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Pre-built decision models serving as the base for Marketplace subscription apps.

Python SDKs for Nextmv

This article reframes pricing as a negotiation rather than a prediction, showing how price emerges from tensions between product reality, market dynamics, and buyer behavior. It introduces negotiation-aware ML, value decomposition, and equilibrium modeling to build transparent, human-aligned pricing systems.

Demos and utilities for control ROI, VoI, and risk reduction in modern security programs.

End-to-end café inventory project: clean transaction data, build daily item-level demand series, backtest strong baseline forecasters, generate next-30-day demand forecasts, convert forecasts into safety stock + reorder points, and validate policies with Monte Carlo stockout-risk simulations, wrapped in a Streamlit dashboard.

A comprehensive ride-pooling simulation engine featuring VRP optimization (OR-Tools), dynamic pricing algorithms and ML-based demand prediction.

Reusable decision-science utilities for security: Monte Carlo, Bayes, Survival, VoI, light causal helpers.

Reproduction code for "Taking Advice from ChatGPT"

Decision Science🔬/决策科学

Holds data documentation

A simple command line application that continuously exports data from Zendesk

Data Science Case Study: To model the price of cars with the available features to understand the pricing dynamics of the car market and help management in making informed decisions regarding optimal business strategies and car designs.

Elite decision intelligence platform — make better decisions with science, not gut feelings

Professional reference textbook on applied econometrics, causal inference, and decision science for real-world economic decision-making. Authored by an applied economist; companion repository to the full textbook PDF.

Decision-support analysis showing why public signals can mislead product teams when used without internal context.

Data Science Case Study: To help X Education select the most promising leads (Hot Leads), i.e. the leads that are most likely to convert into paying customers.

Repo for Northwestern Data Science program projects

Machine learning decision system for oil well location optimization using regression, bootstrapping, and risk-constrained profit simulation.

A local decision simulator that uses Monte Carlo methods to explore possible outcomes of complex decisions under uncertainty.