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In this article, we study the effect of vector-valued interventions in votesunder a binary voter model, where each voter expresses their vote as a $0-1$valued random variable to choose between two candidates. We assume that theoutcome is determined by the majority function, which is true for a democraticsystem. The term intervention includes cases of counting errors, reportingirregularities, electoral malpractice etc. Our focus is to analyze the effectof the intervention on the final outcome. We construct statistical tests todetect significant irregularities in elections under two scenarios, one whereexit poll data is available and more broadly under the assumption of a costfunction associated with causing the interventions. Relevant theoreticalresults on the consistency of the test procedures are also derived. Through adetailed simulation study, we show that the test procedure has good power andis robust across various settings. We also implement our method on threereal-life data sets. The applications provide results consistent with existingknowledge and establish that the method can be adopted for crucial problemsrelated to political elections.

Effect of influence in voter models and its application in detecting significant interference in political elections