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Networks: Learning, Cooperation and Community Norms

Paper Session

Sunday, Jan. 6, 2019 8:00 AM - 10:00 AM

Atlanta Marriott Marquis, L503
Hosted By: Econometric Society
  • Chair: Matthew O. Jackson, Stanford University

Learning Dynamics in Social Networks

Simon Board
,
University of California-Los Angeles
Moritz Meyer-ter-Vehn
,
University of California-Los Angeles

Abstract

This paper proposes a tractable model of Bayesian learning on social networks in which agents choose whether to adopt an innovation. We study the impact of network structure on learning dynamics and diffusion. In tree networks, we provide conditions under which all direct and indirect links contribute to an agent’s learning. Beyond trees, not all links are beneficial: An agent’s learning deteriorates when her neighbors are linked to each other, and when her neighbors learn from herself. These results imply that an agent’s favorite network is the directed star with herself at the center, and that learning is better in “decentralized” networks than “centralized” networks.

Seeking Relationship Support: Strategic Network Formation and Robust Cooperation

David Miller
,
University of Michigan
Xu Tan
,
University of Washington

Abstract

We study cooperation on social networks with private monitoring and communication. For arbitrary networks, we construct a class of multilateral restitution equilibria that attain high cooperation on all supported links---i.e., all links that are in triangles. These equilibria are robust to social contagion, bilaterally renegotiation proof, and invariant to players' beliefs about the network outside their local neighborhoods. In these equilibria, guilty players are not ostracized, instead they remain to sustain the stability of the cooperation network by exerting high effort for their innocent partners, and they are willing to do so because they are compensated for their effort costs. Anticipating cooperation, players in a network formation game with random opportunities to form links will strategically form a network with realistic small worlds properties, including high support but relatively low clustering.

Behavioral Communities and the Atomic Structure of Networks

Matthew O. Jackson
,
Stanford University
Evan Storms
,
Stanford University

Abstract

We develop a theory of `behavioral communities' and the `atomic structure' of networks. We define atoms to be groups of agents whose behaviors always match each other in a set of coordination games played on the network. This provides a microfoundation for a method of detecting communities in social and economic networks. We provide theoretical results characterizing such behavior-based communities and atomic structures and discussing their properties. We also provide an algorithm for identifying behavioral communities. We discuss applications including: detecting which attributes a society's network fractures along, a method of estimating underlying preferences by observing behavioral conventions in data, and optimally seeding diffusion processes when there are peer interactions and homophily. We illustrate the techniques with applications to high school friendship networks and rural village networks.
Discussant(s)
Ben Golub
,
Harvard University
Alexander Wolitzky
,
Massachusetts Institute of Technology
Rakesh Vohra
,
University of Pennsylvania
JEL Classifications
  • D8 - Information, Knowledge, and Uncertainty
  • D0 - General