Transcript Grand Scientific & Societal Challenges

Grand Scientific & Societal
Challenges
Larry Brandt, Roberto Dandi, Keith Hampton,
Erik Jakobbson, Laura Koehly, Michael Macy,
Peter Monge, Garry Robins, Bob Wilhelmson
The (Grand)Mother of All Grand
Challenges for SNA
 We know lots about
 Individuals (surveys)
 Small network structures (snapshots)
 Populations (aggregations, gov’t data)
 But it is very hard to study the thing
that is most important:
 Social interaction: what goes on inside
the network
 How networks change
A Tale of Two CI-ties
 Research CI: Supercomputers, digitial
libraries, software, caves, etc.
 Social CI: email, blogs, news groups,
cell phones, text messaging
Some good news & some bad news
 First the good news
 SCI leaves a digital trail
 Possibility for the first time to collect
real-time data about social interaction
 Now the bad news: the trails are big
and messy
 Solution:
 Use RCI to tap SCI
 Next generation SNA
Illustrations
 Multiplex genetic susceptibility testing
 SCI: connects patients with databases,
providers, other patients
 RCI:
 how does information about genetic
susceptibility change behavior?
 how do support networks among
susceptibles change behavior?
Local environmental elites
 SCI: communications among officials
and leaders
 RCI:
 mining the missives
 how does the flow of information and
influence alter policy and practice?
Biotech firms
 SCI: mechanisms for collaborations
and info exchange among labs
 RCI:
 Source of clues about how emerging
networks shaped the evolution of the
biotech industry
 What is an optimal network?
Natural language processing
 SCI: political blogs
 RCI:
 tools that can read not just the links but
the texts
 how people influence one another in
response to the influence they receive
 Empirical tests of
 Tipping models
 Hopfield models
 Diffusion models