Group Membership and Public Goods Provision: The Case of Kiva and Wikipedia

Sophy Ramírez  | 28 de septiembre de 2018  | Vistas: 41

Yan Chen, professor of information at the University of Michigan shows in this conference the importance of computer systems in online services to solve the free-rider problem, exposing an experiment she developed in Kiva, an online micro financial service, and in this presentation explains how it works.

She first points out how the computer science influence in an online service, using randomize fictitious experiments and the predictive accuracy from a machine learning, can this promote the goal of the company. In this case, the goal of Kiva.

The experiment is trying to tease out what the coordination or competition is more effective in getting people to make loans, getting teams to be more active”.

Chen mentions some cases where the free-riders problem could be solved, how some computer systems are backed by a behavioral mechanism design, and the way this helps to form a sense of inclusion according to specific preferences, instead of making what is called ingroup favoritism and outgroup discrimination.

The professor explains the methodology of Kiva, the problem that the organization had before they implemented a system of lending groups. She describes how introducing teams of lending and algorithms that send emails to involve people to participate, have been developing the functioning and increasing the productivity of it.

Lenders will be more likely to join teams if we make good recommendations, and this is when computer science comes in”.

Chen shows the use of algorithms and how they work, mentions the influence that they can produce in the results and progress, and explains that some of the recommendations that the algorithms have are more effective than others, concluding that what makes a team membership useful as a mechanism is coordination and competition.