Networks of Biosensors: Decentralized Activation and Social Learning

This paper deals with the dynamics of biosensors and networks of biosensors, where individual biosensors are constructed out of protein molecules. Such biosensors are fully functioning nano-machines. The paper explores methods for decentralized self-activation of networks of biosensors using game-theoretic methods. A global game analysis in terms of a Bayesian game, and a correlated equilibrium analysis is carried out. Also an example of change detection using quickest time detection with social learning is presented. The unifying theme is to understand how local decisions affect global decision making in a multi-agent system.