Decision Making: Uncertainty, Imperfection, Deliberation and Scalability

Decision Making: Uncertainty, Imperfection, Deliberation and Scalability

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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: a€c task allocation to maximize a€œthe wisdom of the crowda€; a€c design of a society of a€œedutainmenta€ robots who account for one anothersa€™ emotional states; a€c recognizing and counteracting seemingly non-rational human decision making; a€c coping with extreme scale when learning causality in networks; a€c efficiently incorporating expert knowledge in personalized medicine; a€c 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.Chapter. 1. Bayesian. Methods. for. Intelligent. Task. Assignment. in. Crowdsourcing. Systems. Edwin Simpson and Stephen Roberts Abstract In many decision-making scenarios, it is necessary to aggregate information from a number ofanbsp;...

Title:Decision Making: Uncertainty, Imperfection, Deliberation and Scalability
Author: Tatiana V. Guy, Miroslav Karny, David Wolpert
Publisher:Springer - 2015-02-09

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