Persuasion in Networks: Public Signals and Cores

          @misc{ scivideos_23039,
            doi = {},
            url = {https://old.simons.berkeley.edu/talks/persuasion-networks-public-signals-and-cores},
            author = {},
            keywords = {},
            language = {en},
            title = {Persuasion in Networks: Public Signals and Cores},
            publisher = {The Simons Institute for the Theory of Computing},
            year = {2022},
            month = {nov},
            note = {23039 see, \url{https://scivideos.org/index.php/simons-institute/23039}}
          }
          

Abstract

We consider a setting where agents in a social network take binary actions that exhibit local strategic complementarities. Their payoffs are affine and increasing in an underlying real-valued state of the world. An information designer commits to a signaling mechanism that publicly reveals a signal that is potentially informative about the state. She wants to maximize the expected number of agents who take action 1. We study the structure and design of optimal public signaling mechanisms. The designer’s payoff is an increasing step function of the posterior mean (of the state) induced by the realization of her signal. We provide a convex optimization formulation and an algorithm that obtain an optimal public signaling mechanism whenever the designer’s payoff admits this structure. This structure is prevalent, making our formulation and results useful well beyond persuasion in networks. In our problem, the step function is characterized in terms of the cores of the underlying network. The optimal mechanism is based on a “double-interval partition” of the set of states: it associates up to two subintervals of the set of states with each core, and when the state realization belongs to the interval(s) associated with a core, the mechanism publicly reveals this fact. In turn, this induces the agents in the relevant core to take action 1. We also provide a framework for obtaining asymptotically optimal public signaling mechanisms for a class of random networks. Our approach uses only the limiting degree distribution information, thereby making it useful even when the network structure is not fully known. Finally, we explore which networks are more amenable to persuasion, and show that more assortative connection structures lead to larger payoffs for the designer. On the other hand, the dependence of the designer’s payoff on the agents’ degrees can be quite counterintuitive. In particular, we focus on networks sampled uniformly at random from the set of all networks consistent with a degree sequence, and illustrate that when the degrees of some nodes increase, this can reduce the designer’s expected payoff, despite an increase in the extent of (positive) network externalities.