This week a group of economists won the Nobel Prize in economics for applying the laws of randomisation to non-profit analysis. The results are astounding.
Abhijit Banerjee and Esther Duflo of MIT and Michael Kremer of Harvard started asking themselves how do we know if the money and programs non-profits dispatch to remedy social problems are actually working?
What once always seemed intuitive "Kids who are struggling to learn need access to books" is now being rigorously tested through the scientific method like never before. In some cases they actually benefit more from medicine, or incentives that tie teacher pay with student performance.
This thinking is completely changing the way non profits and foundations are going to do business in the future, but it also holds great promise for blockchain based social good efforts, especially applied with machine learning.
The method developed by Banerjee, Duflo, and Kremer effectively relies on randomised trials. You have two almost identical groups and one group gets and intervention, and the other does not. You can then directly measure the impact of the intervention against the group that didn't receive it, known as the control.
However, in theory a blockchain built to engage a community facing a problem could designed to facilitate multiple stakeholders, weigh hundreds of factors, and respond to life's invariable messiness more effectively than a single objective control trial. With systematic and high quality data and a blockchain powered with machine learning like the GNY chain it may be possible in a few short years to get teams of researchers working on a single issue from multiple perspectives and theses to collaborate simultaneously.
It's an exciting time to imagine how we might work together to tackle the multitude of problems and inequalities we face in the coming century.
More information on the prize and the winners on NPR