Consumer Optimization Problem Solving

by Alfred L Norman

Overview

What algorithms are tractable depends on the speed of the processor. Given the speed of digital computers, polynomial algorithms are considered tractable. But, a human can take several seconds to make one binary comparison between two pens. Given this slow speed, sublinear algorithms are considered tractable for an unaided human and this defines Simon's concept of bounded rationality.Humans make simplifications to solve the intractable consumer optimization problem. Consumers search for goods and services item-by-item, which greatly reduces the number of alternatives to consider. In addition, consumers have operators that can process a set in a single operation. Also, consumers budget by incremental adjustment.In considering consumer performance the question to ask is how close to optimal is consumer performance and not whether consumers optimize as a yes/no question. Given the ordinal nature of utility theory this creates a basic measurement problem. The book presents a review of the literature on consumer performance.This is an opportune time to study consumer procedures because the Internet provides a media to make substantial improvements in consumer performance. The book includes a case study comparing the performance of a digital camera selection code with the advice of sales people. A field experiment demonstrates that the software code provides better advice.

Product Details

ISBN-13:	9789814635288
Publisher:	World Scientific Publishing Company, Incorporated
Publication date:	01/21/2015
Pages:	252
Product dimensions:	6.00(w) x 9.00(h) x 0.63(d)

Table of Contents

Acknowledgements v

1 Introduction 1

1.1 Introduction 1

1.2 Computational Complexity and Consumers 2

1.3 Component Studies 4

1.4 Item Search and Budgeting 6

1.5 How Close to Optimal and Better Performance 9

2 Computational Complexity 11

2.1 Introduction 11

2.2 Computational Complexity 12

2.3 Discrete Consumer Problem 20

2.4 Bounded-Rational Human Procedures 24

3 Ordering 29

3.1 Introduction 29

3.2 Computer Ordering 30

3.3 Experimental Ordering Design 33

3.4 Observations 37

3.5 Analysis of Algorithms 40

3.6 Additional Analysis 44

3.7 Concluding Remarks 47

4 Computational Complexity: Decision Rules 49

4.1 Introduction 49

4.2 Linear Item Search 49

4.3 A Sublinear Item Search 56

4.4 Market Organization 65

4.5 Conclusions 70

5 Repeated Price Search 71

5.1 Introduction 71

5.2 Experiments 72

5.3 Buying Textbooks Online 80

5.4 Conclusion 89

6 Repeated Item Search: Forecasting 91

6.1 Introduction 91

6.2 Forecasts, Judgments, Knowledge, and Data 92

6.3 Sequential Trial and Error, STE Strategy 93

6.4 Forecasting with Data Provided by Others 98

6.5 Search Variation Across Consumers 110

6.6 Conclusions 113

7 Repeated Item Search: Choice 115

7.1 Introduction 115

7.2 Steps: Sets 117

7.3 Steps: Preferred Item From Final Set 123

7.4 Search Efficiency: Jeans 127

7.5 Conclusions 136

8 Budgeting 139

8.1 Introduction 139

8.2 Budgeting Surveys 140

8.3 Budget Procedures 143

8.4 Monitoring 148

8.5 Adjustment Process 153

8.6 Conclusion 162

9 How Close to Optimal? 165

9.1 Introduction 165

9.2 Reference Model 166

9.3 The Measurement Problem 168

9.4 Tests of Utility Theory 171

9.5 Simplification Performance 173

9.6 Overall Assessment 186

10 Improving Consumer Performance 189

10.1 Introduction 189

10.2 Government and Consumers 191

10.3 Business and Consumers 193

10.4 Near Term Changes 203

10.5 Long Term Improvements 216

10.6 Research 220

10.7 Conclusion 222

Appendix A CC of the Discrete Two-Stage Budgeting Problem 223

Bibliography 227

Index 237