Implement AI and big data at your organization using principles from behavioral economics
In Behavioral AI: Unleash Decision Making with Data, behavioral economist Dr. Rogayeh Tabrizi delivers an intuitive roadmap to help organizations disentangle the complexity of their data to create tangible and lasting value. The book explains how to balance the multiple disciplines that power AI and behavioral economics using a combination of the right questions and insightful problem solving.
You’ll learn why intellectual diversity and combining subject matter experts in psychology, behavior, economics, physics, computer science, and engineering is essential to creating advanced AI solutions. You’ll also discover:
- How behavioral economics principles influence data models and governance architectures and make digital transformation processes more efficient and effective
- Discussions of the most important barriers to value in typical big data and AI projects and how to bring them down
- The most effective methodology to help shorten the long, wasteful process of “boiling the ocean of data”
An exciting and essential resource for managers, executives, board members, and other business leaders engaged or interested in harnessing the power of artificial intelligence and big data, Behavioral AI will also benefit data and machine learning professionals.
Spis treści
Preface xi
Chapter 1 Magic Happens at the Intersections 1
Asking the Right Questions: Data, Intuition, and Strategy 3
Simplifying the Complexity 6
Connecting the Dots 8
Uncovering Hidden Patterns: Models and Algorithms in Action 10
Decoding Consumer Behavior: The Interplay of Psychology and Economics 11
Empowering Behavioral Economics: The Synergy of Data Analytics, ML, and AI 14
Crafting a Customer- Centric Paradigm: The Fusion of Technology and Behavioral Insights 17
Chapter 2 It Is All Connected: Behavioral Economics, Decision-Making, Biases, and Heuristics 19
History and Origins of Behavioral Economics 20
Early Days 21
Entering Mainstream Economics 23
Current Research and Practical Applications 26
Back to the Beginning 30
Psychology of Decision- Making 33
Dual- Process Theories 33
Heuristics and Biases 35
Noise 39
Prospect Theory 40
Nudging 42
Experimentation 46
How It Works 47
Uber and Experimentation 49
Chapter 3 Minimal Data, Maximal Impact: From Big Data to Minimum Viable Data 53
How Much Data Are We Talking About? Lots and Lots 55
You Do Not Need a Lot of Data to Get Started, You Need the MVD 58
Asking the Right Questions, Again! 59
Synthetic Data: What It Is and What It Isn’t 63
Survey Data to the Rescue 67
Chapter 4 Building Intelligence: AI and ML Essentials, Transforming Data into Intelligence 73
Classical AI 76
ML 78
Deep Learning 82
Generative AI 86
Machine Intelligence and Biologically Inspired Models 90
Chapter 5 Real-World Impact: Harnessing AI and ml for Practical Solutions 95
Unleashing the Full Potential of AI: Beyond the Hype 96
Rethinking Segmentation: Beyond Demographics and Life Stages 97
Uncovering Unexpected Customer Patterns 101
Predicting Intent and Mapping Customer Journeys 104
Overcoming Challenges in Predicting Customer Intent 106
Predicting and Managing Returns 108
The Power and Nuances of Recommendation Models 111
Broadening Horizons: Beyond Category Killers 114
Enhancing In- Store Experience with Recommendation Models 116
Leveraging Propensity Models for Targeted Campaigns 118
Personalized Pricing: Influencing Behaviors and Financial Outcomes 121
Behavioral Economics in Personalized Pricing Strategies 124
Forecasting: Understanding the Dynamics of Demand 127
The Power of Forecasting and Optimization 131
Transparent MMMs 132
Ensembling Models for Enhanced Forecasting 133
The Interplay of Demand Forecasting and Inventory Optimization 133
Conclusion 135
Chapter 6 Decoding Complexity: Leveraging Systems Thinking in Modern Organizations 137
Only a Wet Baby Likes Change: Loss Aversion + Status Quo Bias 140
It Gets Better! Commitment Device, Peer Effect, and Sunk Cost Fallacy 145
Conclusion 151
Chapter 7 Unlocking Scale: Overcoming Operational and Organizational Complexity in Scaling AI Projects 155
Enablers of Success 156
Communication and Intellectual Diversity 159
Building Trust, Experimentation, and Adoption 164
Interpretation Layers 166
The Power of Experimentation 169
Measuring ROI Through Experimentation 171
Conclusion 173
Epilogue 175
Notes 179
Additional Reading 189
Bibliography 197
Acknowledgments 205
About the Author 207
Index 209
O autorze
ROGAYEH TABRIZI, PHD, is the founder and CEO of Theory+Practice, a technology company with deep expertise in AI and data and specializes in helping large CPG and Retail enterprises utilize their data to radically enhance revenue optimization decisions. She has a Ph D in Economics and a MSc in Particle Physics and worked on the ATLAS detector at CERN.
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