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Decision making[electronic resource] :uncertainty, imperfection, deliberation and scalability /

纪录类型:	书目-语言数据,印刷品 : Monograph/item
[NT 15000414] null:	658.403
[NT 47271] Title/Author:	Decision making : uncertainty, imperfection, deliberation and scalability // edited by Tatiana V. Guy, Miroslav Kárný, David H. Wolpert.
[NT 51406] other author:	Guy, Tatiana V.
出版者:	Cham : : Springer International Publishing :, 2015.
面页册数:	xii, 184 p. : : ill. (some col.), digital ;; 24 cm.
Contained By:	Springer eBooks
标题:	Decision making.
标题:	Artificial intelligence.
标题:	Decision support systems.
标题:	Engineering.
标题:	Computational Intelligence.
标题:	Artificial Intelligence (incl. Robotics).
ISBN:	9783319151441 (electronic bk.)
ISBN:	9783319151434 (paper)
[NT 15000228] null:	Bayesian Methods for Intelligent Task Assignment in Crowdsourcing Systems -- Designing Societies of Robots -- On the Origins of Imperfection and Apparent Non-Rationality -- Lasso Granger Causal Models: Some Strategies and their Efficiency for Gene Expression Regulatory Networks -- Cooperative Feature Selection in Personalized Medicine -- Imperfect Decision Making and Risk Taking are affected by Personality.
[NT 15000229] null:	This volume focuses on uncovering the fundamental forces underlying dynamic decision making among multiple interacting, imperfect and selﬁsh decision makers. The chapters are written by leading experts from different disciplines, all considering the many sources of imperfection in decision making, and always with an eye to decreasing the myriad discrepancies between theory and real world human decision making. Topics addressed include uncertainty, deliberation cost and the complexity arising from the inherent large computational scale of decision making in these systems. In particular, analyses and experiments are presented which concern: task allocation to maximize "the wisdom of the crowd"; design of a society of "edutainment" robots who account for one anothers' emotional states; recognizing and counteracting seemingly non-rational human decision making; coping with extreme scale when learning causality in networks; efﬁciently incorporating expert knowledge in personalized medicine; the effects of personality on risky decision making. The volume is a valuable source for researchers, graduate students and practitioners in machine learning, stochastic control, robotics, and economics, among other ﬁelds.
电子资源:	http://dx.doi.org/10.1007/978-3-319-15144-1

Decision making[electronic resource] :uncertainty, imperfection, deliberation and scalability /
Decision makinguncertainty, imperfection, deliberation and scalability /[electronic resource] :edited by Tatiana V. Guy, Miroslav Kárný, David H. Wolpert. - Cham :Springer International Publishing :2015. - xii, 184 p. :ill. (some col.), digital ;24 cm. - Studies in computational intelligence,v.5381860-949X ;. - Studies in computational intelligence ;v.379..

Bayesian Methods for Intelligent Task Assignment in Crowdsourcing Systems -- Designing Societies of Robots -- On the Origins of Imperfection and Apparent Non-Rationality -- Lasso Granger Causal Models: Some Strategies and their Efficiency for Gene Expression Regulatory Networks -- Cooperative Feature Selection in Personalized Medicine -- Imperfect Decision Making and Risk Taking are affected by Personality.

This volume focuses on uncovering the fundamental forces underlying dynamic decision making among multiple interacting, imperfect and selﬁsh decision makers. The chapters are written by leading experts from different disciplines, all considering the many sources of imperfection in decision making, and always with an eye to decreasing the myriad discrepancies between theory and real world human decision making. Topics addressed include uncertainty, deliberation cost and the complexity arising from the inherent large computational scale of decision making in these systems. In particular, analyses and experiments are presented which concern: task allocation to maximize "the wisdom of the crowd"; design of a society of "edutainment" robots who account for one anothers' emotional states; recognizing and counteracting seemingly non-rational human decision making; coping with extreme scale when learning causality in networks; efﬁciently incorporating expert knowledge in personalized medicine; the effects of personality on risky decision making. The volume is a valuable source for researchers, graduate students and practitioners in machine learning, stochastic control, robotics, and economics, among other ﬁelds.

ISBN: 9783319151441 (electronic bk.)

Standard No.: 10.1007/978-3-319-15144-1doiSubjects--Topical Terms:

337308
Decision making.

LC Class. No.: HD30.213

Dewey Class. No.: 658.403

Decision making[electronic resource] :uncertainty, imperfection, deliberation and scalability /
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520 $a This volume focuses on uncovering the fundamental forces underlying dynamic decision making among multiple interacting, imperfect and selﬁsh decision makers. The chapters are written by leading experts from different disciplines, all considering the many sources of imperfection in decision making, and always with an eye to decreasing the myriad discrepancies between theory and real world human decision making. Topics addressed include uncertainty, deliberation cost and the complexity arising from the inherent large computational scale of decision making in these systems. In particular, analyses and experiments are presented which concern: task allocation to maximize "the wisdom of the crowd"; design of a society of "edutainment" robots who account for one anothers' emotional states; recognizing and counteracting seemingly non-rational human decision making; coping with extreme scale when learning causality in networks; efﬁciently incorporating expert knowledge in personalized medicine; the effects of personality on risky decision making. The volume is a valuable source for researchers, graduate students and practitioners in machine learning, stochastic control, robotics, and economics, among other ﬁelds.
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