Cognitive Networks

NSF CIF: Large: Collaborative Research: Cooperation and Learning over Cognitive Networks (project start date – September 1st, 2010)

Abstract. Studies on herding in economics and the social and biological sciences have observed that coordination among multiple agents leads to regular patterns of behavior and swarm intelligence, even when each group member shows very limited behavioral complexity. In ant colonies, for example, individual ants cannot capture rich spatial information from their environment because of their limited, localized sensing ability. Nevertheless, when the ants coordinate their activities together within a colony, the group ends up exhibiting better sensing abilities. Can one explain how and why such manifestations of rational behavior arise at the group level from the interactions of agents with limited individual abilities at the local level? What communication topologies enable such behavior and how much information quantization is performed at the local level? Likewise, self-organization is a remarkable property of nature and it has been observed in several physical and biological systems. Examples include fish joining together in schools, chemicals forming spirals, and sand grains assembling into rippling dunes. In self-organizing systems, a global pattern emerges from the interaction of the individual components of the system. For example, flocks of birds self-organize into V-formations when they need to travel long distances. What type of coordination is employed by the birds to get into this formation? How can other formation topologies be justified? What type of communication patterns enables such formations? Interestingly, a close synergy is evolving between studies on herding and flocking in the social and biological sciences and recent developments in the signal processing and communications communities on cognitive networks. These networks avoid centralized information processing and perform in-network inference and control decisions without relying on fusion centers. This is because solutions that rely on information fusion are not scalable, are hard to adapt to changing network conditions, and create single points of vulnerability and information bottlenecks.

Objectives. The research proposes to exploit the connection between socio-economic-biological networks and cognitive networks in an effort to understand and reverse-engineer the decentralized intelligence encountered in the biological and socio-economic domains. What distinguishes this work from prior efforts is its focus on understanding how learning and rationality evolve from low-level interactions on one hand, and how network dynamics and mobility relate to optimality on the other hand.

Lab Members

  1. June Zhang