Optimization-based selection of influential agents in a rural Afghan social network

Abstract

This work considers the nonlethal targeting assignment problem in counterinsurgency in Afghanistan, the problem of deciding on the people whom US forces should engage through outreach, negotiations, meetings, and other interactions in order to ultimately win the support of the population in their area of operations. We developed three models: 1) the Afghan COIN social influence model, to represent how attitudes of local leaders are affected by repeated interactions with other local leaders, insurgents, and counter-insurgents, 2) the network generation model, to arrive at a reasonable representation of a Pashtun district-level, opinion leader social network, and 3) the nonlethal targeting model, a nonlinear programming (NLP) optimization formulation that identifies the k US agent assignment strategy producing the greatest arithmetic mean of the expected long-term attitude of the population. We demonstrate in experiments the merits of the optimization model in nonlethal targeting, which performs significantly better than both doctrine-based and random methods of assignment in a large network.