CIF: Small: Controlled Sensing with Social Learning

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Abstract: This research addresses the growing need for new techniques in controlled sensing and information fusion with social sensors. Social sensors learn from and interact with each other over a social network to estimate an underlying state - the process is called social learning. The aim of this research is to develop mathematical models, algorithms and analysis for controlled sensing and information fusion with social learning. Controlled sensing with social learning is a first step towards constructing generative feedback models and algorithms in the data science of social networks. Similar formulations arise in multi-agent signal processing problems where automated decision makers interact to achieve a sensing goal. The research transcends classical statistical signal processing (which deals with extracting signals from noisy measurements) to address the deeper issue of how multi-agent decision systems and signal processing algorithms interact.The objectives of this research fall under two inter-related themes: Bayesian social learning where quickest change detection and controlled fusion are considered; and interactive sensing in large scale networks where adaptive estimation of degree distribution dynamics, infection dynamics and posterior Cramer Rao bounds are considered. The research involves the interplay of Bayesian social learning, stochastic control and mean field dynamics. The key unifying themes underpinning this research are statistical signal processing and controlled sensing. Social learning involves myopic decision making by individual agents; while controlled sensing involves stochastic control over a time horizon. This interaction between myopic social sensors (local decision makers) and a non-myopic controller (global decision maker) results in unusual behavior. The scientific innovation of this research stems from advancing deep results in Bayesian estimation, stochastic optimization, weak convergence analysis, and lattice programming.