Self-managing terror: Resolving agency problems with diverse teams
Peter Schram
Games and Economic Behavior, 2021, vol. 130, issue C, 240-257
Abstract:
I examine a principal-agents model of subversion with externalities to illustrate a novel mechanism for why diversity can be valuable to organizations: teams of diverse agents can self-manage and discourage their teammates from subversion through compromise. In contrast to standard “ally-principle” type results, I find that integrating more extreme agents can result in better-behaved teams. The model describes, among other cases, radical Islamist terror groups that use foreign fighters. Because foreign and domestic fighters have conflicting preferences over how they want to subvert, integrated teams may self-manage with efficiency gains for the principal. This model explains variation in agency problems and foreign fighter usage in major insurgent groups, including al Qaeda in Iraq, the Haqqani Network, and the Islamic State. Additionally, the theory here can explain management practices in a wide range of alternate settings, for example, where a busy or constrained principal cannot easily implement auditing or incentive contracts.
Keywords: Political economy; Conflict; Organizational economics; Civil wars; Terrorism; Principal-agent models (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0899825621001020
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:gamebe:v:130:y:2021:i:c:p:240-257
DOI: 10.1016/j.geb.2021.07.010
Access Statistics for this article
Games and Economic Behavior is currently edited by E. Kalai
More articles in Games and Economic Behavior from Elsevier
Bibliographic data for series maintained by Catherine Liu ().