IDEAS home Printed from https://ideas.repec.org/a/sae/compsc/v29y2012i3p279-313.html
   My bibliography  Save this article

Complex Dependencies in the Alliance Network

Author

Listed:
  • Skyler J. Cranmer

    (University of North Carolina at Chapel Hill)

  • Bruce A. Desmarais

    (University of Massachusetts Amherst)

  • Elizabeth J. Menninga

    (University of North Carolina at Chapel Hill)

Abstract

The multifaceted and strategic interactions inherent in the formation of international military pacts render the alliance decisions of states highly interdependent. Our aim here is to model the network of alliances in such a way as to capture the effects of covariates and account for the complex dependencies inherent in the network. Regression analysis, due to its foundational assumption of conditional independence, cannot be used to analyze alliance decisions specifically and interdependent decisions generally. We demonstrate how alliance decisions are interdependent and define the problems associated with the regression analysis of nonindependent dyads. We then show that alliances can naturally be conceived of as constituting a network, where alliance formation is an inherently interdependent process. We proceed by introducing the exponential random graph model for analyzing interdependence in the alliance network and estimating the effect of covariates on alliances.

Suggested Citation

  • Skyler J. Cranmer & Bruce A. Desmarais & Elizabeth J. Menninga, 2012. "Complex Dependencies in the Alliance Network," Conflict Management and Peace Science, Peace Science Society (International), vol. 29(3), pages 279-313, July.
    • Handle: RePEc:sae:compsc:v:29:y:2012:i:3:p:279-313
    DOI: 10.1177/0738894212443446
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0738894212443446
    Download Restriction: no
    File URL: https://libkey.io/10.1177/0738894212443446?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Margaret Sullivan Pepe, 2000. "An Interpretation for the ROC Curve and Inference Using GLM Procedures," Biometrics, The International Biometric Society, vol. 56(2), pages 352-359, June.
    2. Zeev Maoz, 2009. "The Effects of Strategic and Economic Interdependence on International Conflict Across Levels of Analysis," American Journal of Political Science, John Wiley & Sons, vol. 53(1), pages 223-240, January.
    3. Cranmer, Skyler J. & Desmarais, Bruce A., 2011. "Inferential Network Analysis with Exponential Random Graph Models," Political Analysis, Cambridge University Press, vol. 19(1), pages 66-86, January.
    4. Desmarais, B.A. & Cranmer, S.J., 2012. "Statistical mechanics of networks: Estimation and uncertainty," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1865-1876.
    5. King, Gary & Zeng, Langche, 2001. "Explaining Rare Events in International Relations," International Organization, Cambridge University Press, vol. 55(3), pages 693-715, July.
    6. Brett Leeds & Jeffrey Ritter & Sara Mitchell & Andrew Long, 2002. "Alliance Treaty Obligations and Provisions, 1815-1944," International Interactions, Taylor & Francis Journals, vol. 28(3), pages 237-260, July.
    7. Hoff P.D. & Raftery A.E. & Handcock M.S., 2002. "Latent Space Approaches to Social Network Analysis," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1090-1098, December.
    8. Michael D. Ward & Randolph M. Siverson & Xun Cao, 2007. "Disputes, Democracies, and Dependencies: A Reexamination of the Kantian Peace," American Journal of Political Science, John Wiley & Sons, vol. 51(3), pages 583-601, July.
    9. Pete, Andras & Pattipati, Krishna R & Kleinman, David L, 1993. "Optimal Team and Individual Decision Rules in Uncertain Dichotomous Situations," Public Choice, Springer, vol. 75(3), pages 205-230, March.
    10. Stanley Wasserman & Philippa Pattison, 1996. "Logit models and logistic regressions for social networks: I. An introduction to Markov graphs andp," Psychometrika, Springer;The Psychometric Society, vol. 61(3), pages 401-425, September.
    11. Li, Quan & Reuveny, Rafael, 2003. "Economic Globalization and Democracy: An Empirical Analysis," British Journal of Political Science, Cambridge University Press, vol. 33(1), pages 29-54, January.
    12. Hoff, Peter D. & Ward, Michael D., 2004. "Modeling Dependencies in International Relations Networks," Political Analysis, Cambridge University Press, vol. 12(2), pages 160-175, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Pauls, Scott D. & Cranmer, Skyler J., 2017. "Affinity communities in United Nations voting: Implications for democracy, cooperation, and conflict," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 484(C), pages 428-439.
    2. Johannes Pol, 2019. "Introduction to Network Modeling Using Exponential Random Graph Models (ERGM): Theory and an Application Using R-Project," Computational Economics, Springer;Society for Computational Economics, vol. 54(3), pages 845-875, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Cornelius Fritz & Michael Lebacher & Göran Kauermann, 2020. "Tempus volat, hora fugit: A survey of tie‐oriented dynamic network models in discrete and continuous time," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 74(3), pages 275-299, August.
    2. Lee, Jihui & Li, Gen & Wilson, James D., 2020. "Varying-coefficient models for dynamic networks," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).
    3. Mark S. Handcock & Adrian E. Raftery & Jeremy M. Tantrum, 2007. "Model‐based clustering for social networks," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(2), pages 301-354, March.
    4. Samrachana Adhikari & Beau Dabbs, 2018. "Social Network Analysis in R: A Software Review," Journal of Educational and Behavioral Statistics, , vol. 43(2), pages 225-253, April.
    5. Samrachana Adhikari & Tracy Sweet & Brian Junker, 2021. "Analysis of longitudinal advice‐seeking networks following implementation of high stakes testing," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(4), pages 1475-1500, October.
    6. Chih‐Sheng Hsieh & Lung‐Fei Lee & Vincent Boucher, 2020. "Specification and estimation of network formation and network interaction models with the exponential probability distribution," Quantitative Economics, Econometric Society, vol. 11(4), pages 1349-1390, November.
    7. Emilie M. Hafner-Burton & Alexander H. Montgomery, 2012. "War, Trade, and Distrust: Why Trade Agreements Don’t Always Keep the Peace," Conflict Management and Peace Science, Peace Science Society (International), vol. 29(3), pages 257-278, July.
    8. Alexandra Goritz & Nina Kolleck & Helge Jörgens, 2019. "Education for Sustainable Development and Climate Change Education: The Potential of Social Network Analysis Based on Twitter Data," Sustainability, MDPI, vol. 11(19), pages 1-15, October.
    9. Wong, Ling Heng & Pattison, Philippa & Robins, Garry, 2006. "A spatial model for social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 360(1), pages 99-120.
    10. Brandon J Kinne, 2014. "Does third-party trade reduce conflict? Credible signaling versus opportunity costs," Conflict Management and Peace Science, Peace Science Society (International), vol. 31(1), pages 28-48, February.
    11. Sudeshna Paul & A. James O'Malley, 2013. "Hierarchical longitudinal models of relationships in social networks," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 62(5), pages 705-722, November.
    12. Aysegul Aydin, 2010. "The deterrent effects of economic integration," Journal of Peace Research, Peace Research Institute Oslo, vol. 47(5), pages 523-533, September.
    13. Cai, Wei & Guan, Guoyu & Pan, Rui & Zhu, Xuening & Wang, Hansheng, 2018. "Network linear discriminant analysis," Computational Statistics & Data Analysis, Elsevier, vol. 117(C), pages 32-44.
    14. Sayan Chakraborty & Arnab Bhattacharjee & Taps Maiti, 2021. "Structural Factorization of Latent Adjacency Matrix, with an application to Auto Industry Networks," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(2), pages 185-206, November.
    15. Tracy M. Sweet, 2019. "Modeling Social Networks as Mediators: A Mixed Membership Stochastic Blockmodel for Mediation," Journal of Educational and Behavioral Statistics, , vol. 44(2), pages 210-240, April.
    16. Jing Wang & Anocha Aribarg & Yves F. Atchadé, 2013. "Modeling Choice Interdependence in a Social Network," Marketing Science, INFORMS, vol. 32(6), pages 977-997, November.
    17. Dzemski, Andreas, 2017. "An empirical model of dyadic link formation in a network with unobserved heterogeneity," Working Papers in Economics 698, University of Gothenburg, Department of Economics, revised Apr 2018.
    18. Teague R. Henry & Kathleen M. Gates & Mitchell J. Prinstein & Douglas Steinley, 2020. "Modeling Heterogeneous Peer Assortment Effects Using Finite Mixture Exponential Random Graph Models," Psychometrika, Springer;The Psychometric Society, vol. 85(1), pages 8-34, March.
    19. Federica Bianchi & Francesco Bartolucci & Stefano Peluso & Antonietta Mira, 2020. "Longitudinal networks of dyadic relationships using latent trajectories: evidence from the European interbank market," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(4), pages 711-739, August.
    20. Tom Broekel & Pierre-Alexandre Balland & Martijn Burger & Frank Oort, 2014. "Modeling knowledge networks in economic geography: a discussion of four methods," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 53(2), pages 423-452, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:compsc:v:29:y:2012:i:3:p:279-313. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: http://pss.la.psu.edu/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.