Targeting in adaptive networks

This paper studies optimal targeting policies, consisting of eliminating (preserving) a set of agents in a network and aimed at minimizing (maximizing) aggregate effort levels. Different from the existing literature, we allow the equilibrium network to adapt after a network intervention and consider targeting of multiple agents. A simple and tractable adjustment process is introduced. We find that allowing the network to adapt may overturn optimal targeting results for a fixed network and that congestion/competition effects are crucial to understanding differences between the two settings.