We study whether individuals can learn the informativeness of their information technology through social learning. As in the classic sequential social learning model, rational agents arrive in order and make decisions based on the past actions of others and their private signals. There is uncertainty regarding the informativeness of the common signal-generating process. We show that in this setting asymptotic learning about informativeness is not guaranteed and depends crucially on the relative tail distributions of private beliefs induced by uninformative and informative signals. We identify the phenomenon of perpetual disagreement as the cause of learning and characterize learning in the canonical Gaussian environment.

We study how long-lived, rational, exponentially discounting agents learn in a social network. In every period, each agent observes the past actions of his neighbors, receives a private signal, and chooses an action with the objective of matching the state. Since agents behave strategically, and since their actions depend on higher order beliefs, it is difficult to characterize equilibrium behavior. Nevertheless, we show that regardless of the size and shape of the network, and the patience of the agents, the equilibrium speed of learning is bounded from above by a constant that only depends on the private signal distribution.

Space–air–ground Internet of Vehicle networks is a promising network paradigm to support diverse vehicular services. Exploiting the unique advantages of spatial, aerial, and terrestrial network segments, the Quality of Service (QoS) of different services can be met by smart access network selection. However, the integrated network inevitably has to face many challenges, such as dynamic network topology, heterogeneous resources, and long propagation latency, which can seriously degrade the QoS performance. In order to provide QoS guarantee to vehicles, we propose a novel architecture, called QoS-guaranteed access assistance (QA2) serve vehicles with access assistance. To be specific, a virtualized layer is established in the network, which contains the logical resources of ground network infrastructures. Using that layer, Access Assistants are flexibly deployed at a dynamic set of ground infrastructures, and then help vehicles to acquire network services with satisfied QoS requirements. Furthermore, to accommodate temporal–spatial-varying network demands of vehicles, QA2 adopts a cost-effective deployment scheme, which updates the locations of Assistants based on the real-time traffic flow with the minimum operation cost. The evaluation results demonstrate the effectiveness of QA2, which increases request success rate by more than 50%. The results also prove that the cost-effective deployment scheme can efficiently adapt to the change of traffic flow and achieve a good balance between QoS guarantee and cost saving. The operation cost is saved by more than 25% and the QoS performance is improved by more than 22%.