following post is an ongoing thinking on “collective intelligence measurement in the context of participative services” (having transit in mind as a first contextual application)
at a community scale, users sharing information can become a collective intelligence in the context of the service they are using (assuming that user incentives + information vizualization makes it a viable collective intelligence)
we have an opportunity to explore and quantify this collective intelligence in context.
At the information level itself :
- passive user generated information
sensors and tracking devices… mobile phone, car, … that creates mesh/grids networks through time, space, context, // DATAS
- active user generated information
from noise to social information to functional information // the information granularity
// DATAS from statistics, geo statistics, time statistic of the type of information exchanged, the participation mechanics behind it, being generated out of it, the average information given by users, their reliability, …
the size of the community of these users doing infomediation :
From a small group of interest on a targeted time window, space, context, spot, culture to a wider and spread community throughout time and space there is a critical mass correlation that we are currently studying applied in the specific context on transportation where information assymetry between users / transit is huge.
we can find a way to quantify collective intelligence, in a given network or service and start using those measurements to compare the information reliability / the user satisfaction / risk and cost management … as in any other kinds of information systems…
(Any advices/ references are more than welcome here)