Via Ajaxian I come to know that Kiva, the distributed micro loan platform, has just released a new developer API that gives third parties access to create innovative applications on top of the platform! I’m investing 100 dollars in Kiva since some years (my Kiva lender profile) and I found it a neat idea! If you don’t know what Kiva is, check this video about Kiva, there is also a Kiva Facebook application.
And the amazing guys behind “How We Know Us – Investigating, discussing, and measuring social capital” already started poking at the data available with the hypothesis network analysis will be able to help predict rates of return! Check
the image of the complete(?) network of relationships between the recipients and the lenders and the partners.
Go play with the new KIVA API!
Some papers are more worth than others.
Dynamic spread of happiness in a large social network: longitudinal analysis over 20 years in the Framingham Heart Study by James H Fowler and Nicholas A Christakis.
Solid analysis based on data from 4739 individuals followed from 1983 to 2003.
Conclusions People’s happiness depends on the happiness of others with whom they are connected. This provides further justification for seeing happiness, like health, as a collective phenomenon.
Objectives To evaluate whether happiness can spread from person to person and whether niches of happiness form within social networks.
Clusters of happy and unhappy people are visible in the network, and the relationship between people’s happiness extends up to three degrees of separation (for example, to the friends of one’s friends’ friends).
People who are surrounded by many happy people and those who are central in the network are more likely to become happy in the future.
Longitudinal statistical models suggest that clusters of happiness result from the spread of happiness and not just a tendency for people to associate with similar individuals. A friend who lives within a mile (about 1.6 km) and who becomes happy increases the probability that a person is happy by 25% (95% confidence interval 1% to 57%). Similar effects are seen in coresident spouses (8%, 0.2% to 16%), siblings who live within a mile (14%, 1% to 28%), and next door neighbours (34%, 7% to 70%). Effects are not seen between coworkers. The effect decays with time and with geographical separation.
(credits: Photo by beija-flor released on Flickr under Creative Commons Attribution Noncommercial No Derivative license)