CRISP Type 1: Multi-agent Modeling Framework for Mitigating Distributed Disruptions in Critical Supply Chains

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This Critical Resilient Interdependent Infrastructure Systems and Processes (CRISP) Type 1 grant focuses on new mathematical models and analysis methods for improving resiliency in complex human-managed systems. Stakeholders within interdependent critical infrastructure systems traditionally focus on strategies to provide resiliency in the face of single, large disruptive events. This limited focus, however, leaves supply chains vulnerable to large disruptions that arise from cascades of multiple smaller distributed events and which individually are too small to warrant stakeholders attention until it is too late. Exemplifying these features, this award supports the investigation of the drug shortage crisis within the United States for which it is the combination of events, occurring at distinct time periods and distinct levels within the pharmaceutical and health care infrastructures, along with the human responses to these events, that often leads to cascading effects and prolonged drug shortages. Current efforts to mitigate the shortages, have not been successful. The research findings will inform and contribute to the development of new policies to address this contemporary life-critical problem and to understanding in general how to create a resilient system. Understanding resilient systems is key for a 21st century that faces much adversity due to environmental change, terrorism, and globalization. In addition to the research components, educational activities and content will be developed, and publicly distributed for integration in supply chain and resiliency courses in higher education institutions. These courses will demonstrate how complex, contemporary problems can be dealt with using a transdisciplinary and transformative approach.This research seeks to drive a paradigm shift with regard to infrastructure resiliency and supply chain risk management, accounting for the critical inclusion of realistic human decision making components with information sharing and bounded rationality. This is achieved by developing a unique and transformative multi-agent modeling framework that combines mathematical modeling, realistic human behavior modeling, and game design in order to more appropriately consider human behavior and dynamics of the underlying physical, information, and social systems in critical infrastructures. This requires development of new modeling frameworks in partially observable Markov decision process (POMDP) and stochastic games, including the development of a reward function structure that is: (1) computationally tractable despite large action and state space definitions, (2) able to capture fundamental trade-offs in a supply chain with distributed disruptions, and (3) amenable to being fitted with real human decision making data obtained from automatically generated and specifically designed computer games. The outcomes of this research will be to extract and define new resiliency measures and classifications incorporating a robust characterization of stable systems and agent behavior in undisrupted multilayered networked systems with multiple realistic agent-based models of human decision makers and contrast these to system performance and agent behaviors after distributed and single extreme events.