Accelerating opponent strategy inference for voting dynamics on complex networks

Abstract

In this paper, we study the problem of opponent strategy inference from observations of information diffusion in voting dynamics on complex networks. We demonstrate that, by deploying resources of an active controller, it is possible to influence the information dynamics in such a way that opponent strategies can be more easily uncovered. To this end, we use the framework of maximum likelihood estimation and the Fisher information to construct confidence intervals for opponent strategy estimates. We then design heuristics for optimally deploying resources with the aim of minimizing the variance of estimates. In the first part of the paper, we focus on inferring an opponent strategy at a single node. Here, we derive optimal resource allocations, finding that, for low controller budget, resources should be focused on the inferred node and, for large budget, on the inferred nodes' neighbours.
In the second part, we extend the setting to inferring opponent strategies over the entire network. We find that opponents are the harder to detect the more heterogeneous networks are, even with optimal targeting.

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Identifiers

ORCID for Enrico Gerding: orcid.org/0000-0001-7200-552X

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