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?"
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