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Books > Reference & Interdisciplinary > Communication studies > Decision theory
Quantitative risk assessments cannot eliminate risk, nor can they
resolve trade-offs. They can, however, guide principled risk
management and reduction - if the quality of assessment is high and
decision makers understand how to use it. This book builds a
unifying scientific framework for discussing and evaluating the
quality of risk assessments and whether they are fit for purpose.
Uncertainty is a central topic. In practice, uncertainties about
inputs are rarely reflected in assessments, with the result that
many safety measures are considered unjustified. Other topics
include the meaning of a probability, the use of probability
models, the use of Bayesian ideas and techniques, and the use of
risk assessment in a practical decision-making context. Written for
professionals, as well as graduate students and researchers, the
book assumes basic probability, statistics and risk assessment
methods. Examples make concepts concrete, and three extended case
studies show the scientific framework in action.
Prospect Theory: For Risk and Ambiguity provides the first
comprehensive and accessible textbook treatment of the way
decisions are made both when we have the statistical probabilities
associated with uncertain future events (risk) and when we lack
them (ambiguity). The book presents models, primarily prospect
theory, that are both tractable and psychologically realistic. A
method of presentation is chosen that makes the empirical meaning
of each theoretical model completely transparent. Prospect theory
has many applications in a wide variety of disciplines. The
material in the book has been carefully organized to allow readers
to select pathways through the book relevant to their own
interests. With numerous exercises and worked examples, the book is
ideally suited to the needs of students taking courses in decision
theory in economics, mathematics, finance, psychology, management
science, health, computer science, Bayesian statistics, and
engineering.
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