Optimization-based influencing of village social networks in a counterinsurgency

This article considers the nonlethal targeting assignment problem in the counterinsurgency in Afghanistan, the problem of deciding on the people whom U.S. 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 propose two models: (1) the Afghan counterinsurgency (COIN) social influence model, to represent how attitudes of local leaders are affected by repeated interactions with other local leaders, insurgents, and counterinsurgents, and (2) the nonlethal targeting model, a NonLinear Programming (NLP) optimization formulation that identifies a strategy for assigning k U.S. agents to produce the greatest arithmetic mean of the expected long-term attitude of the population. We demonstrate in an experiment 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.