A Network Formation Model in Extensive Form

2001 ◽  
pp. 153-172
Author(s):  
Marco Slikker ◽  
Anne Van Den Nouweland
Keyword(s):  
Network Formation ◽  
Formation Model ◽  
Extensive Form

Analysis and Interventions in Large Network Games

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Francesca Parise ◽  
Asuman Ozdaglar
Keyword(s):  
Network Formation ◽  
Autonomous Systems ◽  
Equilibrium Analysis ◽  
Annual Review ◽  
Publication Date ◽  
Stochastic Network ◽  
Large Network ◽  
Formation Model ◽  
Efficient Manner ◽  
Network Games

We review classic results and recent progress on equilibrium analysis, dynamics, and optimal interventions in network games with both continuous and discrete strategy sets. We study strategic interactions in deterministic networks as well as networks generated from a stochastic network formation model. For the former case, we review a unifying framework for analysis based on the theory of variational inequalities. For the latter case, we highlight how knowledge of the stochastic network formation model can be used by a central planner to design interventions for large networks in a computationally efficient manner when exact network data are not available. Expected final online publication date for the Annual Review of Control, Robotics, and Autonomous Systems, Volume 4 is May 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Social Clubs and Social Networks

2021 ◽  
Vol 13 (1) ◽  
pp. 224-251
Author(s):  
Chaim Fershtman ◽  
Dotan Persitz
Keyword(s):  
Social Networks ◽  
Network Formation ◽  
Third Parties ◽  
Formation Model ◽  
Trade Off ◽  
Weighted Network ◽  
Social Clubs ◽  
Model Based

We present a strategic network formation model based on membership in clubs. Individuals choose affiliations. The set of all memberships induces a weighted network where two individuals are directly connected if they share a club. Two individuals may also be indirectly connected using multiple memberships of third parties. Individuals gain from their position in the induced network and pay membership fees. We study the club congestion model where the weight of a link decreases with the size of the smallest shared club. A trade-off emerges between the size of clubs, the depreciation of indirect connections, and the membership fee. (JEL D71, D85, Z13)

scholarly journals Voting to the Link: a Static Network Formation Model

2020 ◽  
Vol 17 (3) ◽  
pp. 207-228
Author(s):  
Róbert Pethes ◽  
Levente Kovács
Keyword(s):  
Network Formation ◽  
Formation Model

scholarly journals Using Aggregated Relational Data to Feasibly Identify Network Structure without Network Data

2020 ◽  
Vol 110 (8) ◽  
pp. 2454-2484 ◽  
Author(s):  
Emily Breza ◽  
Arun G. Chandrasekhar ◽  
Tyler H. McCormick ◽  
Mengjie Pan
Keyword(s):  
Social Network ◽  
Network Structure ◽  
Field Experiments ◽  
Network Formation ◽  
Relational Data ◽  
Network Data ◽  
Formation Model ◽  
Social Network Data ◽  
Level Statistics ◽  
Aggregated Relational Data

Social network data are often prohibitively expensive to collect, limiting empirical network research. We propose an inexpensive and feasible strategy for network elicitation using Aggregated Relational Data (ARD): responses to questions of the form “how many of your links have trait k ?” Our method uses ARD to recover parameters of a network formation model, which permits sampling from a distribution over node- or graph-level statistics. We replicate the results of two field experiments that used network data and draw similar conclusions with ARD alone. (JEL C81, C93, D85, Z13)

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