CRISP 2.0 Type 1: Accelerating restoration through information-sharing: Understanding operator behavior for improved management of interdependent infrastructure

Rapid restoration of infrastructure services following a disaster is fundamental for community recovery. Restoration delays may be created when infrastructure sectors are interdependent and when their operators, potentially lacking knowledge of other systems' conditions and restoration plans, mutually wait for others to act. For interdependent infrastructure recovery, the decisions made by the operators has been overlooked in research, yet it is an important factor in quicker recovery times. To improve recovery of interdependent infrastructure systems, there is a fundamental need to broaden the current perspective to include 1) how individual operators make recovery decisions for their infrastructure systems, and 2) how to improve these decisions. This Critical Resilient Interdependent Infrastructure Systems and Processes (CRISP) project compares the effectiveness of a variety of interventions, including information sharing, in reducing restoration times. The project uses interdependent chilled water, information technologies, and electric-power infrastructures at the University of Maryland as an initial case study. To explore the usefulness beyond the case study, a collaboration with a U.S. military installation will evaluate the co-management opportunities, given the models, to improve infrastructure recovery time. Graduate students will also be mentored and trained in these approaches to facilitate a multidisciplinary understanding of linked physical and decision structures to improve infrastructure recovery after a failure.

This project will produce insights into how operators use information to make decisions in the context of interdependent infrastructure. Technically, the research employs novel tools by integrating regulatory analysis, semi-structured interviews, fault tree analysis, agent-based modeling, and serious gaming, to determine how information, and other nudges and regulatory interventions, alter decisions, and ultimately infrastructure recovery processes. The modeling and serious gaming approaches are strongly grounded in an understanding of the physical behavior, observed operator behavior, and governing decision rules and heuristics of operators. Unique to this study is the intersection of interdependent infrastructure modeling and decision science. This interdisciplinary research seeks to develop a new theoretical understanding of interdependent infrastructure informed by operator behavior and to identify practical interventions that improve the co-management of these interdependent systems. Further value is added by comparing computer simulated infrastructure recovery models that produce a 'theoretical' optimum to an operator-revealed 'practical' optimum via serious gaming. This presents a significant advance to the interdependent infrastructure field by evaluating the divide between current academic optimization recovery models and actual operator recovery behavior given inducements for better decision-making.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.