Behavioral AI

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.

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