Dynamic Decision Making under Uncertainty and Partial Information
Abstract
This project is concerned with the study of basic questions aimed at meeting challenges in information superiority, logistics, and planning for the Air Force of the future. For successful military operations, the future requirements of the Air Force will include information fusion at a much larger scale and much more agile, responsive, and integrated systems. Such problems and systems are exceedingly complex; however, a central part of them is decision making, which often takes place sequentially in time, subject to uncertainty in the future and limited partial information at hand. In order to address these problems, we investigated efficient computational methodologies for dynamic decision making under uncertainty and partial information. In the course of this research, we developed and studied efficient simulation-based methodologies for dynamic decision making under uncertainty and partial information; (ii) studied the application of these decision making models and methodologies to practical problems, such as those arising in planning, logistics, and risk management. The proposed research resulted in (i) new mathematical tools and theories for dynamic decision making and optimal stopping; (ii) useful application of these models and new methodologies in a wide range of problems.
Document Details
- Document Type
- Technical Report
- Publication Date
- Nov 14, 2013
- Accession Number
- ADA591355
Entities
People
- Enlu Zhou
Organizations
- Georgia Tech