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Leveraging Advances in Social Network Thinking for National Security Proceedings of a Workshop (2018) / Chapter Skim
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5 Discussion
Pages 37-44

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From page 37...
... He noted that all agencies within the IC have made considerable efforts to examine existing collaborative communities that appeared to work well and create new communities incorporating their features. Pherson pointed out that some of the most effective communities were found to be those that drew their members from those who had worked together before; they could form a cohesive group quickly.
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From page 38...
... a common lexicon. He elaborated that for these communities, mutual trust refers to a willingness to demonstrate one's own vulnerability; mutual benefit refers to everyone getting something productive out of the effort; incentives refer to support from management and leadership to engage in collaborative behaviors; mission criticality refers to recognition that collaboration should revolve around what team members must do every day and not be considered an add-on task; access and agility refer to permission and flexibility to participate; and a common lexicon refers to having common understandings of language, definitions, and rules of operation.
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From page 39...
... He argued for greater emphasis on building social or cultural models from such disciplines as sociology, psychology, and cognitive science to inform artificial intelligence systems. Kenneth Joseph, Northeastern University, expressed concern that some machinelearning approaches are starting to emulate human cognitive biases.
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From page 40...
... She emphasized the promise of funding efforts that would bring together complementary expertise and create research teams able to integrate knowledge from psychology, cognitive neuroscience, sociology, social network thinking, and statistics. Both Emily Falk, University of Pennsylvania, and Zachary Neal, Michigan State University, agreed that interdisciplinary teams will be important in the future.
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From page 41...
... . Hsinchun Chen, University of Arizona, suggested a different approach to the importance of interdisciplinary work -- what he called a "three-leg approach to human data fusion." In developing data mining tools for practical applications, he found that this work requires knowledge from the social and behavioral sciences to provide theory and explanations for social behaviors; analytic techniques from the data sciences to enable the aggregation of information from noisy data; and input from practitioners to provide insight on application and practical challenges.
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From page 42...
... He proposed developing anonymized, summary statistics to create a historical record of social activity without violating data retention rules. He acknowledged that some form of a consensus process would be necessary among the various research communities to determine how these summary statistics would be created and what information they would include.
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From page 43...
... He underscored the importance of work on causal inference, which he characterized as an important methodological issue for moving the field of network science beyond describing networks to evaluating their processes and outcomes. He added that the type of research envisioned at the workshop will require interdisciplinary teams whose members have enough knowledge of each other's areas of expertise to engage collectively in solving some of the research challenges highlighted at the workshop.
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From page 44...
... She suggested that scholars in the field of network science think about creating anthologies of robust findings in this area to help the IC and new researchers recognize what is implicit in the science of network thinking.
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Key Terms

  • social network
  • social media
  • network science
  • mutual trust
  • summary statistics

  • social networks
  • media data
  • cognitive neuroscience
  • data retention
  • data mining

  • mission criticality
  • collaborative communities
  • professional networks
  • called attention
  • mutual benefit

  • cognitive biases
  • retention rules
  • interdisciplinary teams
  • common lexicon
  • neuroscience research

  • network interactions
  • research computation
  • social behaviors
  • social scientists
  • ing social

  • human data
  • research discussed
  • social activity
  • research explore
  • research purposes


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