Scientific foundations of resilient socio-technical systems
This thrust seeks to establish the foundations for the realization of resilient, complex systems through collective innovation, which is characterized by the self-organization of individuals into decentralized, non-hierarchical communities. The research objective will be achieved by using principles from biological evolution and network dynamics to understand the bottom-up evolution of systems and the self-organization of communities. The research approach combines mathematical theories and computational approaches, including the theory of network evolution, social network analysis, and agent-based modeling. Knowledge pertaining to the evolutionary dynamics of systems and communities gained from this research, is being used to develop cyberinfrastructure for collective innovation.
If successful, the results of this research will enable engineering enterprises to use a significant but underutilized mode of innovation by communities of employees within organizations, and of enthusiasts outside the organizations. The cyberinfrastructure (based on a bottom-up combination of systems and communities) will complement the current product lifecycle management platforms by fostering community-centered innovation strategies. Through this effort, we are establishing foundational techniques for modeling and analyzing the evolutionary dynamics of complex networked systems in terms of node-level agents' decisions. The research objective is being achieved through an integration of discrete-choice random-utility theory and network science-based approaches to model the evolutionary characteristics of complex networked systems. Specific examples of complex networked systems, including air-transportation networks and autonomous system level Internet, are being used for validation. The research will result in novel approaches to model network evolution resulting from decisions made by independent or competing entities, and to evaluate mechanisms for steering the evolution towards higher performance and resilience, such as robustness to node failure and targeted attacks.
The results of this research hold promise for accurate system performance prediction, and for forecasting how the complex networked systems would evolve in the future. The resulting approaches would enable the development of better surrogate models of networks for efficient design of processes and protocols on networked systems. The approaches will also enable policy and incentive design to guide the restructuring of existing networks for improved performance.