David. H Wolpert – författare

This book provides different mathematical frameworks for addressing supervised learning. It is based on a workshop held under the auspices of the Center for Nonlinear Studies at Los Alamos and the Santa Fe Institute in the summer of 1992.

This book provides different mathematical frameworks for addressing supervised learning. It is based on a workshop held under the auspices of the Center for Nonlinear Studies at Los Alamos and the Santa Fe Institute in the summer of 1992.

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.

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.

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?