We provide two programmatic frameworks for integrating philosophical research on understanding with complementary work in computer science, psychology, and neuroscience. First, philosophical theories of understanding have consequences about how agents should reason if they are to understand that can then be evaluated empirically by their concordance with findings in scientific studies of reasoning. Second, these studies use a multitude of explanations, and a philosophical theory of understanding is well suited to integrating these explanations in illuminating ways.

This chapter provides a systematic overview of topological explanations in the philosophy of science literature. It does so by presenting an account of topological explanation that I (Kostić and Khalifa 2021; Kostić 2020a; 2020b; 2018) have developed in other publications and then comparing this account to other accounts of topological explanation. Finally, this appraisal is opinionated because it highlights some problems in alternative accounts of topological explanations, and also it outlines responses to some of the main criticisms raised (...) by the so-called new mechanists. (shrink)

In the last 20 years or so, since the publication of a seminal paper by Watts and Strogatz :440–442, 1998), an interest in topological explanations has spread like a wild fire over many areas of science, e.g. ecology, evolutionary biology, medicine, and cognitive neuroscience. The topological approach is still very young by all standards, and even within special sciences it still doesn’t have a single methodological programme that is applicable across all areas of science. That is why this special issue (...) is important as a first systematic philosophical study of topological explanations and their relation to a well understood and widespread explanatory strategy, such as mechanistic one. (shrink)

The philosophical literature on scientific explanation in neuroscience has been dominated by the idea of mechanisms. The mechanist philosophers often claim that neuroscience is in the business of finding mechanisms. This view has been challenged in numerous ways by showing that there are other successful and widespread explanatory strategies in neuroscience. However, the empirical evidence for all these claims was hitherto lacking. Empirical evidence about the pervasiveness and uses of various explanatory strategies in neuroscience is particularly needed because examples and (...) case studies that are used to illustrate philosophical claims so far tend to be hand-picked. The risk of confirmation bias is therefore considerable: when looking for white swans, all one finds is that swans are white. The more systematic quantitative and qualitative bibliometric study of a large body of relevant literature that we present in this paper can put such claims into perspective. Using bibliometric tools, we identify the typical linguistic patterns used in the alleged mechanistic, dynamical, and topological explanations in the literature, their preponderance and how they change over time. Our findings show abundant use of mechanistic language, but also the presence of a significant neuroscience literature using topological and dynamical explanatory language, which grows over time and increasingly differentiates from each other and from mechanistic explanations. (shrink)

We provide three innovations to recent debates about whether topological or “network” explanations are a species of mechanistic explanation. First, we more precisely characterize the requirement that all topological explanations are mechanistic explanations and show scientific practice to belie such a requirement. Second, we provide an account that unifies mechanistic and non-mechanistic topological explanations, thereby enriching both the mechanist and autonomist programs by highlighting when and where topological explanations are mechanistic. Third, we defend this view against some powerful mechanist objections. (...) We conclude from this that topological explanations are autonomous from their mechanistic counterparts. (shrink)

In this paper, I present a general theory of topological explanations, and illustrate its fruitfulness by showing how it accounts for explanatory asymmetry. My argument is developed in three steps. In the first step, I show what it is for some topological property A to explain some physical or dynamical property B. Based on that, I derive three key criteria of successful topological explanations: a criterion concerning the facticity of topological explanations, i.e. what makes it true of a particular system; (...) a criterion for describing counterfactual dependencies in two explanatory modes, i.e. the vertical and the horizontal; and, finally, a third perspectival one that tells us when to use the vertical and when to use the horizontal mode. In the second step, I show how this general theory of topological explanations accounts for explanatory asymmetry in both the vertical and horizontal explanatory modes. Finally, in the third step, I argue that this theory is universally applicable across biological sciences, which helps to unify essential concepts of biological networks. (shrink)

In this paper, I argue that the newly developed network approach in neuroscience and biology provides a basis for formulating a unique type of realization, which I call topological realization. Some of its features and its relation to one of the dominant paradigms of realization and explanation in sciences, i.e. the mechanistic one, are already being discussed in the literature. But the detailed features of topological realization, its explanatory power and its relation to another prominent view of realization, namely the (...) semantic one, have not yet been discussed. I argue that topological realization is distinct from mechanistic and semantic ones because the realization base in this framework is not based on local realisers, regardless of the scale but on global realizers. In mechanistic approach, the realization base is always at the local level, in both ontic and epistemic accounts. The explanatory power of realization relation in mechanistic approach comes directly from the realization relation-either by showing how a model is mapped onto a mechanism, or by describing some ontic relations that are explanatory in themselves. Similarly, the semantic approach requires that concepts at different scales logically satisfy microphysical descriptions, which are at the local level. In topological framework the realization base can be found at different scales, but whatever the scale the realization base is global, within that scale, and not local. Furthermore, topological realization enables us to answer the “why” questions, which according to Polger 2010 make it explanatory. The explanatoriness of topological realization stems from understanding mathematical consequences of different topologies, not from the mere fact that a system realizes them. (shrink)

Proponents of ontic conceptions of explanation require all explanations to be backed by causal, constitutive, or similar relations. Among their justifications is that only ontic conceptions can do justice to the ‘directionality’ of explanation, i.e., the requirement that if X explains Y , then not-Y does not explain not-X . Using topological explanations as an illustration, we argue that non-ontic conceptions of explanation have ample resources for securing the directionality of explanations. The different ways in which neuroscientists rely on multiplexes (...) involving both functional and anatomical connectivity in their topological explanations vividly illustrate why ontic considerations are frequently (if not always) irrelevant to explanatory directionality. Therefore, directionality poses no problem to non-ontic conceptions of explanation. (shrink)

In non-causal explanations, some non-causal facts (such as mathematical, modal or metaphysical) are used to explain some physical facts. However, precisely because these explanations abstract away from causal facts, they face two challenges: 1) it is not clear why would one rather than the other non-causal explanantia be relevant for the explanandum; and 2) why would standing in a particular explanatory relation (e.g., “counterfactual dependence”, “constraint”, “entailment”, “constitution”, “grounding”, and so on), and not in some other, be explanatory. I develop (...) an explanatory relevance account which is based on erotetic constraints and show how it addresses these two challenges. (shrink)

In this paper, I outline a heuristic for thinking about the relation between explanation and understanding that can be used to capture various levels of “intimacy”, between them. I argue that the level of complexity in the structure of explanation is inversely proportional to the level of intimacy between explanation and understanding, i.e. the more complexity the less intimacy. I further argue that the level of complexity in the structure of explanation also affects the explanatory depth in a similar way (...) to intimacy between explanation and understanding, i.e. the less complexity the greater explanatory depth and vice versa. (shrink)

Over the last decades, network-based approaches have become highly popular in diverse fields of biology, including neuroscience, ecology, molecular biology and genetics. While these approaches continue to grow very rapidly, some of their conceptual and methodological aspects still require a programmatic foundation. This challenge particularly concerns the question of whether a generalized account of explanatory, organisational and descriptive levels of networks can be applied universally across biological sciences. To this end, this highly interdisciplinary theme issue focuses on the definition, motivation (...) and application of key concepts in biological network science, such as explanatory power of distinctively network explanations, network levels, and network hierarchies. (shrink)

This paper is concerned with a quality space model as an account of the intelligibility of explanation. I argue that descriptions of causal or functional roles (Chalmers Levine, 2001) are not the only basis for intelligible explanations. If we accept that phenomenal concepts refer directly, not via descriptions of causal or functional roles, then it is difficult to find role fillers for the described causal roles. This constitutes a vagueness constraint on the intelligibility of explanation. Thus, I propose to use (...) quality space models to develop a systematic way of studying different modalities of perception and feelings, e.g., visual and auditory perception, pain, and emotion, that can reveal some structural relations among these modalities. It might turn out that topological explanation can be more intelligible than causal explanation in this case. I discuss two accounts of a quality space for color vision (Clark, 2000; Rosenthal, 2010) and propose how to construct a quality space for pain. Daniel Kostic is Associated Researcher at Berlin School of Mind and Brain. (shrink)

In the last couple of years a few seemingly independent debates on scientific explanation have emerged, with several key questions that take different forms in different areas. For example, the question what makes an explanation distinctly mathematical and are there any non-causal explanations in sciences (i.e. explanations that don’t cite causes in the explanans) sometimes take a form of the question what makes mathematical models explanatory, especially whether highly idealized models in science can be explanatory and in virtue of what (...) they are explanatory. These questions raise further issues about counterfactuals, modality and explanatory asymmetries, i.e. do mathematical and non-causal explanations support counterfactuals, and how to understand explanatory asymmetries in non-causal explanations. Even though these are very common issues in the philosophy of physics and mathematics, they can be found in different guises in the philosophy of biology, where there is the statistical interpretation of the Modern Synthesis theory of evolution, according to which the post-Darwinian theory of natural selection explains evolutionary change by citing statistical properties of populations and not the causes of changes. These questions also arise in philosophy of ecology or neuroscience in regard to the nature of topological explanations. The question here is whether in network models in biology, ecology, neuroscience and computer science the mathematical or more precisely topological properties can be explanatory of physical phenomena, or they are just different ways to represent causal structures. The aim of the special issue is to unify all these debates around several overlapping questions. (shrink)

Over the last two decades, network-focused approaches have become highly popular in diverse fields of biology, including neuroscience, ecology, molecular biology and genetics. While the network approach continues to grow very rapidly, some of its conceptual and methodological aspects still require a programmatic foundation. This challenge particularly concerns the question of whether a generalized account of explanatory, organisational and descriptive levels of networks can be applied universally across biological sciences. Consequently, the central focus of this theme issue will be on (...) the definition, motivation and application of key concepts in biological network science, such as levels, hierarchies, and explanatory directionality. A unification will be achieved by formulating norms for delimiting the distinctively network-topological class of explanations that connect general as well as very specific biological research questions. The impact of this theme issue is broad and encompassing, as it is highly interdisciplinary and opens a uniquely normative perspective on the foundational aspects of network-based explanations and modelling. This theme issue is intended to become a landmark publication for practical research as well as funding policy decisions, by unifying network approaches in biological sciences in terms of fundamental concepts and informing the public understanding of the network science. (shrink)

I argue that the hard problem of consciousness occurs only in very limited contexts. My argument is based on the idea of explanatory perspectivalism, according to which what we want to know about a phenomenon determines the type of explanation we use to understand it. To that effect the hard problem arises only in regard to questions such as how is it that concepts of subjective experience can refer to physical properties, but not concerning questions such as what gives rise (...) to qualia or why certain brain states have certain qualities and not others. In this sense we could for example fully explain why certain brain processes have certain subjective qualities, while we still don’t have a viable theory of concepts that explains co-referentiality of phenomenal and physical concepts. Given this limitation, the hard problem doesn’t pose a problem for the empirical study of consciousness. (shrink)

In the last couple of years a few seemingly independent debates on scientific explanation have emerged, with several key questions that take different forms in different areas. For example, the questions what makes an explanation distinctly mathematical and are there any non-causal explanations in sciences sometimes take a form of the question what makes mathematical models explanatory, especially whether highly idealized models in science can be explanatory and in virtue of what they are explanatory. These questions raise further issues about (...) counterfactuals, modality, and explanatory asymmetries: i.e., do mathematical and non-causal explanations support... (shrink)

This paper examines the explanatory gap account. The key notions for its proper understanding are analysed. In particular, the analysis is concerned with the role of “thick” and “thin” modes of presentation and “thick” and “thin” concepts which are relevant for the notions of “thick” and “thin” conceivability, and to that effect relevant for the gappy and non-gappy identities. The last section of the paper discusses the issue of the intelligibility of explanations. One of the conclusions is that the explanatory (...) gap account only succeeds in establishing the epistemic gap. The claim that psychophysical identity is not intelligibly explicable, and thus opens the explanatory gap, would require an indepen- dent argument which would prove that intelligible explanations stem only from conceptual analysis. This, I argue, is not the case. (shrink)

This paper is divided into three sections. In the first section I briefly outline the background of the problem, i.e. Kripke’s modal argument (Kripke 1980). In the second section I present Chalmers’ account of two- dimensional semantics and two-dimensional argument against physicalism. In the third section I criticize Chalmers’ approach based on two crucial points, one is about necessity of identities and the other is about microphysi- cal descriptions and a priori derivation.

In the last couple of years, a few seemingly independent debates on scientific explanation have emerged, with several key questions that take different forms in different areas. For example, the questions what makes an explanation distinctly mathematical and are there any non-causal explanations in sciences (i.e., explanations that don’t cite causes in the explanans) sometimes take a form of the question of what makes mathematical models explanatory, especially whether highly idealized models in science can be explanatory and in virtue of (...) what they are explanatory. These questions raise further issues about counterfactuals, modality, and explanatory asymmetries: i.e., do mathematical and non-causal explanations support counterfactuals, and how ought we to understand explanatory asymmetries in non-causal explanations? Even though these are very common issues in the philosophy of physics and mathematics, they can be found in different guises in the philosophy of biology where there is the statistical interpretation of the Modern Synthesis theory of evolution, according to which the post-Darwinian theory of natural selection explains evolutionary change by citing statistical properties of populations and not the causes of changes. These questions also arise in philosophy of ecology or neuroscience in regard to the nature of topological explanations. The question here is can the mathematical (or more precisely topological) properties in network models in biology, ecology, neuroscience, and computer science be explanatory of physical phenomena, or are they just different ways to represent causal structures. The aim of this special issue is to unify all these debates around several overlapping questions. These questions are: are there genuinely or distinctively mathematical and non-causal explanations?; are all distinctively mathematical explanations also non-causal; in virtue of what they are explanatory; does the instantiation, implementation, or in general, applicability of mathematical structures to a variety of phenomena and systems play any explanatory role? This special issue provides a platform for unifying the debates around several key issues and thus opens up avenues for better understanding of mathematical and non-causal explanations in general, but also, it will enable even better understanding of key issues within each of the debates. (shrink)

In this paper, we explore the conceptual problems arising when using network analysis in person- centered care (PCC) in psychiatry. Personalized network models are potentially helpful tools for PCC, but we argue that using them in psychiatric practice raises boundary problems, i.e., problems in demarcating what should and should not be included in the model, which may limit their ability to provide clinically-relevant knowledge. Models can have explanatory and representational boundaries, among others. We argue that we can make more explicit (...) what kind of questions personalized network models can address in PCC, given their representational and explanatory boundaries, using perspectival reasoning. (shrink)

Over the last decades, network-based approaches have become highly popular in diverse areas of biology. While these approaches continue to grow very rapidly, some of their conceptual and methodological aspects still require a programmatic foundation. In order to unify and systematize network approaches across biological sciences, this theme issue brings together scientists working in many diverse areas of biological sciences as well as philosophers working on foundational issues of network explanations and modelling, who together aim to develop universally applicable norms (...) of network explanations, as well as systematize network concepts, such as levels and hierarchies. (shrink)

This thesis evaluates several powerful arguments that not only deny that brain states and conscious states are one and the same thing, but also claim that such an identity is unintelligible. I argue that these accounts do not undermine physicalism because they don’t provide any direct or independent justification for their tacit assumptions about a link between modes of presentation and explanation. In my view intelligibility of psychophysical identity should not be based exclusively on the analysis of meaning. The main (...) concern then should be why expect that fully intelligible explanation must be based on the descriptions of the causal roles as modes of presentation. To this effect I propose that we examine "psychological concepts". The psychological concepts are concepts that use descriptions of the functional roles but are about qualities of our experiences. I propose to analyze them in quality space models in order to unveil why phenomenal concepts are expected to refer via descriptions of the causal or functional roles. The quality space should be understood here as a multidimensional space consisting of several axes of relative similarity and differences among the structures of ordering in different modalities of conscious experience. On my proposal it is possible that some axes in the quality space consist of their own quality spaces so we could “zoom in” and “zoom out” into the descriptions of the functional roles and see more clearly what the explanation of certain aspects of consciousness looks like when thought of in terms of psychological concepts. (shrink)

Kareem Khalifa’s Understanding, Explanation, and Scientific Knowledge is a splendid book, written in a beautiful and accessible style. It provides the ultimate articulation of his account of explanatory understanding that I am sure will be regarded as one of the landmark publications on the topic of scientific understanding. Many of the central questions regarding scientific understanding are treated from different perspectives in the book. Such questions are: Does understanding require explanations? Must it consist of mostly true information? Is it a (...) species of knowledge? -/- I cannot do justice to all the intricate details of Khalifa’s arguments in a short piece like this, so I will focus on discussing his main line of argument and some of the most salient questions that are addressed in the book, instead of summarizing and commenting on each chapter. (shrink)

Levenstein et al. aptly highlight some of the foundational issues in theoretical neuroscience, such as the role of abstraction and idealization in providing scientific explanations and understanding, and distinguishing under which conditions neuroscientific models provide genuine explanations, or mere descriptions, predictions, or control. -/- The authors rightly emphasize that philosophers of science can also gain valuable insights from the vast body of neuroscience literature, by employing methods of digital humanities, such as text mining, in line with the cognitive metascience approach (...) (Miłkowski, 2023). -/- Conversely, neuroscientists can benefit from the latest research in philosophy of science, particularly on the epistemic norms of diverse kinds of explanations in neuroscience, e.g., topological (Kostić, 2020), computational (Miłkowski, 2013; Chirimuuta, 2018), dynamical (Favela, 2020), and functional (Egan, 2017). Understanding the diverse goals of theories is conducive to fruitful exploratory and explanatory scientific practice. Some forms of scientific understanding are not always grounded in explanations, but also in diverse theoretical representations, e.g., diagrams, taxonomies, and diagnostic manuals (Miłkowski, 2023). Philosophical theories of understanding can also integrate different kinds of explanations in a single framework (Khalifa et al., 2022). -/- The need for these interdisciplinary connections is particularly vivid in the case of network models that are increasingly being used in neuroscience (Kostić et al., 2020), and especially when it comes to evaluating their explanatory power (Kostić, 2020; Kostić and Khalifa, 2023). -/- In conclusion, we strongly support Levenstein et al.'s call for greater collaboration between philosophers of science and neuroscientists on the foundational issues in neuroscience. Embracing this interdisciplinary approach is essential for advancing our knowledge of the brain and its function. (shrink)

It is common to hear talk of the aim of belief and to find philosophers appealing to that aim for numerous explanatory purposes. What belief 's aim explains depends, of course, on what that aim is. Many hold that it is somehow related to truth, but there are various ways in which one might specify belief 's aim using the notion of truth. In this article, by considering whether they can account for belief 's standard of correctness and the epistemic (...) norms governing belief, I argue against certain prominent specifications of belief 's aim given in terms of truth, and advance a neglected alternative. (shrink)

The _Physics_ is a foundational work of western philosophy, and the crucial one for understanding Aristotle's views on matter, form, essence, causation, movement, space, and time. This richly annotated, scrupulously accurate, and consistent translation makes it available to a contemporary English reader as no other does—in part because it fits together seamlessly with other closely associated works in the New Hackett Aristotle series, such as the _Metaphysics_, _De Anima_, and forthcoming _De Caelo_ and _On Coming to Be and Passing Away_. (...) Eventually the series will include all of Aristotle's works. Sequentially numbered endnotes provide the information most needed at each juncture, while a detailed Index of Terms indicates places where focused discussion of key notions occurs. An illuminating general Introduction describes the book that lies ahead, explaining what sort of work it is and what sorts of evidence it relies on. (shrink)

In the international bestseller, Thinking, Fast and Slow, Daniel Kahneman, the renowned psychologist and winner of the Nobel Prize in Economics, takes us on a groundbreaking tour of the mind and explains the two systems that drive the way we think. System 1 is fast, intuitive, and emotional; System 2 is slower, more deliberative, and more logical. The impact of overconfidence on corporate strategies, the difficulties of predicting what will make us happy in the future, the profound effect of (...) cognitive biases on everything from playing the stock market to planning our next vacation—each of these can be understood only by knowing how the two systems shape our judgments and decisions. -/- Engaging the reader in a lively conversation about how we think, Kahneman reveals where we can and cannot trust our intuitions and how we can tap into the benefits of slow thinking. He offers practical and enlightening insights into how choices are made in both our business and our personal lives—and how we can use different techniques to guard against the mental glitches that often get us into trouble. Winner of the National Academy of Sciences Best Book Award and the Los Angeles Times Book Prize and selected by The New York Times Book Review as one of the ten best books of 2011, Thinking, Fast and Slow is destined to be a classic. (shrink)

Sometimes, learning about the origins of a belief can make it irrational to continue to hold that belief—a phenomenon we call ‘genealogical defeat’. According to explanationist accounts, genealogical defeat occurs when one learns that there is no appropriate explanatory connection between one’s belief and the truth. Flatfooted versions of explanationism have been widely and rightly rejected on the grounds that they would disallow beliefs about the future and other inductively-formed beliefs. After motivating the need for some explanationist account, we raise (...) some problems for recent versions of explanationism. Learning from their failures, we then produce and defend a more resilient explanationism. (shrink)

Examines the lived experience of social encounters drawing on phenomenological insights.

Infallibilism is the view that a belief cannot be at once warranted and false. In this essay we assess three nonpartisan arguments for infallibilism, arguments that do not depend on a prior commitment to some substantive theory of warrant. Three premises, one from each argument, are most significant: if a belief can be at once warranted and false, then the Gettier Problem cannot be solved; if a belief can be at once warranted and false, then its warrant can be transferred (...) to an accidentally true belief; if a belief can be at once warranted and false, then it can be warranted and accidentally true. We argue that each of these is either false or no more plausible than its denial. Along the way, we offer a solution to the Gettier Problem that is compatible with fallibilism. (shrink)

Supererogatory acts—good deeds “beyond the call of duty”—are a part of moral common sense, but conceptually puzzling. I propose a unified solution to three of the most infamous puzzles: the classic Paradox of Supererogation (if it’s so good, why isn’t it just obligatory?), Horton’s All or Nothing Problem, and Kamm’s Intransitivity Paradox. I conclude that supererogation makes sense if, and only if, the grounds of rightness are multi-dimensional and comparative.

In Lukács: Praxis and the Absolute, Daniel Andrés López reassembles Lukács’s philosophy of praxis on a Hegelian basis, as a conceptual-historical totality, both defending him and proposing an unprecedented, immanent critique that raises problems for Marxian philosophy as a whole.

" Qualia " is an unfamiliar term for something that could not be more familiar to each of us: the ways things seem to us. As is so often the case with philosophical jargon, it is easier to give examples than to give a definition of the term. Look at a glass of milk at sunset; the way it looks to you--the particular, personal, subjective visual quality of the glass of milk is the quale of your visual experience at the (...) moment. The way the milk tastes to you then is another, gustatory quale, and how it sounds to you as you swallow is an auditory quale; These various "properties of conscious experience" are prime examples of qualia. Nothing, it seems, could you know more intimately than your own qualia ; let the entire universe be some vast illusion, some mere figment of Descartes' evil demon, and yet what the figment is made of will be the qualia of your hallucinatory experiences. Descartes claimed to doubt everything that could be doubted, but he never doubted that his conscious experiences had qualia, the properties by which he knew or apprehended them. (shrink)

The social sciences playa variety of multifaceted roles in the policymaking process. So varied are these roles, indeed, that it is futile to talk in the singular about the use of social science in policymaking, as if there were one constant relationship between two fixed and stable entities. Instead, to address this issue sensibly one must talk in the plural about uses of dif ferent modes of social scientific inquiry for different kinds of policies under various circumstances. In some cases, (...) the influence of social scientific research is direct and tangible, and the connection between the find ings and the policy is easy to see. In other cases, perhaps most, its influence is indirect-one small piece in a larger mosaic of politics, bargaining, and compromise. Occasionally the findings of social scientific studies are explicitly drawn upon by policymakers in the formation, implementation, or evaluation of particular policies. More often, the categories and theoretical models of social science provide a general background orientation within which policymakers concep tualize problems and frame policy options. At times, the in fluence of social scientific work is cognitive and informational in nature; in other instances, policymakers use social science primarily for symbolic and political purposes in order to le gitimate preestablished goals and strategies. Nonetheless, amid this diversity and variety, troubling general questions persistently arise. (shrink)

Supererogatory acts are those that lie “beyond the call of duty.” There are two standard ways to define this idea more precisely. Although the definitions are often seen as equivalent, I argue that they can diverge when options are infinite, or when there are cycles of better options; moreover, each definition is acceptable in only one case. I consider two ways out of this dilemma.

The potential to collect brain data more directly, with higher resolution, and in greater amounts has heightened worries about mental and brain privacy. In order to manage the risks to individuals posed by these privacy challenges, some have suggested codifying new privacy rights, including a right to “mental privacy.” In this paper, we consider these arguments and conclude that while neurotechnologies do raise significant privacy concerns, such concerns are—at least for now—no different from those raised by other well-understood data collection (...) technologies, such as gene sequencing tools and online surveillance. To better understand the privacy stakes of brain data, we suggest the use of a conceptual framework from information ethics, Helen Nissenbaum’s “contextual integrity” theory. To illustrate the importance of context, we examine neurotechnologies and the information flows they produce in three familiar contexts—healthcare and medical research, criminal justice, and consumer marketing. We argue that by emphasizing what is distinct about brain privacy issues, rather than what they share with other data privacy concerns, risks weakening broader efforts to enact more robust privacy law and policy. (shrink)

1. Happiness, then and now -- Happiness, eudaimonia, and practical reasoning -- Happiness as eudaimonia -- Happiness and virtuous activity -- New directions from old debates -- 2. Happiness then: the sufficiency debate -- Aristotle's case against the sufficiency thesis -- 3. Happiness now: rethinking the self -- Socrates' case for the sufficiency thesis -- Epictetus and the stoic self -- The Stoics' case for the sufficiency thesis -- The embodied conception of the self -- The embodied conception and psychological (...) well-being -- The Stoics' case against the embodied conception. (shrink)

Professor Little presents an introduction to the philosophy of social science with an emphasis on the central forms of explanation in social science: rational-intentional, causal, functional, structural, materialist, statistical and interpretive. The book is very strong on recent developments, particularly in its treatment of rational choice theory, microfoundations for social explanation, the idea of supervenience, functionalism, and current discussions of relativism.Of special interest is Professor Little’s insight that, like the philosophy of natural science, the philosophy of social science can profit (...) from examining actual scientific examples. Throughout the book, philosophical theory is integrated with recent empirical work on both agrarian and industrial society drawn from political science, sociology, geography, anthropology, and economics.Clearly written and well structured, this text provides the logical and conceptual tools necessary for dealing with the debates at the cutting edge of contemporary philosophy of social science. It will prove indispensible for philosophers, social scientists and their students. (shrink)

Public discussions of political and social issues are often characterized by deep and persistent polarization. In social psychology, it’s standard to treat belief polarization as the product of epistemic irrationality. In contrast, we argue that the persistent disagreement that grounds political and social polarization can be produced by epistemically rational agents, when those agents have limited cognitive resources. Using an agent-based model of group deliberation, we show that groups of deliberating agents using coherence-based strategies for managing their limited resources tend (...) to polarize into different subgroups. We argue that using that strategy is epistemically rational for limited agents. So even though group polarization looks like it must be the product of human irrationality, polarization can be the result of fully rational deliberation with natural human limitations. (shrink)

From an early age, humans know a surprising amount about basic physical principles, such as gravity, force, mass, and shape. We can see this in the way that young children play, and manipulate objects around them. The same behaviour has long been observed in primates - chimpanzees have been shown to possess a remarkable ability to make and use simple tools. But what does this tell us about their inner mental state - do they therefore share the same understanding to (...) that of a young child? Do they understand the simple, underlying physical principles involved? Though some people would say that they do, this book reports groundbreaking research that questions whether this really is the case. -/- Folk Physics for Apes challenges the assumptions so often made about apes. It offers us a rare glimpse into the workings of another mind, examining how apes perceive and understand the physical world - an understanding that appears to be both similar to, and yet profoundly different from our own. The book will have broad appeal to evolutionary psychologists, developmental psychologists, and those interested in the sub-disciplines of cognitive science (philosophy, anthropology). The book additionally offers for developmental psychologists some valuable new non-verbal techniques for assessing causal understanding in young children. (shrink)