Rational decision-making for managers : an introduction

Rational Decision Making for Managers provides students with a basic understanding of quantitative and analytical techniques that managers use to make complex business decisions and helps them to recognise when they are appropriate. Sarah Keast and Mike Towler also show the characteristics of the decisions that can be informed by the use of each technique, thereby guiding the reader in their choice.

Rational Decision Making for Managers features:

separate chapters on robustness analysis and game theory a strong contextual discussion and clear structure a concise mathematical appendix

The book is essential reading for students studying business decision making, quantitative methods and business research methods.

• About the Authors p. ix
• Acknowledgements p. x
• Preface p. xi
• Part 1 The Decision Context p. 1
• 1 Introduction to Decision-making p. 3
• Introduction p. 3
• What is a Decision? p. 4
• Uncertainty and Risk in Decision-taking p. 7
• Probability p. 8
• Rational Decision-taker p. 9
• Biases p. 10
• Descriptive, Normative, and Prescriptive Decision-making p. 12
• Models p. 13
• Who Should Participate in a Decision Process? p. 15
• Overview of Text p. 17
• Further Reading p. 18
• 2 Time Series Forecasting p. 21
• Introduction p. 21
• Creating a Time Series Forecast-Overview p. 22
• Data Types p. 22
• Identifying Trends p. 25
• Decomposition p. 31
• Finding the Seasonal Component p. 33
• Average and Moving Average Forecasts p. 39
• Simple Exponential Smoothing p. 41
• Exponential Smoothing for Data with a Trend p. 46
• Exponential Smoothing for Non-Stationary Data with Seasonality p. 49
• Standard Deviation of the Forecast p. 54
• Choosing Appropriate Forecasting Models p. 57
• Transforming Data p. 58
• Alternative Time Series Forecasting Techniques p. 59
• Summary p. 59
• Further Reading p. 60
• 3 Explanatory and Qualitative Forecasting p. 65
• Introduction p. 65
• Quantitative Explanatory Forecasting p. 66
• Linear Regression p. 67
• Elicitation of an Expert's Probabilities p. 80
• Structured Group Processes p. 82
• Scenario Analysis p. 84
• Summary p. 85
• Further Reading p. 86
• Part 2 One-off and Repeat Decisions p. 89
• 4 Inventory Management p. 91
• Introduction p. 91
• The Fixed Order Quantity Inventory System p. 92
• The Newsvendor Model p. 110
• The Economic Order Quantity Model with Known Stock-out Costs p. 114
• Summary p. 115
• Further Reading p. 116
• 5 Payoff Matrices p. 121
• Introduction p. 121
• Payoff Matrices with Certainty p. 123
• Payoff Matrices with Multiple Future States and a Dominant Strategy p. 123
• Payoff Matrices with Strict Uncertainty p. 124
• Payoff Matrices with Uncertainty or Risk p. 134
• Choosing a Decision Criterion p. 136
• The Value of Perfect Information p. 139
• Sensitivity Analysis p. 142
• Summary p. 144
• Further Reading p. 145
• 6 Linear Programming p. 151
• Introduction p. 151
• Solving the Linear Programme p. 154
• Corner Method p. 160
• Sensitivity Analysis p. 166
• Changing Constraints p. 166
• Changing the Objective Function p. 169
• Using Excel Solver p. 171
• Integer Programming p. 182
• Formulating Decision-making Problems as Linear Programmes p. 185
• Summary p. 187
• Further Reading p. 188
• 7 Simultaneous Move Games p. 193
• Introduction p. 193
• Terminology p. 194
• Zero-sum Games p. 195
• Non-zero-sum Games p. 200
• Mixed Strategies for Players with Three or More Strategies p. 207
• Continuous Strategies p. 212
• N-Player Games p. 214
• Summary p. 215
• Further Reading p. 216
• Part 3 Sequential Decisions p. 223
• 8 Robustness Analysis p. 225
• Introduction p. 225
• Robustness Analysis p. 227
• Robustness Analysis as a Framework: Literature Examples p. 234
• Further Reading p. 238
• 9 Decision Tree Analysis p. 239
• Introduction p. 239
• Decision Tree Notation p. 240
• Constructing a Decision Tree p. 240
• Rolling Back a Decision Tree 1: Sequential Decision and Event Nodes p. 241
• Rolling Back a Decision Tree 2: Waiting for Uncertainty to Resolve p. 244
• Rolling Back a Decision Tree 3: Exploratory Actions and Posterior Probabilities p. 245
• Introduction to Sensitivity Analysis p. 250
• Univariate Sensitivity Analysis p. 251
• Bivariate Sensitivity Analysis p. 260
• N-Way (Multivariate) Sensitivity Analysis p. 260
• Summary of the Decision Tree Analysis Method p. 262
• Chapter Appendix. A Brief Introduction to Monte Carlo Simulation p. 262
• Further Reading p. 266
• 10 Sequential Games p. 275
• Introduction p. 275
• Extensive Form p. 276
• Rollback Revisited p. 277
• Asymmetry of Information p. 280
• Signals p. 284
• Signal Jamming and Screening p. 287
• Bargaining p. 287
• Summary p. 290
• Further Reading p. 291
• Appendix: Mathematics Revision p. 297
• References p. 327
• Index p. 331