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In the first ever book-length treatment of David Hume's philosophy of action, Constantine Sandis brings together seemingly disparate aspects of Hume's work to present an understanding of human action that is much richer than previously assumed. Sandis showcases Hume's interconnected views on action and its causes by situating them within a wider vision of our human understanding of personal identity, causation, freedom, historical explanation, and morality. In so doing, he also relates key aspects of the emerging picture to contemporary concerns within the philosophy of action and moral psychology, including debates between Humeans and anti-Humeans about both 'motivating' and 'normative' reasons. Character and Causation takes the form of a series of essays which collectively argue that Hume's overall project proceeds by way of a soft conceptual revisionism that emerges from his Copy Principle. This involves re-calibrating our philosophical ideas of all that agency involves to fit a scheme that more readily matches the range of impressions that human beings actually have. On such a reading, once we rid ourselves of a certain kind of metaphysical ambition we are left with a perfectly adequate account of how it is that people can act in character, freely, and for good reasons. The resulting picture is one that both unifies Hume's practical and theoretical philosophy and radically transforms contemporary philosophy of action for the better.
A Companion to the Philosophy of Action offers a comprehensive overview of the issues and problems central to the philosophy of action. The first volume to survey the entire field of philosophy of action (the central issues and processes relating to human actions) Brings together specially commissioned chapters from international experts Discusses a range of ideas and doctrines, including rationality, free will and determinism, virtuous action, criminal responsibility, Attribution Theory, and rational agency in evolutionary perspective Individual chapters also cover prominent historic figures from Plato to Ricoeur Can be approached as a complete narrative, but also serves as a work of reference Offers rich insights into an area of philosophical thought that has attracted thinkers since the time of the ancient Greeks
Annette Baier goes beyond her earlier work on David Hume to reflect on a topic that links his philosophy to questions of immediate relevance—in particular, questions about what character is and how it shapes our lives. Her reading radically revises the received interpretation of Hume's epistemology and, in particular, philosophy of mind.
We like to think of ourselves and our friends and families as pretty good people. The more we put our characters to the test, however, the more we see that we are decidedly a mixed bag. Fortunately there are some promising strategies - both secular and religious - for developing better characters.
An accessible, contemporary introduction to the methods for determining cause and effect in the Social Sciences “Causation versus correlation has been the basis of arguments—economic and otherwise—since the beginning of time. Causal Inference: The Mixtape uses legit real-world examples that I found genuinely thought-provoking. It’s rare that a book prompts readers to expand their outlook; this one did for me.”—Marvin Young (Young MC) Causal inference encompasses the tools that allow social scientists to determine what causes what. In a messy world, causal inference is what helps establish the causes and effects of the actions being studied—for example, the impact (or lack thereof) of increases in the minimum wage on employment, the effects of early childhood education on incarceration later in life, or the influence on economic growth of introducing malaria nets in developing regions. Scott Cunningham introduces students and practitioners to the methods necessary to arrive at meaningful answers to the questions of causation, using a range of modeling techniques and coding instructions for both the R and the Stata programming languages.
Causality offers the first comprehensive coverage of causal analysis in many sciences, including recent advances using graphical methods. Pearl presents a unified account of the probabilistic, manipulative, counterfactual and structural approaches to causation, and devises simple mathematical tools for analyzing the relationships between causal connections, statistical associations, actions and observations. The book will open the way for including causal analysis in the standard curriculum of statistics, artificial intelligence ...
The issues of mental causation, consciousness, and free will have vexed philosophers since Plato. This book examines these unresolved issues from a neuroscientific perspective. In contrast with philosophers who use logic rather than data to argue whether mental causation or consciousness can exist given unproven first assumptions, Tse proposes that we instead listen to what neurons have to say. Because the brain must already embody a solution to the mind--body problem, why not focus on how the brain actually realizes mental causation? Tse draws on exciting recent neuroscientific data concerning how informational causation is realized in physical causation at the level of NMDA receptors, synapses, dendrites, neurons, and neuronal circuits. He argues that a particular kind of strong free will and downward mental causation are realized in rapid synaptic plasticity. Recent neurophysiological breakthroughs reveal that neurons function as criterial assessors of their inputs, which then change the criteria that will make other neurons fire in the future. Such informational causation cannot change the physical basis of information realized in the present, but it can change the physical basis of information that may be realized in the immediate future. This gets around the standard argument against free will centered on the impossibility of self-causation. Tse explores the ways that mental causation and qualia might be realized in this kind of neuronal and associated information-processing architecture, and considers the psychological and philosophical implications of having such an architecture realized in our brains.
A Turing Award-winning computer scientist and statistician shows how understanding causality has revolutionized science and will revolutionize artificial intelligence "Correlation is not causation." This mantra, chanted by scientists for more than a century, has led to a virtual prohibition on causal talk. Today, that taboo is dead. The causal revolution, instigated by Judea Pearl and his colleagues, has cut through a century of confusion and established causality -- the study of cause and effect -- on a firm scientific basis. His work explains how we can know easy things, like whether it was rain or a sprinkler that made a sidewalk wet; and how to answer hard questions, like whether a drug cured an illness. Pearl's work enables us to know not just whether one thing causes another: it lets us explore the world that is and the worlds that could have been. It shows us the essence of human thought and key to artificial intelligence. Anyone who wants to understand either needs The Book of Why.
The authors demonstrate that Hume's views can stand up to contemporary criticism and are relevant to current debates on causality.
This book goes beyond the truism that 'correlation does not imply causation' and explores the logical and methodological relationships between correlation and causation. It presents a series of statistical methods that can test, and potentially discover, cause-effect relationships between variables in situations in which it is not possible to conduct randomised or experimentally controlled experiments. Many of these methods are quite new and most are generally unknown to biologists. In addition to describing how to conduct these statistical tests, the book also puts the methods into historical context and explains when they can and cannot justifiably be used to test or discover causal claims. Written in a conversational style that minimises technical jargon, the book is aimed at practising biologists and advanced students, and assumes only a very basic knowledge of introductory statistics.