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The Dynamics of Common Knowledge on Social Networks

PI: Gizem Korkmaz, University of Virginia

Year selected for award: 2016

The Dynamics of Common Knowledge on Social Networks: An Experimental Approach

Principal Investigator: Gizem Korkmaz, University of Virgina

Co-Principal Investigator: Read Montague, Virginia Tech; Monica Capra, Claremont Graduate University; Chris Kuhlman, University of Virginia; and Mark Orr, University of Virginia

Years of award: 2017-2021

Managing Service Agency: Air Force Office of Scientific Research

Project Description:
The use of social networking sites (especially Facebook, Twitter and YouTube) was a distinctive feature of the infamous uprisings against authoritarian regimes such as the Arab spring and Gezi protests in Istanbul. Social media helps the spread of information prior to, as well as during protests, allowing the protestors to reach a critical mass of participants. Protest is a collective action problem where an individual wants to participate only if joined by “enough" others since the risk of prosecution can be reduced if overwhelming numbers of people successfully coordinate their actions. In game-theoretic contexts, coordination requires that agents know about each other’s willingness to participate and that this information is common knowledge among a sufficient number of people. Knowledge of what other players know about other players is crucial for coordination. Social networks that represent local interactions may facilitate information sharing in a way that generates actionable common knowledge within groups (as opposed to mass media).

Chwe (2000)1 and Korkmaz (2014)2 combine social structure and individual incentives together, and provide a rigorous game-theoretic formalization of common knowledge, and the characterizing network structures. The former emphasizes simple node-to-node or bilateral communication, whereas the latter studies the ef-fects of “richer" on-line communication mechanisms, such as Facebook. However, very little is known about how well these stylized models explain phenomena in practice; nor do these models incorporate individual and behavioral factors, and psychological processes. We aim to empirically test novel hypotheses at both individual and group levels by conducting controlled human subjects experiments in three different environments (laboratory, online, and neuroimaging) that will inform models of collective behavior. The objectives of this integrated framework are (i) to characterize how different communication mechanisms can facilitate actionable common knowledge through local interactions, (ii) to understand the effect of network structure, and (iii) to quantify individual and group level behaviors, and neural processes that affect its formation.

Select Publications:
Korkmaz, G., Capra, M., Kraig, A., Kuhlman, C.J., Lakkaraju, K., and Vega-Redondo, F. 2018. “Coordination and Collective Action on Communication Networks.” In Proceedings of the 17th ACM International Conference on Autonomous Agents and Multi-agent Systems (AAMAS 2018). pp. 1062-1070. International Foundation for Autonomous Agents and Multiagent Systems.
Korkmaz, G., Kuhlman, C.J., Goldstein, J., and Vega-Redondo, F. (forthcoming) “A Computational Study of Homophily and Diffusion of Common Knowledge on Social Networks Based on a Model of Facebook.” Social Network Analysis and Mining (SNAM). Springer.
Korkmaz, G.; Kuhlman, C.J., Goldstein, J., and Vega-Redondo, F. 2018. “A Model of Homophily, Common Knowledge and Collective Action Through Facebook.” In Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Network Analysis and Mining (ASONAM). pp. 409-412. IEEE.
Korkmaz, G., Kuhlman, C.J., Ravi, S.S., and Vega-Redondo, F. 2018. "Spreading of Social Contagions without Key Players." World Wide Web: Internet and Web Information Systems. 21:1187-1221.
Korkmaz, G., Kuhlman C.J., and Vega-Redondo F. 2017. “Can Social Contagion Spread Without Key Players?” In Proceedings of the 2016 IEEE International Conference on Behavioral, Economic and Socio-cultural Computing (BESC’ 16). pp. 1-6. IEEE.
Korkmaz, G., Kuhlman, C.J., Ravi, S.S., and Vega-Redondo, F. 2016. “Approximate Contagion Model of Common Knowledge on Facebook." In Proceedings of the 27th ACM Conference on Hypertext and Social Media (HT' 16). pp.  231-236. ACM.
Korkmaz, G.; Kuhlman, C.J., Marathe A., Marathe M. V., and Vega-Redondo, F. 2014. “Collective Action through Common Knowledge Using a Facebook Model.” In Proceedings of the 2014 ACM International Conference on Autonomous Agents and Multi-agent Systems (AAMAS). pp. 253-260. International Foundation for Autonomous Agents and Multiagent Systems.