End-User Behavior and Prospect Pricing in Wireless Data Networks

Prof. Narayan Mandayam has received an NSF grant for his project : "End-User Behavior and Prospect Pricing in Wireless Data Networks”.   This is $500K 3-year grant and Arnold Glass of Psychology is a co-PI. The abstract of the grant is shown below.

Abstract: There is a recognition and push in both industry and academia towards the goal of achieving "1000x" capacity for wireless. The solution approaches range from spectrally agile cognitive radios with novel spectrum sharing, to use of higher frequency spectrum as well as smaller and denser cell deployments referred to as heterogeneous networks (HetNets). While this is a much needed activity with many challenges to overcome, providing a spatially high density of wireless/wired backhaul as required for HetNets is expensive and the overwhelming demands on wireless capacity fundamentally remain, in that state-of-the-art systems are nowhere near the 1000x capacity target goals and perhaps even an order of magnitude or two away. As a result, wireless service providers (SPs) in recent times have resorted to control access and services being provided to end-users via differentiated and hierarchical monetary pricing. A complementary approach termed “prospect pricing" is proposed as a way to support data demand and relies on influencing end-user (human) behavior using dynamic pricing algorithms when technological solutions by themselves cannot satisfy the demands of wireless data. When a SP controls access to end-users via differentiated and hierarchical monetary pricing, then the performance of the network is directly subject to end-user decision-making that has shown to deviate from expected utility theory (EUT). Prospect Theory, a Nobel prize winning theory that explains real-life decision-making and its deviations from EUT behavior is used to design “prospect pricing" for wireless networks. Specifically, dynamic pricing algorithms for wireless data are designed to enable HetNets to manage the ever increasing demand for data, especially when both spectrum and infrastructure resources are constrained. Using a mix of theory, algorithm development and experimentation with human subjects, the research agenda is carried out by a team comprised of a wireless networking/systems engineer and a cognitive psychologist.