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For courses in Decision Making and Engineering.
The Fundamentals of Analyzing and Making Decisions
Foundations of Decision Analysis is a groundbreaking text that explores the art of decision making, both in life and in professional settings. By exploring themes such as dealing with uncertainty and understanding the distinction between a decision and its outcome, the First Edition teaches students to achieve clarity of action in any situation.
The book treats decision making as an evolutionary process from a scientific standpoint. Strategic decision-making analysis is presented as a tool to help students understand, discuss, and settle on important life choices. Through this text, students will understand the specific thought process that occurs behind approaching any decision to make easier and better life choices for themselves.
Features + Benefits
Foundations of Decision Analysis contains the following features to facilitate learning:
An easy to read text accessible by all audiences
The book approaches the process of decision making from a mathematical standpoint, but many of its chapters steer clear of complex equations so basic fundamentals can be easily understood by a general audience.
Chapters 1-17 introduce foundations of decision analysis without mathematical or computational emphasis. Topics include characterizing a decision, the rules of actional thought, u-curves, sensitivity analysis, probability encoding, and framing.
Chapter 26 discusses multi-attribute decision problems with no uncertainty to prepare readers to approach these issues in real life when uncertainty is present.
Chapter 29 teachers readers to make decisions based on differing beliefs using rules of probability.
Chapter 33 explores decisions that involve a small probability of death.
Chapters and 37-39 applies the decision analysis approach to large group settings.
Chapter 40 discusses ethical consideration in decision making.
Readers with more mathematical and computational preparation can benefit from the latter half of the book after understanding basic fundamentals presented in chapters 1-17.
Chapters 18-25 discuss advanced information gathering from multiple sources, the concept of creating operations in our daily lives, u-curves that describe risk aversion, using approximate formulas for valuing deals, and the concept of probabilistic dominance relations to facilitate the best alternative.
Chapters 27 and 28 uses a value function for cash flows to determine and explain multi-attribute problems with uncertainty.
Chapter 30 teaches students to update probability after observing the results of an experiment.
Chapter 31 explores using the basic concepts for decision analysis to determine the best bid at the value of bidding opportunity at a variety of auction types.
Chapter 32 presents the concepts of risk scaling and sharing, exploring how decision makers can determine the best portion of an investment, how a partnership can share an investment, and how to establish the risk tolerance of a partnership.
Chapter 34 analyzes situations in which the decision maker is exposed to a high probability of death.
Chapters 35 and 36 teach students to solve decision making problems mathematically by using simulation and discretization.
Numerical problems are exposed in tabular format to help facilitate completion.
The “Decision Analysis Core Concepts Map” is a pedagogical feature that helps students understand major concepts
Provides a summary of major concepts that students can use as a reference of major points to grasp in each chapter.
Concepts are presented in chronological order to make for an easy flow of understanding key information.
Arrows are used between related concepts to show students what they must understand before approaching the next topic.
A text that teaches by real world example
Chapter 37 presents a case study that exemplifies the decision making tools presented throughout the book in a real life setting.
Part 1 Defining a Good Decision
Chapter 1: Introduction to Quality Decision Making
Chapter 2: Experiencing a Decision
Part 2 Clear Thinking and Characterization
Chapter 3: Clarifying Values
Chapter 4: Precise Decision Language
Chapter 5: Possibilities
Chapter 6: Handling Uncertainty
Chapter 7: Relevance
Part 3 Making any Decision
Chapter 8: Rules of Actional Thought
Chapter 9: The Party Problem
Chapter 10: Using a Value Measure
Part 4 Building on the Rules
Chapter 11: Risk Attitude
Chapter 12: Sensitivity Analysis
Chapter 13: Basic Information Gathering
Chapter 14: Decision Diagrams
Part 5 Characterizing What you Know
Chapter 15: Encoding a Probability Distribution on a Measure
Chapter 16: From Phenomenon to Assessment
Part 6 Framing a Decision
Chapter 17: Framing a Decision
Part 7 Advanced Information Gathering
Chapter 18: Valuing Information from Multiple Sources
Chapter 19: Options
Chapter 20: Detectors with Multiple Indications
Chapter 21: Decisions with Influences
Part 8 Characterizing What You Want
Chapter 22: The Logarithmic u-Curve
Chapter 23: The Linear Risk Tolerance u-Curve
Chapter 24: Approximate Expressions for the Certain
Chapter 25: Deterministic and Probabilistic Dominance
Chapter 26: Decisions with Multiple Attributes (1)–Ordering
Chapter 27: Decisions with Multiple Attributes (2)–Value Functions
Chapter 28: Decisions with Multiple Attributes (3)–Preference Equivalent
Prospects with Preference and Value Functions
for Investment Cash Flows: Time Preference
Probabilities Over Value
Part 9 Some Practical Extensions
Chapter 29: Betting on Disparate Belief
Chapter 30: Learning from Experimentation
Chapter 31: Auctions and Bidding
Chapter 32: Evaluating, Scaling, and Sharing Uncertain Deals
Chapter 33: Making Risky Decisions
Chapter 34: Decisions with a High Probability of Death
Part 10 Computing Decision Problems
Chapter 35: Discretizing Continuous Probability Distributions
Chapter 36: Solving Decision Problems by Simulation
Part 11 Professional Decisions
Chapter 37: The Decision Analysis Cycle
Chapter 38: Topics in Organizational Decision Making
Chapter 39: Coordinating the Decision Making of Large
Part 12 Ethical Considerations
Chapter 40: Decisions and Ethics Groups
For courses in Decision Making and Engineering. The Fundamentals of Analyzing and Making Decisions Foundations of Decision Analysis is a groundbreaking text that explores the art of decision making, both in life and in professional settings. By exploring themes such as dealing with uncertainty and understanding the distinction between a decision and its outcome, the First Edition teaches students to achieve clarity of action in any situation. The book treats decision making as an evolutionary process from a scientific standpoint. Strategic decision-making analysis is presented as a tool to help students understand, discuss, and settle on important life choices. Through this text, students will understand the specific thought process that occurs behind approaching any decision to make easier and better life choices for themselves.
The Fundamentals of Analyzing and Making Decisions
Foundations of Decision Analysis is a groundbreaking text that explores the art of decision making, both in life and in professional settings. By exploring themes such as dealing with uncertainty and understanding the distinction between a decision and its outcome, the First Edition teaches students to achieve clarity of action in any situation.
The book treats decision making as an evolutionary process from a scientific standpoint. Strategic decision-making analysis is presented as a tool to help students understand, discuss, and settle on important life choices. Through this text, students will understand the specific thought process that occurs behind approaching any decision to make easier and better life choices for themselves.
Features + Benefits
Foundations of Decision Analysis contains the following features to facilitate learning:
An easy to read text accessible by all audiences
The book approaches the process of decision making from a mathematical standpoint, but many of its chapters steer clear of complex equations so basic fundamentals can be easily understood by a general audience.
Chapters 1-17 introduce foundations of decision analysis without mathematical or computational emphasis. Topics include characterizing a decision, the rules of actional thought, u-curves, sensitivity analysis, probability encoding, and framing.
Chapter 26 discusses multi-attribute decision problems with no uncertainty to prepare readers to approach these issues in real life when uncertainty is present.
Chapter 29 teachers readers to make decisions based on differing beliefs using rules of probability.
Chapter 33 explores decisions that involve a small probability of death.
Chapters and 37-39 applies the decision analysis approach to large group settings.
Chapter 40 discusses ethical consideration in decision making.
Readers with more mathematical and computational preparation can benefit from the latter half of the book after understanding basic fundamentals presented in chapters 1-17.
Chapters 18-25 discuss advanced information gathering from multiple sources, the concept of creating operations in our daily lives, u-curves that describe risk aversion, using approximate formulas for valuing deals, and the concept of probabilistic dominance relations to facilitate the best alternative.
Chapters 27 and 28 uses a value function for cash flows to determine and explain multi-attribute problems with uncertainty.
Chapter 30 teaches students to update probability after observing the results of an experiment.
Chapter 31 explores using the basic concepts for decision analysis to determine the best bid at the value of bidding opportunity at a variety of auction types.
Chapter 32 presents the concepts of risk scaling and sharing, exploring how decision makers can determine the best portion of an investment, how a partnership can share an investment, and how to establish the risk tolerance of a partnership.
Chapter 34 analyzes situations in which the decision maker is exposed to a high probability of death.
Chapters 35 and 36 teach students to solve decision making problems mathematically by using simulation and discretization.
Numerical problems are exposed in tabular format to help facilitate completion.
The “Decision Analysis Core Concepts Map” is a pedagogical feature that helps students understand major concepts
Provides a summary of major concepts that students can use as a reference of major points to grasp in each chapter.
Concepts are presented in chronological order to make for an easy flow of understanding key information.
Arrows are used between related concepts to show students what they must understand before approaching the next topic.
A text that teaches by real world example
Chapter 37 presents a case study that exemplifies the decision making tools presented throughout the book in a real life setting.
Part 1 Defining a Good Decision
Chapter 1: Introduction to Quality Decision Making
Chapter 2: Experiencing a Decision
Part 2 Clear Thinking and Characterization
Chapter 3: Clarifying Values
Chapter 4: Precise Decision Language
Chapter 5: Possibilities
Chapter 6: Handling Uncertainty
Chapter 7: Relevance
Part 3 Making any Decision
Chapter 8: Rules of Actional Thought
Chapter 9: The Party Problem
Chapter 10: Using a Value Measure
Part 4 Building on the Rules
Chapter 11: Risk Attitude
Chapter 12: Sensitivity Analysis
Chapter 13: Basic Information Gathering
Chapter 14: Decision Diagrams
Part 5 Characterizing What you Know
Chapter 15: Encoding a Probability Distribution on a Measure
Chapter 16: From Phenomenon to Assessment
Part 6 Framing a Decision
Chapter 17: Framing a Decision
Part 7 Advanced Information Gathering
Chapter 18: Valuing Information from Multiple Sources
Chapter 19: Options
Chapter 20: Detectors with Multiple Indications
Chapter 21: Decisions with Influences
Part 8 Characterizing What You Want
Chapter 22: The Logarithmic u-Curve
Chapter 23: The Linear Risk Tolerance u-Curve
Chapter 24: Approximate Expressions for the Certain
Chapter 25: Deterministic and Probabilistic Dominance
Chapter 26: Decisions with Multiple Attributes (1)–Ordering
Chapter 27: Decisions with Multiple Attributes (2)–Value Functions
Chapter 28: Decisions with Multiple Attributes (3)–Preference Equivalent
Prospects with Preference and Value Functions
for Investment Cash Flows: Time Preference
Probabilities Over Value
Part 9 Some Practical Extensions
Chapter 29: Betting on Disparate Belief
Chapter 30: Learning from Experimentation
Chapter 31: Auctions and Bidding
Chapter 32: Evaluating, Scaling, and Sharing Uncertain Deals
Chapter 33: Making Risky Decisions
Chapter 34: Decisions with a High Probability of Death
Part 10 Computing Decision Problems
Chapter 35: Discretizing Continuous Probability Distributions
Chapter 36: Solving Decision Problems by Simulation
Part 11 Professional Decisions
Chapter 37: The Decision Analysis Cycle
Chapter 38: Topics in Organizational Decision Making
Chapter 39: Coordinating the Decision Making of Large
Part 12 Ethical Considerations
Chapter 40: Decisions and Ethics Groups
For courses in Decision Making and Engineering. The Fundamentals of Analyzing and Making Decisions Foundations of Decision Analysis is a groundbreaking text that explores the art of decision making, both in life and in professional settings. By exploring themes such as dealing with uncertainty and understanding the distinction between a decision and its outcome, the First Edition teaches students to achieve clarity of action in any situation. The book treats decision making as an evolutionary process from a scientific standpoint. Strategic decision-making analysis is presented as a tool to help students understand, discuss, and settle on important life choices. Through this text, students will understand the specific thought process that occurs behind approaching any decision to make easier and better life choices for themselves.