Behavioral Modeling, Forecasting, and Adoption

This unit integrates predictive modeling (recommendation, propensity, and personalized pricing) with forecasting, behavioral-economics-informed interventions, and the practical enablers required for real-world adoption. Students will learn to design, evaluate, and communicate models and experiments, while applying interpretation and trust-building practices that prepare them for operational deployment in the next unit.

Learning Objectives

• Analyze and compare recommendation and propensity models by computing and interpreting at least three evaluation metrics (e.g., precision@k, AUC, lift) on provided datasets and justify model choice for a specified targeted campaign.
• Apply at least two time-series forecasting methods to produce short-term demand forecasts, quantify forecast accuracy (e.g., RMSE, MAPE), and use optimization to recommend pricing or allocation scenarios under alternative demand assumptions.
• Evaluate behavioral biases (loss aversion, status quo bias, sunk cost, peer effects, commitment devices) and design a measurable intervention including a randomized experiment or A/B test plan with hypothesis, metrics, control/variant, and sample-size estimation to assess behavioral impact.
• Demonstrate effective communication and trust-building practices by producing an executive summary and an interpretation artifact (e.g., feature importance, counterfactual explanations), and proposing governance, monitoring, and adoption steps to operationalize model-led recommendations.