Table of contents
About this book
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.
Editors and affiliations
- Book Title Decision Making: Uncertainty, Imperfection, Deliberation and Scalability
Tatiana V. Guy
David H. Wolpert
- Series Title Studies in Computational Intelligence
- Series Abbreviated Title Studies Comp.Intelligence
- DOI https://doi.org/10.1007/978-3-319-15144-1
- Copyright Information Springer International Publishing Switzerland 2015
- Publisher Name Springer, Cham
- eBook Packages Engineering Engineering (R0)
- Hardcover ISBN 978-3-319-15143-4
- Softcover ISBN 978-3-319-35020-2
- eBook ISBN 978-3-319-15144-1
- Series ISSN 1860-949X
- Series E-ISSN 1860-9503
- Edition Number 1
- Number of Pages XII, 184
- Number of Illustrations 28 b/w illustrations, 13 illustrations in colour
- Buy this book on publisher's site