Abstract
This paper considers the nonlethal targeting assignment problem in the 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 propose two 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 counterinsurgents, and 2) the nonlethal targeting model, a nonlinear programming (NLP) optimization formulation that identifies a strategy for assigning k US 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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Field Manual 3-24, Counterinsurgency. Department of the Army, Washington DC (2006)
Jackson, M.: Social and Economic Networks. Princeton University Press, Princeton (2008)
Abelson, R.: Mathematical Models of the Distribution of Attitudes Under Controversy. In: Frederiksen, N., Gulliksen, H. (eds.) Contributions to Mathematical Psychology, pp. 141–160. Holt, Rinehart (1964)
DeGroot, M.: Reaching a Consensus. J. Amer. Statistical Assoc. 69(345), 118–121 (1974)
Friedkin, N., Johnsen, E.: Social Influence Networks and Opinion Change. Adv. Group Processes 16, 1–29 (1999)
Deffuant, G., Neau, D., Amblard, F.: Mixing Beliefs Among Interacting Agents. Adv. Complex Syst. 3, 87–98 (2000)
Deffuant, G., Amblard, F., Weisbuch, G., Faure, T.: How can extremism prevail? A study on the relative agreement interaction model. J. Art. Soc. Social Sim. 5(4) (2002)
Hegselmann, R., Krause, U.: Opinion Dynamics and Bounded Confidence Models: Analysis and Simulation. J. Art. Soc. Social Sim. 5(3) (2002)
Acemoglu, D., Ozdaglar, A., ParandehGheibi, A.: Spread of (Mis)Information in Social Networks. MIT LIDS report 2812, to appear in Games Econ. Behav. (2010)
Acemoglu, D., Como, G., Fagnani, F., Ozdaglar, A.: Opinion fluctuations and persistant disagreement in social networks. Submitted to Ann. Appl. Probab. (2010)
Borgatti, S.: Identifying sets of key players in a social network. Comp. Math. Org. Theory 12, 21–34 (2006)
Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the Spread of Influence through a Social Network. SIGKDD (2003)
Hung, B.: Optimization-Based Selection of Influential Agents in a Rural Afghan Social Network. Master’s Thesis, MIT, Operations Research Center, Thesis Supervisor, Asuman Ozdaglar (June 2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hung, B.W.K., Kolitz, S.E., Ozdaglar, A. (2011). Optimization-Based Influencing of Village Social Networks in a Counterinsurgency. In: Salerno, J., Yang, S.J., Nau, D., Chai, SK. (eds) Social Computing, Behavioral-Cultural Modeling and Prediction. SBP 2011. Lecture Notes in Computer Science, vol 6589. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19656-0_3
Download citation
DOI: https://doi.org/10.1007/978-3-642-19656-0_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19655-3
Online ISBN: 978-3-642-19656-0
eBook Packages: Computer ScienceComputer Science (R0)