Escaping from Bad Decisions e-bog
1240,73 DKK
(inkl. moms 1550,91 DKK)
Escaping from Bad Decisions presents a modern conceptual and mathematical framework of the decision-making process. By interpreting ordinal utility theory as normative analysis examined in view of rationality, it shows how decision-making under certainty, risk, and uncertainty can be better understood. It provides a critical examination of psychological models in multi-attribute decision-making...
E-bog
1240,73 DKK
Forlag
Academic Press
Udgivet
27 juli 2021
Længde
542 sider
Genrer
Microeconomics
Sprog
English
Format
epub
Beskyttelse
LCP
ISBN
9780128160336
Escaping from Bad Decisions presents a modern conceptual and mathematical framework of the decision-making process. By interpreting ordinal utility theory as normative analysis examined in view of rationality, it shows how decision-making under certainty, risk, and uncertainty can be better understood. It provides a critical examination of psychological models in multi-attribute decision-making, and evaluates the constitutive elements of "e;good"e; and "e;bad"e; decisions. Multi-attribute decision-making is analysed descriptively, based on the psychological model of decision-making and computer simulations of decision strategies. Finally, prescriptive examinations of multi-attribute decision-making are performed, supporting the argument that decision-making from a pluralistic perspective creates results that can help "e;escape"e; from bad decisions. This book will be of particular interest to graduate students and early career researchers in economics, decision-theory, behavioral economics, experimental economics, psychology, cognitive sciences, and decision neurosciences. Provides a comprehensive background to the phenomena of bad decisions, considered in their economic, psychological and cognitive aspects Reinterprets existing theories and phenomena and proposes a new overview of decision behaviors by integrating mathematical and psychological perspectives Adapts model-based techniques, such as mathematical model based functional magnetic resonance imaging (fMRI) using mathematical models of the decision process