Explore how information plus decision theory shapes real choices
In this collection of conference papers, leading experts examine how to make better decisions under uncertainty. It covers methods that turn data into actionable insights, with an eye toward practical uses in engineering, management, and policy.
The book presents portraits of ideas that merge mathematics, computing, and practical problem solving. Topics range from dynamic programming and Bayesian decision theory to the role of computers in guiding decisions, with discussions grounded in real-world applications and cautious treatment of uncertainty.
- Foundations of information and decision processes in uncertain environments
- Bayesian approaches to decision making and experiment design
- How computers aid reasoning, planning, and optimization
- Practical examples spanning statistics, economics, and engineering
Ideal for readers of statistical decision theory, operations research, and managers seeking quantitative tools to improve decision making in the face of uncertainty. The edition compiles the latest thinking from a Purdue symposium and related work.