Bayesian Inferences for Counterterrorism Policy: A Retrospective Case Study of the U.S. War in Afghanistan

ABSTRACT

This study employs hierarchical Bayesian analysis of terrorist attacks to provide a retrospective analysis of the war in Afghanistan between 2002 and 2018. We examine the relationship between U.S. troop levels, target type, and the severity of attacks in terms of the number killed or wounded. We find that although some targets might have become better fortified after enduring attacks (such as police departments), terrorists would subsequently succeed in either attacking other targets (such as educational institutions) or even those same targets in subsequent years. Our analysis also finds that increases in U.S. troop levels throughout much of the conflict did not seem to quell violence, although explanations of this phenomenon are considerably more nuanced after taking expert opinion into account. We hope our analysis provides a useful retrospective analysis of the U.S. War in Afghanistan for policymakers to assess how or if security measures in the country could have been improved over the course of the conflict. Our modeling framework, however, is easily generalizable to other conflicts worldwide and thus provides a useful statistical tool for analyzing terrorism in many other settings as well.

KEYWORDS:

• Terrorism
• war in Afghanistan
• Bayesian modeling
• security
• historical analysis

Acknowledgments

The authors would like to thank Parker Sheppard, Norbert Michel, Steven J. Miller, Steven Bucci, Carl Densing, and Max Primorac for insightful comments on earlier drafts of this work and Richard Shin and Ryan Sotnick for research assistance.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. Kevin Dayaratna, “Contributions to Bayesian Statistical Modeling in Public Policy Research” (PhD thesis, University of Maryland, 2014).

2. History, “September 11 Attacks,” History, 2010.

3. George Tenet and Bill Harlow, At the Center of the Storm: My Years at the CIA (New York, NY: HarperPress, 2007).

4. The Washington Post, “Text: Bush Announces Strikes Against Taliban,” The Washington Post, 2001.

5. Thomas Spoeher, “America Deserves Answers on Botched Afghanistan Withdrawal,” The Daily Signal, 2021.

6. Carolina Tranchita, N. Hadjsaid, and A. Torres, “Ranking Contingency Resulting from Terrorism by Utilization of Bayesian Networks” (Electrotechnical Conference, 2006. MELECON 2006. IEEE Mediterranean, IEEE, 2006), 964–67; Elisabeth Paté-Cornell, “Fusion of Intelligence Information: A Bayesian Approach,” Risk Analysis 22, no. 3 (2002): 445–54; Stan Kaplan, “Applying the General Theory of Quantitative Risk Assessment (QRA) to Terrorism Risk,” Risk-Based Decision Making (2002): 77–81; Robert L. Popp and John Yen, Emergent Information Technologies and Enabling Policies for Counter-Terrorism, vol. 6. (Hoboken, NJ: John Wiley & Sons, 2006); Barry Charles Ezell, Steven P. Bennett, Detlof Von Winterfeldt, John Sokolowski, and Andrew J. Collins, “Probabilistic Risk Analysis and Terrorism Risk,” Risk Analysis 30, no. 4 (2010): 575–89; Manoj K. Jha, “Dynamic Bayesian Network for Predicting the Likelihood of a Terrorist Attack at Critical Transportation Infrastructure Facilities,” Journal of Infrastructure Systems 15, no. 1 (2009): 31–39.

7. Erik Lewis, George Mohler, P. Jeffrey Brantingham, and Andrea L. Bertozzi, “Self-Exciting Point Process Models of Civilian Deaths in Iraq,” Security Journal 25, no. 3 (2012): 244–64.

8. Ibid.

9. Ibid.

10. See note 1 above.

11. Minjung Kyung, Jeff Gill, and George Casella, “New Findings From Terrorism Data: Dirichlet Process Random-Effects Models for Latent Groups,” Journal of the Royal Statistical Society: Series C (Applied Statistics) 60, no. 5 (2011): 701–21; André Python, Janine B. Illian, Charlotte M. Jones-Todd, and Marta Blangiardo, “A Bayesian Approach to Modelling Subnational Spatial Dynamics of Worldwide Non-State Terrorism, 2010–2016,” Journal of the Royal Statistical Society: Series A (Statistics in Society) 182, no. 1 (2019): 323–44.

12. Global Terrorism Database [Data file]. National Consortium for the Study of Terrorism and Responses to Terrorism (START). 2020; Data from 2018 was the most recently data available at the time of this analysis.

13. Ibid.

14. Amy Belasco, “Troop Levels in the Afghan and Iraq Wars, FY2001-FY2012: Cost and Other Potential Issues” (2009), 67–69; Heidi M. Peters and Sofia Plagakis, Department of Defense Contractor and Troop Levels in Afghanistan and Iraq: 2007–2018 (Washington, DC: Congressional Research Service, 2019), 10–11.

15. Ibid.

16. John F. Geweke, “Evaluating the Accuracy of Sampling-Based Approaches to the Calculation of Posterior Moments,” in In Bayesian Statistics, ed. J. M. Bernardo, J. O. Berger, A. P. Dawid, and A. F. M. Smith, Vol. 4 (Oxford, UK: Oxford University Press, 1992), 169–93.

17. Geometric means are preferred to arithmetic means as Poisson regression coefficients represent growth rates, and geometric means are considered mathematically the correct way to average a set of growth rates together.

18. Gary Kleck and James C Barnes, “Do More Police Lead to More Crime Deterrence?” Crime & Delinquency 60, no. 5 (2014): 716–38; Herbert Jacob and Michael J. Rich, “The Effects of the Police on Crime: A Second Look,” Law and Society Review 15, no. 1 (1980): 109–22, https://doi.org/10.2307/3053224; James P. Levine, “The Ineffectiveness of Adding Police to Prevent Crime,” Public Policy 23, no. 4 (1975): 523–45; Ming-Jen Lin, “More Police, Less Crime: Evidence from US State Data,” International Review of Law and Economics 29, no. 2 (2009): 73–80; John M. MacDonald, Jonathan Klick, and Ben Grunwald, “The Effect of Private Police on Crime: Evidence from a Geographic Regression Discontinuity Design,” Journal of the Royal Statistical Society: Series A (Statistics in Society) 179, no. 3 (2016): 831–46.

19. Faiz Ur Rehman, “Does Military Intervention Reduce Violence? Evidence from Federally Administered Tribal Area of Pakistan (2001–2011),” The Journal of Development Studies 54, no. 9 (2018): 1572–92.

20. Ibid.

21. Isaac Chotiner, “David Petraeus on American Mistakes in Afghanistan,” The New Yorker, August, 2011.

22. Ibid.; Tommy R. Franks, American Soldier (New York, NY: HarperCollins, 2004).

23. Luke Coffey, “U.S., Taliban Sign Peace Deal for Afghanistan,” The Daily Signal, 2020.

24. Robert Pape, Kevin Ruby, Vincent Bauer, and Gentry Jenkins, “How to Fix the Flaws in the Global Terrorism Database and Why it Matters,” The Washington Post, August, 2014.

25. Ibid.; Gary LaFree and Laura Dugan, “Tracking Global Terrorism Trends, 1970–2004,” in To Protect and to Serve (New York, NY: Springer New York, 2009), 43–80; Gary LaFree and Laura Dugan, “Introducing the Global Terrorism Database,” Terrorism and Political Violence 19, no. 2 (2007): 181–204; Laura Dugan, “The Making of the Global Terrorism Database and its Applicability to Studying the Life Cycles of Terrorist,” in The SAGE Handbook of Criminological Research Methods (London: SAGE Publications Ltd, 2012), 175–98; José V Pagán, “Improving the Classification of Terrorist Attacks a Study on Data Pre-Processing for Mining the Global Terrorism Database” (2010 2nd International Conference on Software Technology and Engineering, vol. 1, 2010), V1–104–V1–110; Ivan Sascha Sheehan, “Assessing and Comparing Data Sources for Terrorism Research,” in Evidence-Based Counterterrorism Policy (New York, NY: Springer New York, 2011), 13–40.

26. George W. Bush, “President’s Address to the Nation,” January, 2007, http://georgewbush-whitehouse.archives.gov/news/releases/2007/01/20070110-7.html (accessed April 17, 2014).

27. See note 1 above; Kyung et al., “New Findings From Terrorism Data.”

28. David M. Blei and Peter I. Frazier, “Distance Dependent Chinese Restaurant Processes,” The Journal of Machine Learning Research 12 (2011): 2461–88.

29. See note 14 above; Global Terrorism Database [Data file], “START GTD Database Codebook: Methodology, Inclusion Criteria, and Variables,” (August, 2021).

30. See note 14 above.

Additional information

Notes on contributors

Kevin D. Dayaratna

Kevin D. Dayaratna is Chief Statistician, Data Scientist, and Senior Research Fellow at The Heritage Foundation's Center for Data Analysis.

Chandler Hubbard

Chandler Hubbard is a doctoral student in Economics at the University of Wyoming.

Mary Catherine Legreid

Mary Catherine Legreid is bachelor’s degree student in Management and Information Systems at Texas A&M University.