This unit introduces core concepts linking behavioral economics, psychology, and data-driven AI methods for understanding and influencing decision making. Students learn to frame the right questions, simplify complexity, identify patterns with basic models and algorithms, and apply behavioral insights (including nudging) to customer-centric problems while considering ethical implications.
Learning Objectives
- Analyze business and research questions to identify relevant data sources, hypothesis-driven inquiries, and strategic objectives for behavioral AI projects
- Apply methods for simplifying complex systems—including feature selection, abstraction, and model framing—to prepare data and problems for algorithmic analysis
- Demonstrate the ability to connect psychological and economic theories of decision making to observed data patterns by constructing basic models or visualizations that reveal behavioral drivers
- Evaluate common behavioral interventions (e.g., nudges) and early-stage machine learning approaches for effectiveness and ethical considerations in customer-centric applications
Curriculum
- 1 Section
- 14 Lessons
- 30 hours
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- Unit 1 — Foundations of Behavioral AI14
- 1.1Asking the Right Questions: Data, Intuition, and Strategy20 mins
- 1.2Simplifying the Complexity20 mins
- 1.3Connecting the Dots20 mins
- 1.4Uncovering Hidden Patterns: Models and Algorithms in Action20 mins
- 1.5Decoding Consumer Behavior: The Interplay of Psychology and Economics20 mins
- 1.6Empowering Behavioral Economics: The Synergy of Data Analytics, ML, and AI20 mins
- 1.7Crafting a Customer‐Centric Paradigm: The Fusion of Technology and Behavioral Insights20 mins
- 1.8History and Origins of Behavioral Economics20 mins
- 1.9Early Days20 mins
- 1.10Entering Mainstream Economics20 mins
- 1.11Current Research and Practical Applications20 mins
- 1.12Back to the Beginning20 mins
- 1.13Psychology of Decision‐Making20 mins
- 1.14Nudging20 mins
Instructor
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