Assessing the effectiveness of economic sanctions

  • Bader SabtanEmail author
  • Marc D. Kilgour
  • Keith W. Hipel
Original Article


The strength of sanctions can significantly impact the outcome of a dispute. The effectiveness of economic sanctions will be explored within the context of the conflict between Organization of Petroleum Exporting Countries (OPEC) and US shale oil producers in 2014. The outcome was not what OPEC anticipated, perhaps because OPEC misperceived the opponent’s preferences. Sensitivity to sanctions is a major component of a decision maker’s preferences when a dispute, or a negotiation, is modeled within the Graph Model for Conflict Resolution (GMCR). This study uses Inverse GMCR to determine what preference rankings would be required for the conflict to end as OPEC wished. The difference between the original preference ranking and the required rankings reflects the miscalculation of the strength of the economic “squeeze” that OPEC imposed when it flooded the market with oil to reduce the price. OPEC expected this sanction to be strong enough to damage, and perhaps destroy, the shale industry, but shale producers were able to withstand it. The graph model analysis suggests why this conflict ended as it did, and provides guidelines for understanding whether sanctions can be effective in forcing a particular outcome on a dispute.


Conflict resolution Graph model Inverse preference problem Economic sanctions Stability analysis 

Mathematics Subject Classification

Primary 91B06 Secondary 05C90 



  1. Behar A, Ritz RA (2016) An analysis of OPEC’s strategic actions, US shale growth and the 2014 oil price crash. International monetary fund. Accessed 10 Jan 2017
  2. Eckbo PL (1976) The future of world oil. Balling Publishing Company, CambridgeGoogle Scholar
  3. Energy Information Administration (EIA) (2016) World liquid fuels production and consumption. Energy information administration. Accessed 15 Jun 2017
  4. Fang L, Hipel KW, Kilgour DM (1993) Interactive decision making: the graph model for conflict resolution. Wiley, New YorkGoogle Scholar
  5. Fraser NM, Hipel KW (1979) Solving complex conflicts. IEEE Trans Syst Man Cybern 9(12):805–816CrossRefGoogle Scholar
  6. Fraser NM, Hipel KW (1984) Conflict analysis: models and resolutions. Elsevier, New YorkGoogle Scholar
  7. Howard N (1971) Paradoxes of rationality: theory of metagames and political behavior. MIT Press, CambridgeGoogle Scholar
  8. Kinsara RA, Kilgour DM, Hipel KW (2015a) Inverse approach to the graph model for conflict resolution. IEEE Trans Syst Man Cybern Syst 45(5):734–742CrossRefGoogle Scholar
  9. Kinsara RA, Petersons O, Hipel KW, Kilgour DM (2015b) Advanced decision support system for the graph model for conflict resolution. J Decis Syst 24:117–145CrossRefGoogle Scholar
  10. Nash JF (1950) Equilibrium points in N-player games. Natl Acad Sci 36(1):48–49CrossRefGoogle Scholar
  11. Nash JF (1951) Non-cooperative games. Ann Math 54(2):286–295CrossRefGoogle Scholar
  12. OPEC (2017) Historical production data. OPEC. Accessed 15 Jun 2017
  13. Xu H, Hipel KW, Kilgour DM, Fang L (2018) Conflict resolution using the graph model: strategic interactions in competition and cooperation. Springer, ChamCrossRefGoogle Scholar

Copyright information

© EURO - The Association of European Operational Research Societies and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Systems Design EngineeringUniversity of WaterlooWaterlooCanada
  2. 2.Department of MathematicsWilfrid Laurier UniversityWaterlooCanada

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