We study the convergence of Bayesian learning in a tandem social network. Each agent receives a noisy signal about the underlying state of the world, and observes her predecessor's action before choosing her own. We characterize the conditions under which, as the network grows larger, agents'...

Many complex decision making problems like scheduling in manufacturing systems, portfolio management in finance, admission control in communication networks etc., with clear and precise objectives, can be formulated as stochastic dynamic programming problems in which the objective of decision...

This thesis addresses a problem at the nexus of engineering, computer science, and economics: in large scale, decentralized systems, how can we efficiently allocate scarce resources among competing interests? On one hand, constraints are imposed on the system designer by the inherent...