DAEN 427 / ISEN 427
Decision and Risk Analysis

Fall 2025

Description

In this course we’ll learn how to model decisions, risk, and preferences using Bayesian inference. We’ll explore how to make choices under uncertainty and how those choices differ from rational models. Drawing from economics, psychology, and management science, we’ll apply these ideas to engineering systems.

Resources/References

• A Course in Behavioral Economics
• Decision Making Under Uncertainty
• Bayes Rules! An Introduction to Applied Bayesian Modeling
• Causal Inference: The Mixtape
• The Effect: An Introduction to Research Design and Causality
• Bayesian Modeling Using Stan
• Algorithms for Decision Making
• Algorithms for Optimization
• Bayesian Models of Perception and Action: An Introduction
• Bayesian Models of Cognition: Reverse Engineering the Mind
• Bayesian Modeling and Computation in Python
• An Introduction to Bayesian Inference, Methods and Computation
• Coding for Economists

"Even one well-done observation will be enough in many cases, just as one well-made instrument often suffices for the establishment of a law." Émile Durkheim

"It is not the strongest or the most intelligent who will survive but those who can best manage change and uncertainty." adapted from Charles Darwin

"Risk comes from not knowing what you're doing." Warren Buffett

"Project success is not about avoiding risks but about making better decisions when they appear."

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