The risks and rewards of conditioning noncooperative designs to additional information
Dario Paccagnan, Jason R. Marden · 2017
A fundamental challenge in multiagent systems is to design local control algorithms to ensure a desirable collective behaviour. The information available to the agents, gathered either through communication or sensing, defines the structure of the admissible control laws and naturally restricts the achievable performance. Hence, it is fundamental to identify what piece of information can be used to produce a significant performance enhancement. This paper studies, within a class of resource allocation problems, the case when such information is uncertain or inaccessible and pinpoints a fundamental risk-reward tradeoff faced by the system designer.