4.5 Article

Social Networks under Stress: Specialized Team Roles and Their Communication Structure

Journal

ACM TRANSACTIONS ON THE WEB
Volume 13, Issue 1, Pages -

Publisher

ASSOC COMPUTING MACHINERY
DOI: 10.1145/3295460

Keywords

Social networks; organizations; temporal dynamics; collective behavior

Funding

  1. Northwestern University Institute on Complex Systems (NICO)
  2. U.S. Army Research Laboratory
  3. U.S. Army Research Office [W911NF-09-2-0053]
  4. Defense Advanced Research Projects Agency [BAA-11-64]
  5. Simons Investigator Award
  6. Google Research Grant
  7. Facebook Faculty Research Grant
  8. ARO MURI grant QUANTA: Quantitative Network-based Models of Adaptive Team Behavior
  9. NSF [IIS-0910664, IIS-1617820]

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Social network research has begun to take advantage of fine-grained communications regarding coordination, decision-making, and knowledge sharing. These studies, however, have not generally analyzed how external events are associated with a social network's structure and communicative properties. Here, we study how external events are associated with a network's change in structure and communications. Analyzing a complete dataset of millions of instant messages among the decision-makers with different roles in a large hedge fund and their network of outside contacts, we investigate the link between price shocks, network structure, and change in the affect and cognition of decision-makers embedded in the network. We also analyze the communication dynamics among specialized teams in the organization. When price shocks occur the communication network tends not to display structural changes associated with adaptiveness such as the activation of weak ties to obtain novel information. Rather, the network turtles up. It displays a propensity for higher clustering, strong tie interaction, and an intensification of insider vs. outsider and within-role vs. between-role communication. Further, we find changes in network structure predict shifts in cognitive and affective processes, execution of new transactions, and local optimality of transactions better than prices, revealing the important predictive relationship between network structure and collective behavior within a social network.

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