رکورد قبلیرکورد بعدی

" Decision making and imperfection "


Document Type : BL
Record Number : 747835
Doc. No : b567786
Main Entry : Tatiana V. Guy, Miroslav Kárný, and David H. Wolpert (eds.).
Title & Author : Decision making and imperfection\ Tatiana V. Guy, Miroslav Kárný, and David H. Wolpert (eds.).
Publication Statement : Berlin ; New York : Springer, ©2013.
Series Statement : Studies in computational intelligence, 474.
Page. NO : xv, 187 pages : illustrations (some color) ; 24 cm.
ISBN : 3642364055
: : 3642364063
: : 3642446388
: : 9783642364051
: : 9783642364068
: : 9783642446382
Contents : Dynamic Bayesian Combination of Multiple Imperfect Classifiers / Edwin Simpson, Stephen Roberts, Ioannis Psorakis --; Distributed Decision Making by Categorically-Thinking Agents / Joong Bum Rhim, Lav R. Varshney, Vivek K. Goyal --; Automated Preference Elicitation for Decision Making / Miroslav Kárný --; Counter-Factual Reinforcement Learning: How to Model Decision-Makers That Anticipate the Future / Ritchie Lee, David H. Wolpert, James Bono --; Effect of Emotion and Personality on Deviation from Purely Rational Decision-Making / Marina Fiori, Alessandra Lintas, Sarah Mesrobian --; An Adversarial Risk Analysis Model for an Autonomous Imperfect Decision Agent / Javier G. Rázuri, Pablo G. Esteban, David Ríos Insua.
Abstract : Decision making (DM) is ubiquitous in both natural and artificial systems. The decisions made often differ from those recommended by the axiomatically well-grounded normative Bayesian decision theory, in a large part due to limited cognitive and computational resources of decision makers (either artificial units or humans). This state of a airs is often described by saying that decision makers are imperfect and exhibit bounded rationality. The neglected influence of emotional state and personality traits is an additional reason why normative theory fails to model human DM process. The book is a joint effort of the top researchers from different disciplines to identify sources of imperfection and ways how to decrease discrepancies between the prescriptive theory and real-life DM. The contributions consider: ʺhow a crowd of imperfect decision makers outperforms experts' decisions; ʺhow to decrease decision makers' imperfection by reducing knowledge available; ʺhow to decrease imperfection via automated elicitation of DM preferences; ʺa human's limited willingness to master the available decision-support tools as an additional source of imperfection; ʺhow the decision maker's emotional state influences the rationality; a DM support of edutainment robot based on its system of values and respecting emotions. The book will appeal to anyone interested in the challenging topic of DM theory and its applications.
Subject : Bayesian statistical decision theory.
Subject : Decision making -- Data processing.
Subject : Uncertainty (Information theory)
LC Classification : ‭T57.95‬‭T385 2013‬
Added Entry : David H Wolpert
: M Kárný
: Tatiana Valentine Guy
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