Gale Researcher Guide for: Hume on Causation and Induction is selected from Gale's academic platform Gale Researcher. These study guides provide peer-reviewed articles that allow students early success in finding scholarly materials and to gain the confidence and vocabulary needed to pursue deeper research.

Gale Researcher Guide for: David Hume: Overview is selected from Gale's academic platform Gale Researcher. These study guides provide peer-reviewed articles that allow students early success in finding scholarly materials and to gain the confidence and vocabulary needed to pursue deeper research.

This investigation into causal modelling presents the rationale of causality, i.e. the notion that guides causal reasoning in causal modelling. It is argued that causal models are regimented by a rationale of variation, nor of regularity neither invariance, thus breaking down the dominant Human paradigm. The notion of variation is shown to be embedded in the scheme of reasoning behind various causal models. It is also shown to be latent – yet fundamental – in many philosophical accounts. Moreover, it has significant consequences for methodological issues: the warranty of the causal interpretation of causal models, the levels of causation, the characterisation of mechanisms, and the interpretation of probability. This book offers a novel philosophical and methodological approach to causal reasoning in causal modelling and provides the reader with the tools to be up to date about various issues causality rises in social science.

Data-intensive science has the potential to transform scientific research and quickly translate scientific progress into complete solutions, policies, and economic success. But this collaborative science is still lacking the effective access and exchange of knowledge among scientists, researchers, and policy makers across a range of disciplines. Bringing together leaders from multiple scientific disciplines, Data-Intensive Science shows how a comprehensive integration of various techniques and technological advances can effectively harness the vast amount of data being generated and significantly accelerate scientific progress to address some of the world's most challenging problems. In the book, a diverse cross-section of application, computer, and data scientists explores the impact of data-intensive science on current research and describes emerging technologies that will enable future scientific breakthroughs. The book identifies best practices used to tackle challenges facing data-intensive science as well as gaps in these approaches. It also focuses on the integration of data-intensive science into standard research practice, explaining how components in the data-intensive science environment need to work together to provide the necessary infrastructure for community-scale scientific collaborations. Organizing the material based on a high-level, data-intensive science workflow, this book provides an understanding of the scientific problems that would benefit from collaborative research, the current capabilities of data-intensive science, and the solutions to enable the next round of scientific advancements.

By applying research in artificial intelligence to problems in the philosophy of science, Paul Thagard develops an exciting new approach to the study of scientific reasoning. This approach uses computational ideas to shed light on how scientific theories are discovered, evaluated, and used in explanations. Thagard describes a detailed computational model of problem solving and discovery that provides a conceptually rich yet rigorous alternative to accounts of scientific knowledge based on formal logic, and he uses it to illuminate such topics as the nature of concepts, hypothesis formation, analogy, and theory justification.

The twenty-eight essays in this Handbook, all by leading experts in the field, provide the most extensive treatment of various epistemological problems, supplemented by a historical account of this field. The entries are self-contained and substantial contributions to topics such as the sources of knowledge and belief, knowledge acquisition, and truth and justification. There are extensive essays on knowledge in specific fields: the sciences, mathematics, the humanities and the social sciences, religion, and language. Special attention is paid to current discussions on evolutionary epistemology, relativism, the relation between epistemology and cognitive science, sociology of knowledge, epistemic logic, knowledge and art, and feminist epistemology. This collection is a must-have for anybody interested in human knowledge, and its fortunes and misfortunes.

Machine learning algorithms and artificial intelligence influence many aspects of life today. This report identifies some of their shortcomings and associated policy risks and examines some approaches for combating these problems.

Despite decades of research into the nature and treatment of stuttering, the causes and underlying mechanisms of it are still not well understood. In this unique and comprehensive overview of the numerous theories and models which seek to understand and explain stuttering, the authors of Theoretical Issues in Stuttering provide an invaluable account. Covering an impressive range of topics including past and current theories of stuttering, this edition provides the reader with an updated evaluation of the literature on the subject of stuttering alongside exploring the evolution of new theories. Placing each within the relevant historical context, the authors explore the contribution of theory to both understanding and managing stuttering. Theoretical Issues in Stuttering is a critical account of the models and theories which surround the subject of stuttering, aiming to act as a key resource for students of speech-language pathology as well as lecturers, clinicians and researchers within the field.