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Approximate variational inference for a model of social interactions

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  • Angelo Mele

    ()
    (Johns Hopkins University - Carey Business School)

Abstract

This paper proposes approximate variational inference methods for estimation of a strategic model of social interactions. Players interact in an exogenous network and sequentially choose a binary action. The utility of an action is a function of the choices of neighbors in the network. I prove that the interaction process can be represented as a potential game and it converges to a unique stationary equilibrium distribution. However, exact inference for this model is infeasible because of a computationally intractable likelihood, which cannot be evaluated even when there are few players. To overcome this problem, I propose variational approximations for the likelihood that allow approximate inference. This technique can be applied to any discrete exponential family, and therefore it is a general tool for inference in models with a large number of players. The methodology is illustrated with several simulated datasets and compared with MCMC methods.

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File URL: http://www.netinst.org/Mele_13-16.pdf
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Bibliographic Info

Paper provided by NET Institute in its series Working Papers with number 13-16.

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Length: 17 pages
Date of creation: Sep 2013
Date of revision:
Handle: RePEc:net:wpaper:1316

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Web page: http://www.NETinst.org/

Related research

Keywords: Variational approximations; Bayesian Estimation; Social Interactions;

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  1. Blume Lawrence E., 1993. "The Statistical Mechanics of Strategic Interaction," Games and Economic Behavior, Elsevier, vol. 5(3), pages 387-424, July.
  2. Ormerod, J. T. & Wand, M. P., 2010. "Explaining Variational Approximations," The American Statistician, American Statistical Association, vol. 64(2), pages 140-153.
  3. Ryo Nakajima, 2004. "Measuring Peer Effects on Youth Smoking Behavior," ISER Discussion Paper 0600, Institute of Social and Economic Research, Osaka University.
  4. Angelo Mele, 2010. "A Structural Model of Segregation in Social Networks," Working Papers 10-16, NET Institute.
  5. Braun, Michael & McAuliffe, Jon, 2010. "Variational Inference for Large-Scale Models of Discrete Choice," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 324-335.
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