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This book extends Bayesian epistemology to develop new approaches to general rational learning within the framework of probability theory.
According to Bayesian epistemology, rational learning from experience is consistent learning, that is learning should incorporate new information consistently into one's old system of beliefs. Simon M. Huttegger argues that this core idea can be transferred to situations where the learner's informational inputs are much more limited than Bayesianism assumes, thereby significantly expanding the reach of a Bayesian type of epistemology. What results from this is a unified account of probabilistic learning in the tradition of Richard Jeffrey's 'radical probabilism'. Along the way, Huttegger addresses a number of debates in epistemology and the philosophy of science, including the status of prior probabilities, whether Bayes' rule is the only legitimate form of learning from experience, and whether rational agents can have sustained disagreements. His book will be of interest to students and scholars of epistemology, of game and decision theory, and of cognitive, economic, and computer sciences.
Beliefs, Interactions and Preferences in Decision Making mixes a selection of papers, presented at the Eighth Foundations and Applications of Utility and Risk Theory (`FUR VIII') conference in Mons, Belgium, together with a few solicited papers from well-known authors in the field. This book addresses some of the questions that have recently emerged in the research on decision-making and risk theory. In particular, authors have modeled more and more as interactions between the individual and the environment or between different individuals the emergence of beliefs as well as the specific type of information treatment traditionally called `rationality'. This book analyzes several cases of such an interaction and derives consequences for the future of decision theory and risk theory. In the last ten years, modeling beliefs has become a specific sub-field of decision making, particularly with respect to low probability events. Rational decision making has also been generalized in order to encompass, in new ways and in more general situations than it used to be fitted to, multiple dimensions in consequences. This book deals with some of the most conspicuous of these advances. It also addresses the difficult question to incorporate several of these recent advances simultaneously into one single decision model. And it offers perspectives about the future trends of modeling such complex decision questions. The volume is organized in three main blocks: The first block is the more `traditional' one. It deals with new extensions of the existing theory, as is always demanded by scientists in the field. A second block handles specific elements in the development of interactions between individuals and their environment, as defined in the most general sense. The last block confronts real-world problems in both financial and non-financial markets and decisions, and tries to show what kind of contributions can be brought to them by the type of research reported on here.
This volume brings together a collection of essays on the history and philosophy of probability and statistics by one of the eminent scholars in these subjects. Written over the last fifteen years, they fall into three broad categories. The first deals with the use of symmetry arguments in inductive probability, in particular, their use in deriving rules of succession. The second group deals with three outstanding individuals who made lasting contributions to probability and statistics in very different ways: Frank Ramsey, R.A. Fisher, Alan Turing, and Abraham de Moivre. The last group of essays deals with the problem of "predicting the unpredictable."
This important new book introduces, analyzes and takes forward a post-Keynesian theory of the firm. It makes a vital contribution to the conceptualisation of uncertainty that is consistent with the methodological presuppositions of Post Keynesian economics. The author attempts to make a positive contribution to the development of Post Keynesian economics by refuting allegations of incoherence, detailing some of the salient implications of a transmutable conception of economic processes and then starting to explore what this means for how Post Keynesians conceptualise uncertainty. The book argues that the Post Keynesian distinctive view of time, understood as a non-deterministic open systems process, is a core and defining characteristic which is linked to its theoretical discussion of money and the principle of effective demand. Covering areas such as the coherence of Post Keynesianism, the future of Post Keynesian economics and Keynesian methodological debates, this book is useful reading for all Post Keynesian scholars with a strong interest in economic methodology and the philosophical underpinnings of economics.
Artificial intelligence (AI) is a complicated science that combines philosophy, cognitive psychology, neuroscience, mathematics and logic (logicism), economics, computer science, computability, and software. Meanwhile, robotics is an engineering field that compliments AI. There can be situations where AI can function without a robot (e.g., Turing Test) and robotics without AI (e.g., teleoperation), but in many cases, each technology requires each other to exhibit a complete system: having "smart" robots and AI being able to control its interactions (i.e., effectors) with its environment. This book provides a complete history of computing, AI, and robotics from its early development to state‐of‐the‐art technology, providing a roadmap of these complicated and constantly evolving subjects. Divided into two volumes covering the progress of symbolic logic and the explosion in learning/deep learning in natural language and perception, this first volume investigates the coming together of AI (the mind) and robotics (the body), and discusses the state of AI today. Key Features: Provides a complete overview of the topic of AI, starting with philosophy, psychology, neuroscience, and logicism, and extending to the action of the robots and AI needed for a futuristic society Provides a holistic view of AI, and touches on all the misconceptions and tangents to the technologies through taking a systematic approach Provides a glossary of terms, list of notable people, and extensive references Provides the interconnections and history of the progress of technology for over 100 years as both the hardware (Moore’s Law, GPUs) and software, i.e., generative AI, have advanced Intended as a complete reference, this book is useful to undergraduate and postgraduate students of computing, as well as the general reader. It can also be used as a textbook by course convenors. If you only had one book on AI and robotics, this set would be the first reference to acquire and learn about the theory and practice.
The Probabilistic Mind is a follow-up to the influential and highly cited Rational Models of Cognition (OUP, 1998). It brings together developmetns in understanding how, and how far, high-level cognitive processes can be understood in rational terms, and particularly using probabilistic Bayesian methods.
This book challenges the generally accepted theories of classical economics, explaining why the expected utility theory, even if it were true, fails to be of much help in solving economic controversies.
This book provides an overview of the theoretical underpinnings of modern probabilistic programming and presents applications in e.g., machine learning, security, and approximate computing. Comprehensive survey chapters make the material accessible to graduate students and non-experts. This title is also available as Open Access on Cambridge Core.
This is an authoritative collection of papers addressing the key challenges that face the Bayesian interpretation of probability today. The volume includes important criticisms of Bayesian reasoning and gives an insight into some of the points of disagreement amongst advocates of the Bayesian approach. It will be of interest to graduate students, researchers, those involved with the applications of Bayesian reasoning, and philosophers.