Search results for ""author john hooker""

Elgar Advanced Introductions are stimulating and thoughtful introductions to major fields in the social sciences, business and law, expertly written by the world’s leading scholars. Designed to be accessible yet rigorous, they offer concise and lucid surveys of the substantive and policy issues associated with discrete subject areas.This concise and engaging Advanced Introduction provides the conceptual tools necessary to make ethical decisions in today’s business world. John Hooker provides an objective and closely-reasoned analysis of ethical issues based on a unified conceptual framework that distils the best of ethical thought into three clearly articulated principles: the generalization, utilitarian, and autonomy principles. Key features include: examples and case studies that illustrate ethical reasoning in complex business dilemmas exploration of business ethics in relation to environmental, social, and financial sustainability factors coverage of cross-cultural business ethics, technological unemployment, and the ethics of artificial intelligence and machine learning. This Advanced Introduction will be a valuable resource for academics and advanced students of business ethics and trust, business leadership, and corporate social responsibility. It will also be beneficial for business managers who wish to build an ethical organization, as well as technical personnel who incorporate ethics into automated systems.

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Elgar Advanced Introductions are stimulating and thoughtful introductions to major fields in the social sciences, business and law, expertly written by the world’s leading scholars. Designed to be accessible yet rigorous, they offer concise and lucid surveys of the substantive and policy issues associated with discrete subject areas.This concise and engaging Advanced Introduction provides the conceptual tools necessary to make ethical decisions in today’s business world. John Hooker provides an objective and closely-reasoned analysis of ethical issues based on a unified conceptual framework that distils the best of ethical thought into three clearly articulated principles: the generalization, utilitarian, and autonomy principles. Key features include: examples and case studies that illustrate ethical reasoning in complex business dilemmas exploration of business ethics in relation to environmental, social, and financial sustainability factors coverage of cross-cultural business ethics, technological unemployment, and the ethics of artificial intelligence and machine learning. This Advanced Introduction will be a valuable resource for academics and advanced students of business ethics and trust, business leadership, and corporate social responsibility. It will also be beneficial for business managers who wish to build an ethical organization, as well as technical personnel who incorporate ethics into automated systems.

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A pioneering look at the fundamental role of logic in optimization and constraint satisfaction While recent efforts to combine optimization and constraint satisfaction have received considerable attention, little has been said about using logic in optimization as the key to unifying the two fields. Logic-Based Methods for Optimization develops for the first time a comprehensive conceptual framework for integrating optimization and constraint satisfaction, then goes a step further and shows how extending logical inference to optimization allows for more powerful as well as flexible modeling and solution techniques. Designed to be easily accessible to industry professionals and academics in both operations research and artificial intelligence, the book provides a wealth of examples as well as elegant techniques and modeling frameworks ready for implementation. Timely, original, and thought-provoking, Logic-Based Methods for Optimization: * Demonstrates the advantages of combining the techniques in problem solving * Offers tutorials in constraint satisfaction/constraint programming and logical inference * Clearly explains such concepts as relaxation, cutting planes, nonserial dynamic programming, and Bender's decomposition * Reviews the necessary technologies for software developers seeking to combine the two techniques * Features extensive references to important computational studies * And much more

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Merging logic and mathematics in deductive inference-an innovative, cutting-edge approach. Optimization methods for logical inference? Absolutely, say Vijay Chandru and John Hooker, two major contributors to this rapidly expanding field. And even though "solving logical inference problems with optimization methods may seem a bit like eating sauerkraut with chopsticks. . . it is the mathematical structure of a problem that determines whether an optimization model can help solve it, not the context in which the problem occurs." Presenting powerful, proven optimization techniques for logic inference problems, Chandru and Hooker show how optimization models can be used not only to solve problems in artificial intelligence and mathematical programming, but also have tremendous application in complex systems in general. They survey most of the recent research from the past decade in logic/optimization interfaces, incorporate some of their own results, and emphasize the types of logic most receptive to optimization methods-propositional logic, first order predicate logic, probabilistic and related logics, logics that combine evidence such as Dempster-Shafer theory, rule systems with confidence factors, and constraint logic programming systems. Requiring no background in logic and clearly explaining all topics from the ground up, Optimization Methods for Logical Inference is an invaluable guide for scientists and students in diverse fields, including operations research, computer science, artificial intelligence, decision support systems, and engineering.

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