Latent Agents in Networks: Estimation and Targeting

TL;DR

This paper develops a method to estimate how observable agents' outcomes depend on their covariates in a network with latent agents, enabling optimal decision-making in advertising and pricing despite incomplete network information.

Contribution

It introduces an estimator for the influence matrix in networks with latent agents, with proven convergence rates under approximate sparsity conditions.

Findings

Estimator performs well with more agents than observations.

Approximate sparsity condition holds under standard network assumptions.

Enables asymptotically optimal advertising and pricing decisions.

Abstract

We consider a network of agents. Associated with each agent are her covariate and outcome. Agents influence each other's outcomes according to a certain connection/influence structure. A subset of the agents participate on a platform, and hence, are observable to it. The rest are not observable to the platform and are called the latent agents. The platform does not know the influence structure of the observable or the latent parts of the network. It only observes the data on past covariates and decisions of the observable agents. Observable agents influence each other both directly and indirectly through the influence they exert on the latent agents. We investigate how the platform can estimate the dependence of the observable agents' outcomes on their covariates, taking the latent agents into account. First, we show that this relationship can be succinctly captured by a matrix and provide an algorithm for estimating it under a suitable approximate sparsity condition using historical data of covariates and outcomes for the observable agents. We also obtain convergence rates for the proposed estimator despite the high dimensionality that allows more agents than observations. Second, we show that the approximate sparsity condition holds under the standard conditions used in the literature. Hence, our results apply to a large class of networks. Finally, we apply our results to two practical settings: targeted advertising and promotional pricing. We show that by using the available historical data with our estimator, it is possible to obtain asymptotically optimal advertising/pricing decisions, despite the presence of latent agents.