Author: Fernando S. Oliveira
This repository contains draft chapters of the forthcoming book
Risk Analytics: Machine Learning and Optimization for Data-Driven Decision Making.
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Chapter 1 — Risk, Uncertainty, and Decision Making
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Chapter 2 — The Psychology of Risk and Uncertainty
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Risk Analytics: Machine Learning and Optimization for Data-Driven Decision Making
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.
The book develops a structured analytical pipeline:
- Identification of uncertainty and risk sources
- Statistical modeling and probabilistic analysis
- Forecasting and predictive modeling
- Optimization and decision modeling
- Data-driven decision making
The chapters introduce both conceptual foundations and practical computational methods, including:
- 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
Copyright © 2026 Fernando S. Oliveira
Draft chapters are shared for academic and educational purposes.
All rights reserved.