Tatiana V. Guy – författare

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.

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:

·          how a crowd of imperfect decision makers outperforms experts'' decisions;

·          how to decrease decision makers'' imperfection by reducing knowledge available;

·          how to decrease imperfection via automated elicitation of DM preferences;

·          a human''s limited willingness to master the available decision-support tools as an additional source of imperfection;

·          how 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.