Library of American University of Madaba
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Guy, Tatiana Valentine.
Decision Making with Imperfect Decision Makers [electronic resource] / edited by Tatiana Valentine Guy, Miroslav Kárný, David H. Wolpert. - XIV, 198p. 50 illus., 39 illus. in color. online resource. - Intelligent Systems Reference Library, 28 1868-4394 ; .
1 Bounded Rationality in Multiagent Systems Using Decentralized Metareasoning -- 2 On Support of Imperfect Bayesian Participants -- 3 Trading value and information in MDPs -- 4 Game theoretic modeling of pilot behavior during mid-air encounters -- 5 Scalable Negotiation Protocol based on Issue-Grouping for Highly Nonlinear Situation -- 6 The Social Ultimatum Game -- 7 Neuroheuristics of Decision Making: from neuronal activity to EEG.
Prescriptive Bayesian decision making has reached a high level of maturity and is well-supported algorithmically. However, experimental data shows that real decision makers choose such Bayes-optimal decisions surprisingly infrequently, often making decisions that are badly sub-optimal. So prevalent is such imperfect decision-making that it should be accepted as an inherent feature of real decision makers living within interacting societies. To date such societies have been investigated from an economic and gametheoretic perspective, and even to a degree from a physics perspective. However, little research has been done from the perspective of computer science and associated disciplines like machine learning, information theory and neuroscience. This book is a major contribution to such research. Some of the particular topics addressed include: • How should we formalise rational decision making of a single imperfect decision maker? • Does the answer change for a system of imperfect decision makers? • Can we extend existing prescriptive theories for perfect decision makers to make them useful for imperfect ones? • How can we exploit the relation of these problems to the control under varying and uncertain resources constraints as well as to the problem of the computational decision making? • What can we learn from natural, engineered, and social systems to help us address these issues?
9783642246470
Engineering.
Artificial intelligence.
Engineering.
Computational Intelligence.
Artificial Intelligence (incl. Robotics).
006.3
Decision Making with Imperfect Decision Makers [electronic resource] / edited by Tatiana Valentine Guy, Miroslav Kárný, David H. Wolpert. - XIV, 198p. 50 illus., 39 illus. in color. online resource. - Intelligent Systems Reference Library, 28 1868-4394 ; .
1 Bounded Rationality in Multiagent Systems Using Decentralized Metareasoning -- 2 On Support of Imperfect Bayesian Participants -- 3 Trading value and information in MDPs -- 4 Game theoretic modeling of pilot behavior during mid-air encounters -- 5 Scalable Negotiation Protocol based on Issue-Grouping for Highly Nonlinear Situation -- 6 The Social Ultimatum Game -- 7 Neuroheuristics of Decision Making: from neuronal activity to EEG.
Prescriptive Bayesian decision making has reached a high level of maturity and is well-supported algorithmically. However, experimental data shows that real decision makers choose such Bayes-optimal decisions surprisingly infrequently, often making decisions that are badly sub-optimal. So prevalent is such imperfect decision-making that it should be accepted as an inherent feature of real decision makers living within interacting societies. To date such societies have been investigated from an economic and gametheoretic perspective, and even to a degree from a physics perspective. However, little research has been done from the perspective of computer science and associated disciplines like machine learning, information theory and neuroscience. This book is a major contribution to such research. Some of the particular topics addressed include: • How should we formalise rational decision making of a single imperfect decision maker? • Does the answer change for a system of imperfect decision makers? • Can we extend existing prescriptive theories for perfect decision makers to make them useful for imperfect ones? • How can we exploit the relation of these problems to the control under varying and uncertain resources constraints as well as to the problem of the computational decision making? • What can we learn from natural, engineered, and social systems to help us address these issues?
9783642246470
Engineering.
Artificial intelligence.
Engineering.
Computational Intelligence.
Artificial Intelligence (incl. Robotics).
006.3