Human Sensor Networks

Cynefin Wiki

• A human sensor network is a network of engaged people who are collecting and sharing observations on an ongoing basis
• Rather than just capturing a snapshot in time, we are instead enabling the continuous collection of stories and observations over long periods of time, much like a virtual journal

• HSN can provide a continuous account of what a large proportion of the target community/population is observing and experiencing, and what that means to them
  • Provides people with a framework through which they can reflect on their day-to-day experiences and interpret them
  • Engages participants in making sense of the system of which they are part
    • We call this ‘Distributed Cognition’ — when a network of people make sense of what’s happening around them and what it means to them by combining multiple perspectives and viewpoints that transcend the limitations of the individual
  • This approach draws on a diverse range of situated knowledge and wisdom
  • Multiple safe-to-fail experiments running in parallel can then be employed to explore pathways towards a desired outcome

[Dave Snowden]

• This involves using people to elicit oral histories from a larger group of people
  
• Wales example
  • They are using school children to ask people in the community what matters in their community
  • This project will replace polling and focus groups
  • It will provide the basis for evidence-based policy-making
• Better still, once this network has been designed and created, it can be reactivated later to provide answers to specific questions as the need arises
• Further, these networks can be used to disseminate information rapidly
  
• Situation on Syria to 2000 American agents example
  • Using SenseMaker® MassSense agent interpretations were mapped as high-abstraction metadata
    • Self-interpreted narrative data cannot be gamed nor filtered by cognitive biased analysts
  • Within one minute we presented back a distribution map of all the different opinions
  • Reality is recognized in visual landscape maps
    • Easily see the dominant and minority views held by multiple agents
    • Detects weak signals by affording insights into attitudes and beliefs which are lead indicators