EAGER: Advancing the Engineering of Complex Systems through Automated Mechanism Design for Complex Network Formation

Purdue University, West Lafayette IN

Investigators

Abstract

Traditional engineering methodologies have proven highly effective for designing complicated systems, such as automobiles and computers, where the sum of the parts equals the whole. However, these methodologies are not very useful for designing complex systems that are greater than the sum of their parts, such as global supply chains, the Internet of Things, social networks and smart power grids. The rapidly increasing and critical role such systems play in U.S. social, industrial and economic infrastructures necessitates new principles for designing complex systems in an inherently efficient, robust and sustainable fashion. This EArly-concept Grant for Exploratory Research (EAGER) award supports fundamental research to provide engineering principles for designing such complex systems. Knowledge from several disciplines including computer science, game theory, optimization and machine learning is integral to this research. Moreover, the emphasis on complex systems will help broaden interest in engineering research and positively impact engineering education. The highly interdependent nature of many physical, virtual and cyber-physical complex systems and our increasing reliance upon them, demand a sound basis upon which to base their design. This research will provide principles for designing complex systems by considering the problem as one of automated mechanism design within the context of strategic Bayesian network formation games to automatically devise incentives such that multiple global design objectives are achieved. The local and stochastic nature inherent to many complex systems motivates a behavior-based perspective on network formation rules, permitting the consideration of more realistic scenarios and may provide deeper insight into network formation itself. The research team will perform simulations, devise models and perform analytical derivations under different system design objectives in order to establish hidden relationships, vary complex system size to better understand adaptation and evolution, consider fixed system interactions to reveal the role of legacy or backbone infrastructure on future systems and mechanisms, and integrate online machine learning as a means for providing feedback thus allowing for adaptive mechanisms.