frequently included in illuminated copies of Rose manuscripts. Even Jean himself seemed to feel the need to gloss and contain Faus Semblant, offering an apology and clarification in lines 15213–30. (shrink)

Recent extensions of mechanistic explanation into psychology suggest that cognitive models are only explanatory insofar as they map neatly onto, and serve as scaffolding for more detailed neural models. Filling in those neural details is what these accounts take the integration of cognitive psychology and neuroscience to mean, and they take this process to be seamless. Critics of this view have given up on cognitive models possibly explaining mechanistically in the course of arguing for cognitive models having explanatory value independent (...) of how well they align with neural mechanisms. We can have things both ways, however. The problem with seamless integration accounts is their seamlessness, not that they take cognitive models to be mechanistic. A non-componential view of mechanisms allows for cognitive and neural models that cross cut one another, and for cognitive models that don’t decompose into parts. I illustrate the inadequacy of seamless accounts of integration by contrasting how “filter” models of attention in psychology and of sodium channels in neuroscience developed; by questioning whether the mappings generated by neuroimaging subtraction studies achieve integration; and by reinterpreting the evidence for cognitive models of memory having been successfully integrated with neural models. I argue that the integrations we can realistically expect are more partial, patchy, and full of loose threads than the mosaic unity Craver describes. (shrink)

There is a vast literature within philosophy of mind that focuses on artificial intelligence, but hardly mentions methodological questions. There is also a growing body of work in philosophy of science about modeling methodology that hardly mentions examples from cognitive science. Here these discussions are connected. Insights developed in the philosophy of science literature about the importance of idealization provide a way of understanding the neural implausibility of connectionist networks. Insights from neurocognitive science illuminate how relevant similarities between models and (...) targets are picked out, how modeling inferences are justified, and the metaphysical status of models. (shrink)

Discussions of psychiatric nosology focus on a few popular examples of disorders, and on the validity of diagnostic criteria. Looking at Anorexia Nervosa, an example rarely mentioned in this literature, reveals a new problem: the DSM has a strict taxonomic structure, which assumes that disorders can only be located on one branch. This taxonomic assumption fails to fit the domain of psychopathology, resulting in obfuscation of cross-category connections. Poor outcomes for treatment of Anorexia may be due to it being pigeonholed (...) as an Eating Disorder, when a disturbance of body perception may be a more central symptom than food restriction. This paper explores the possibility of restructuring the DSM taxonomy to allow for a pluralist classification of disorders. This change could improve treatment and research without requiring any changes to diagnostic criteria. (shrink)

William James presaged, and Alan Allport voiced criticisms of cause theories of executive attention for involving a homunculus who directs attention. I review discussions of this problem, and argue that existing philosophical denials of the problem depend on equivocations between different senses of “Cartesian error”. Another sort of denial tries to get around the problem by offering empirical evidence that such an executive attention director exists in prefrontal cortex. I argue that the evidence does not warrant the conclusion that an (...) executive director can be localized in prefrontal cortex unless dubious assumptions are made, and that computational models purporting to support these assumptions either beg the question, or fail to model executive attention in terms of cause theories. (shrink)

Diversity is often announced as a solution to ethical problems in artificial intelligence (AI), but what exactly is meant by diversity and how it can solve those problems is seldom spelled out. This lack of clarity is one hurdle to motivating diversity in AI. Another hurdle is that while the most common perceptions about what diversity is are too weak to do the work set out for them, stronger notions of diversity are often defended on normative grounds that fail to (...) connect to the values that are important to decision-makers in AI. However, there is a long history of research in feminist philosophy of science and a recent body of work in social epistemology that taken together provide the foundation for a notion of diversity that is both strong enough to do the work demanded of it, and can be defended on epistemic grounds that connect with the values that are important to decision-makers in AI. We clarify and defend that notion here by introducing emergent expertise as a network phenomenon wherein groups of workers with expertise of different types can gain knowledge not available to any individual alone, as long as they have ways of communicating across types of expertise. We illustrate the connected epistemic and ethical benefits of designing technology with diverse groups of workers using the examples of an infamous racist soap dispenser, and the millimeter wave scanners used in US airport security. (shrink)

This chapter explores the epistemic roles played by connectionist models of cognition, and offers a formal analysis of how connectionist models explain. It looks at how other types of computational models explain. Classical artificial intelligence (AI) programs explain using abductive reasoning, or inference to the best explanation; they begin with the phenomena to be explained, and devise rules that can produce the right outcome. The chapter also looks at several examples of connectionist models of cognition, observing what sorts of constraints (...) are used in their design, and how their results are evaluated. It argues that the point of implementing networks roughly analogous to neural structures is to discover and explores the generic mechanisms at work in the brain, not to deduce the precise activities of specific structures. The chapter explores a formal analysis of the explanations offered, which interprets connectionist models and the cognitive theories they represent as sharing membership in a type of mechanism. (shrink)

Synchronicity has long been described as an ‘acausal’ connecting principle. However, the use of this descriptor is not only misleading, but also outright false on any seriously considered picture of synchronicity due to admissions of multiple types of causes. Furthermore, previous attempts to clarify the ‘acausal’ label have served only to further muddy the waters of discussion. A ‘multi-causal’ conception of synchronicity is proposed to ease and encourage future discussion in many disciplines.

This chapter explores some of the ways that mechanisms are invoked in neuroscience and looks at a selection of the philosophical problems that arise when trying to understand mechanistic explanations. It introduces a series of historical case studies that illustrate how neuroscientists have depended on mechanistic metaphors in their efforts to understand the mind and brain, and how their mechanistic explanations have developed over time. The chapter highlights what contemporary philosophers have identified as the fundamental features of mechanisms and mechanistic (...) explanation. It considers some of the methodological issues that arise in neuroscience including how to integrate psychological with neural models, how to generalize findings in model organisms like the sea slug Aplysia to human learning and memory, and whether to favor top-down or bottom-up methods. Constructing multi-level mechanistic explanations involves intensive collaboration across different branches of science, and involves many challenges, both pragmatic and methodological. (shrink)

Design and analysis of algorithms can be a difficult subject for students due to its sometimes-abstract nature and its use of a wide variety of mathematical tools. Here the author, an experienced and successful textbook writer, makes the subject as straightforward as possible in an up-to-date textbook incorporating various new developments appropriate for an introductory course. This text presents the main techniques of algorithm design, namely, divide-and-conquer algorithms, greedy algorithms, dynamic programming algorithms, and backtracking. Graph algorithms are studied in detail, (...) and a careful treatment of the theory of NP-completeness is presented. In addition, the text includes useful introductory material on mathematical background including order notation, algorithm analysis and reductions, and basic data structures. This will serve as a useful review and reference for students who have covered this material in a previous course. Features The first three chapters provide a mathematical review, basic algorithm analysis, and data structures. Detailed pseudocode descriptions of the algorithms along with illustrative algorithms are included. Proofs of correctness of algorithms are included when appropriate. The book presents a suitable amount of mathematical rigor. After reading and understanding the material in this book, students will be able to apply the basic design principles to various real-world problems that they may encounter in their future professional careers. (shrink)

Ocherk 1. Voprosy ontologii -- Ocherk 2. Voprosy ėtiki i ėstetiki -- Ocherk 3. Voprosy gnoseologii i aksiologii.

Prologue, invitation to thinking about God In the Hebrew Bible? -- Part I, questions about God? -- Why does God in the Bible have a body? -- What do God's body parts in the Bible mean? -- Why is God angry in the Bible? -- Does God in the Bible have gender or sexuality? -- Part II, questions about God in the world? -- What can creation tell us about God? -- Who-or what-is the Satan? -- Why do people suffer (...) according to the Hebrew Bible? -- Epilogue, God in the Bible and beyond. (shrink)

Many books explain what is known about the universe. This book investigates what cannot be known. Rather than exploring the amazing facts that science, mathematics, and reason have revealed to us, this work studies what science, mathematics, and reason tell us cannot be revealed. In The Outer Limits of Reason, Noson Yanofsky considers what cannot be predicted, described, or known, and what will never be understood. He discusses the limitations of computers, physics, logic, and our own thought processes. Yanofsky describes (...) simple tasks that would take computers trillions of centuries to complete and other problems that computers can never solve; perfectly formed English sentences that make no sense; different levels of infinity; the bizarre world of the quantum; the relevance of relativity theory; the causes of chaos theory; math problems that cannot be solved by normal means; and statements that are true but cannot be proven. He explains the limitations of our intuitions about the world -- our ideas about space, time, and motion, and the complex relationship between the knower and the known. Moving from the concrete to the abstract, from problems of everyday language to straightforward philosophical questions to the formalities of physics and mathematics, Yanofsky demonstrates a myriad of unsolvable problems and paradoxes. Exploring the various limitations of our knowledge, he shows that many of these limitations have a similar pattern and that by investigating these patterns, we can better understand the structure and limitations of reason itself. Yanofsky even attempts to look beyond the borders of reason to see what, if anything, is out there. (shrink)

Study of Hans-Georg Gadamer's and Julius Stenzel's interpretations of Plato.

Richard Janko's acclaimed translation of Aristotle's _Poetics_ is accompanied by the most comprehensive commentary available in English that does not presume knowledge of the original Greek. Two other unique features are Janko's translations with notes of both the _Tractatus Coislinianus_, which is argued to be a summary of the lost second book of the Poetics, and fragments of Aristotle’s dialogue On Poets, including recently discovered texts about catharsis, which appear in English for the first time.

The phenomenon of ambivalence is an important one for any philosophy of action. Despite this importance, there is a lack of a fully satisfactory analysis of the phenomenon. Although many contemporary philosophers recognize the phenomenon, and address topics related to it, only Harry Frankfurt has given the phenomenon full treatment in the context of action theory - providing an analysis of how it relates to the structure and freedom of the will. In this paper, I develop objections to Frankfurt's account, (...) all revolving around the charge that his account contains a serious ambiguity between willing and identifying. With such objections in place, I then develop an analysis that avoids the difficulties and ambiguities that Frankfurt's analysis is prey to. I briefly distinguish ambivalence from other types of internal conflict. This paper aims to offer conceptual clarification on the phenomenon of ambivalence, which will then allow for discussions about the normative merits and demerits of ambivalence, the effects of ambivalence on autonomous action, and methods of resolution of ambivalence. (shrink)

The first major work in the history of philosophy to bear the title "Metaphysics" was the treatise by Aristotle that we have come to know by that name. But Aristotle himself did not use that title or even describe his field of study as 'metaphysics'; the name was evidently coined by the first century C.E. editor who assembled the treatise we know as Aristotle's Metaphysics out of various smaller selections of Aristotle's works. The title 'metaphysics' -- literally, 'after the Physics' (...) -- very likely indicated the place the topics discussed therein were intended to occupy in the philosophical curriculum. They were to be studied after the treatises dealing with nature (ta phusika). In this entry, we discuss the ideas that are developed in Aristotle's treatise. (shrink)