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This title is printed to order. This book may have been self-published. If so, we cannot guarantee the quality of the content. In the main most books will have gone through the editing process however some may not. We therefore suggest that you be aware of this before ordering this book. If in doubt check either the author or publisher’s details as we are unable to accept any returns unless they are faulty. Please contact us if you have any questions.
Introduction to Decision Making
Scenario Planning and Prediction Markets Group Mechanics Heuristics and Programmed Decisions Probability and Base Rate Neglect
Decision Making Essentials You Always Wanted to Know prepares new managers and leaders to assess tough decisions they face- and eventually make the right choice- by providing them with a tool box of decision analysis techniques to help them understand
and analyze the decisions they make.
The chapters describe key techniques of decision analysis, including:
Cognitive biases and Prospect Theory
Heuristics
Probability and Expected Value
Bayes Theorem
Multi-attribute decision making, including the SMART, Elimination by Aspects, and Even Swaps methods
Game Theory
Prediction Markets Brainstorming and Groupthink
Black Swan Events
Each chapter provides clear examples of the decision making tools and includes practice examples to help train the reader in using these critical tools.
About the Series
The Self-Learning Management series is designed to help students, new managers, career switchers and entrepreneurs learn essential management lessons. This series has management books, finance and accounting books, and strategy and marketing books designed to address every aspect of business from HR to Finance to Marketing to Operations. Each book includes basic fundamentals, important concepts, standard and well-known principles as well as practical ways of application of the subject matter. The distinctiveness of the series lies in that all the relevant information is bundled in a compact form that is very easy to interpret.
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This title is printed to order. This book may have been self-published. If so, we cannot guarantee the quality of the content. In the main most books will have gone through the editing process however some may not. We therefore suggest that you be aware of this before ordering this book. If in doubt check either the author or publisher’s details as we are unable to accept any returns unless they are faulty. Please contact us if you have any questions.
Introduction to Decision Making
Scenario Planning and Prediction Markets Group Mechanics Heuristics and Programmed Decisions Probability and Base Rate Neglect
Decision Making Essentials You Always Wanted to Know prepares new managers and leaders to assess tough decisions they face- and eventually make the right choice- by providing them with a tool box of decision analysis techniques to help them understand
and analyze the decisions they make.
The chapters describe key techniques of decision analysis, including:
Cognitive biases and Prospect Theory
Heuristics
Probability and Expected Value
Bayes Theorem
Multi-attribute decision making, including the SMART, Elimination by Aspects, and Even Swaps methods
Game Theory
Prediction Markets Brainstorming and Groupthink
Black Swan Events
Each chapter provides clear examples of the decision making tools and includes practice examples to help train the reader in using these critical tools.
About the Series
The Self-Learning Management series is designed to help students, new managers, career switchers and entrepreneurs learn essential management lessons. This series has management books, finance and accounting books, and strategy and marketing books designed to address every aspect of business from HR to Finance to Marketing to Operations. Each book includes basic fundamentals, important concepts, standard and well-known principles as well as practical ways of application of the subject matter. The distinctiveness of the series lies in that all the relevant information is bundled in a compact form that is very easy to interpret.