From Future Learning To Current Action: Long-Term Sequential Infrastructure Planning Under Uncertainty

Carnegie Mellon University, Pittsburgh PA

Investigators

Abstract

Stakeholders and owners of assets and infrastructure systems exposed to extreme events have to take decisions related to risk control and mitigation. Long-term infrastructure planning need not be fixed in the present time, as decisions can be periodically revised depending on available knowledge. This may suggest postponing critical decisions until uncertainty has been sufficiently reduced, though delays may actually increase risks in the shorter term. By investigating the relationship between expected future learning (in terms of policy and technology, for example) and current action in optimal decision making under uncertainty, this project will allow for a) adaptive optimizing of long-term management of infrastructure systems, b) exploration of flexible approaches to asset design, and c) assessment of the value of collecting additional information on attendant risks. The outcome of this project will contribute to society's ability to select appropriate, adaptive risk mitigation actions in optimally engaging limited resources. Accordingly, this project will investigate how decisions on infrastructure planning should depend on the expected future available information on various sources of risk and uncertainty. This will include the development of methods for formulating realistic assumptions about learning rates, and for integrating these assumptions into scalable schemes for system-level sequential decision optimization under uncertainty. The project will develop a framework for integrating future expected learning into decision making optimization via probabilistic modeling of the effects of various exogenous conditions on infrastructure use and planning. Sequential infrastructure management will be framed as a Partially Observable Markov Decision Process (POMDP) to create an efficient computational framework able to identify optimal policies.