This repository contains draft chapters of the forthcoming book Risk Analytics: Machine Learning and Optimization for Data-Driven Decision Making.

• Fundations of Risk
  
• Chapter 1 — Risk, Uncertainty, and Decision Making
  PDF
• Chapter 2 — The Psychology of Risk and Uncertainty
  PDF
• Chapter 3 — The Architecture of Risk Preferences
  PDF | Companion code
• Probabilistic Tools for Risk Modeling
  
• Chapter 4 — VaR, CVaR and Downside Risk Assessment
  PDF | Companion code
• Chapter 5 — Tail Risk Optimization with Discrete Distributions
  PDF | Companion code
• Chapter 6 — Tail Risk under Unknown Loss Distributions
  PDF | Companion code
• Risk Segmentation, Prediction, and Scoring
  
• Chapter 7 — Interpretable Risk Segmentation with Classification Trees
  PDF | Companion code
• Chapter 8 — Supervised Learning for Risk Prediction
  PDF | Companion code
• Chapter 9 — Risk Scoring, Calibration, and Risk Decisions
  PDF | Companion code

About the Book

The book develops a unified analytical framework for decision making under uncertainty, integrating statistical modeling, forecasting, optimization, and machine learning techniques. It is intended for graduate students, researchers, and practitioners in operations management, finance, economics, and data science.

Modern organizations operate in environments characterized by uncertainty and complex interdependencies. Decisions regarding investment, supply chains, financial portfolios, infrastructure systems, and technological innovation must be made despite incomplete information about future states of the world.

Risk analytics provides a set of quantitative tools designed to support such decisions. By combining probability theory, stochastic modeling, simulation, optimization methods, and machine learning algorithms, risk analytics allows decision makers to model uncertainty, forecast future outcomes, and design strategies that are robust to unpredictable events.

Analytical Pipeline

1. Identification of uncertainty and risk sources
2. Statistical modeling and probabilistic analysis
3. Forecasting and predictive modeling
4. Optimization and decision modeling
5. Data-driven decision making

Topics Covered

• probability distributions and stochastic modeling
• time-series forecasting
• Monte Carlo simulation
• decision trees and scenario analysis
• dynamic programming and reinforcement learning
• financial risk modeling and option pricing
• project and operational risk management