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An empirical model for strategic network formation

Author: Nicholas A Christakis; National Bureau of Economic Research.
Publisher: Cambridge, Mass. : National Bureau of Economic Research, ©2010.
Series: Working paper series (National Bureau of Economic Research), no. 16039.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
We develop and analyze a tractable empirical model for strategic network formation that can be estimated with data from a single network at a single point in time. We model the network formation as a sequential process where in each period a single randomly selected pair of agents has the opportunity to form a link. Conditional on such an opportunity, a link will be formed if both agents view the link as beneficial  Read more...
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Additional Physical Format: Print version:
Empirical model for strategic network formation.
Cambridge, Mass. : National Bureau of Economic Research, ©2010
(DLC) 2010655929
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Nicholas A Christakis; National Bureau of Economic Research.
OCLC Number: 635515275
Description: 1 online resource (27, [2] pages) : illustrations.
Series Title: Working paper series (National Bureau of Economic Research), no. 16039.
Responsibility: Nicholas A. Christakis [and others].

Abstract:

We develop and analyze a tractable empirical model for strategic network formation that can be estimated with data from a single network at a single point in time. We model the network formation as a sequential process where in each period a single randomly selected pair of agents has the opportunity to form a link. Conditional on such an opportunity, a link will be formed if both agents view the link as beneficial to them. They base their decision on their own characateristics, the characteristics of the potential partner, and on features of the current state of the network, such as whether the the two potential partners already have friends in common. A key assumption is that agents do not take into account possible future changes to the network. This assumption avoids complications with the presence of multiple equilibria, and also greatly simplifies the computational burden of anlyzing these models. We use Bayesian markov-chain-monte-carlo methods to obtain draws from the posterior distribution of interest. We apply our methods to a social network of 669 high school students, with, on average, 4.6 friends. We then use the model to evaluate the effect of an alternative assignment to classes on the topology of the network.

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