Probabilistic Modeling of Information Dynamics in Networked Cyber–Physical–Social Systems

Abstract

Cyber–physical–social systems (CPSS) are physical devices with highly integrated functions of sensing, computing, communication, and control, and are seamlessly embedded in human society. The levels of intelligence and functions that CPSS can perform rely on their extensive collaboration and information sharing through networks. In this article, information diffusion within CPSS networks is studied. Information dynamic models are proposed to characterize the evolution of information processing and decision-making capabilities of individual CPSS nodes. The data-driven statistical models are based on a mesoscale probabilistic graph model, where the individual nodes’ sensing and computing functions are represented as the probabilities of correct predictions; whereas the communication functions are represented as the probabilities of mutual influences between nodes. A copula dynamics model is proposed to explicitly capture the information dependency among individuals with joint prediction probabilities and estimated from extremal probabilities. A topology-informed vector autoregression model is proposed to represent the mutual influence of prediction capabilities. A spatial–temporal hybrid Gaussian process regression model is also proposed to simultaneously capture correlations between nodes and temporal correlation in the time series.