MASTER’S THESIS ONLINE COOPERATION
Editor Pim Schaaf Master’s Thesis Sociology
Examining networked altruistic cooperation on Kiva.org
INTRODUCTION why study online cooperation This thesis is aimed on one aspect of the cyberspace, the web on which we by now interact daily with peers, coworkers, (potential) customers and so on. It aims REFERENCES to explain the emerging cooperation which, Smith, M. A. & Kollock, P. in its context, is surprising. “Given that (1999). Communities in online interaction is relatively anonymous, Cyberspace. London, UK: Routledge. that there is no central authority, and that it is difficult or impossible to impose monetary or physical sanctions on someone, it is striking that the Internet is not literally a war of all against all. For a student of social order, what needs to be explained is not the amount of conflict but the great amount of sharing and cooperation that does occur in online communities” (Kollock, 1999, emphasis added). Specifically then, this thesis is aimed on explaining peoples behavior through their membership of groups.
Goal I like a world in which we not merely take action for our own gain, but take into account the gain of others too and, if possible, prioritize that. This ideal of course is somewhat far fetched as a basis for this thesis, but I it can shed some light on the choice of subject I make when I shorten the list of aspects of online cooperation on the basis of my interest only. What I am talking about here are social dilemmas. For example the so called public goods games in which what is produced (the good), through cooperation from (one or more) members of the public (a group of people), is shared equally among all members regardless their participation.
“Sociologist are going to love the next 100 years.” (Dvorak, 1996 in Smith & Kollok, 1999)
Another example is the prisoner’s dilemma. The field of research that engages in theorizing social dilemma’s is vast but mainly supported by computer-simulated experiments. Ultimately I would like to consider creating a game-like environment that emulates the experiments but within which real actors play. Realisticly though I think I should first focus on testing existing middle-range theories on REFERENCES ‘real’ data (instead of computer generated Oliver, P.E. & Marwell, G. data). (1985). A theory of critical Through their API (application programming mass. I. Interdependence, interface) I have acces to data from Group Heterogeneity, and Kiva.org, a website that leverages the inter- the Production of Collective Action. Amer. J. Sociol., net to establish loans for the poor. On this 91(3), 522-556. site, as a visitor/user, you can microfinance projects of the poor, starting from $25 loans. All that is needed is an act of cooperation from the user, specifically a monetary transfer, which over the course of $293 million in loans by 727,430 lenders gets reciprocated for 98.91% of these cases until date (this may include outstanding loans). These statistics show the success of Kiva.org.
Theoretical relevance The question is, how could the succes of Kiva.org happen? Why do people take the risk of investing their money on a platform that in a best-case scenario repays nothing more than what was initially invested? These questions all circle around the spreading of the idea of investing on Kiva.org. They investigate how the behavior of people on Kiva.org is synchronized. Effectively they pose the question of how these acts are linked. Commonly we believe that for a lot of our actions we are motivated by other people. There are plenty of sociological works that suggest peoples actions are controlled
“Much of the preliminary phase of theorizing involves locating oneself in a social/intellectual space rather than actually engaging ideas.” (Oliver and Marwell, 2001)
socially. There are reasons to suspect that on Kiva.org this works differently. Mostly because, typically, lending on Kiva.org is very much something that is done individually. That is mainly because joining Kiva.org is fairly anonymously (at least you can remain anonymous for other users of the platform and there is no way to check someone’s identity) but also because your investment can become your loss without anyone covering for it. However, despite of this, action is synchronized. Big loans are accumulated through individual micro financing. Hanaki, Peterhansl, Dodds & Watts (2007) have noticed how various mechanisms suggested to explain prosocial behavior “focus exclusively on an individual’s choice of actions with respect to their interaction partners, treating the choice of partners - the individual’s social network - as exogenous.” Subsequently they state that “the coevolution of networks and behavior has not received as much attention as it deserves.” Could it be that the social networks explain the behavior seen on Kiva.org?
REFERENCES Hanaki, N., Peterhansl, A., Dodds, P.S. & Watts, D.J. (2007). Cooperation in Evolving Networks. Management Science, 53(7), p. 1036-1050.
RESEARCH QUESTION what needs to be explained Research question What relationship can be found between group membership and loaning behavior on Kiva.org and how can this be explained?
Sub questions (theoretical) • • •
How can group membership on Kiva. org be described sociologically? How can cooperation on Kiva.org be defined sociologically? Which explanations, based on sociological literature, can explain for the relationship between group membership and loaning behavior on Kiva.org?
Sub questions (empirical) • • • •
How is group membership defined on Kiva.org? How is loaners behavior defined on Kiva.org? How can patterns in loaning behavior be deduced through the Kiva.org API? How can patterns in loaning behavior be matched with group membership?
THEORETICAL FRAMEWORK
behavior and networks
BEHAVIOR ON STATIC NETWORKS the 30-second theory The spatial game can provide an explanation for the basis on which collective social behaviour is maintained. In a spatial game players interact with neighbours in REFERENCES some spatial array (May, Bohoeffer & Nowak, May, R.M., Bohoeffer, M.A. 1995). & Nowak (1995). Spatial The small-world phenomenon (Watts, 1999) games and evolution of similarly describes statistics of separation cooperation. Proc. Third between players as a common property of European Conf. Adv. Artifical Life, 929, 749-759. networks. The perspective of behavior on static net- Watts, D.J. (1999). Networks describe how these arrangements works, dynamics, and the form the basis of collective dynamics like small-world phenomenon. Amer. J. Sociol., 105, 493synchronizability. 527.
On Kiva.org Micro financing on Kiva.org typically attracts small investments. Some form of collective action is required to accumulate enough investments to complete a loan. How lenders relate on Kiva.org might explain how collective dynamics like this can occur.
EVOLUTION OF NETWORKS the 30-second theory Theory on the evolution of networks (Skyrms & Pemantle, 2000; Jackson & Watts, 2002) describes the way a network model changes dynamically through, REFERENCES for instance, stochastic learning. Within this Skyrms, B.R. & Permantle field of theory individuals are seen to begin (2000). A dynamic model of to interact at random but payoffs of these in- social network formation. teractions define an emerging network struc- Proc. Natl. Acad. Sci. USA, 97(16), 9340-9346. ture.
On Kiva.org The success experienced by individual micro financiers on Kiva.org that, assuming small investments, can only be achieved through collective action, might explain why and how people group up through group memberships on Kiva.org.
Jackson, M.O. & Watts, A. (2002). On the formation of interaction networks in social coordination games. Games Econom. Behav., 41, 265-291.
COEVOLUTION OF NETWORKS AND BEHAVIOR the 30-second theory Organizing principles govern the topology and evolution of real networks REFERENCES (Albert & Barabasi, 2002). That is what is de- Albert, R., Barabasi, A.-L. scribed from the perspective of coevolution (2002). Statistical mechanics of complex networks. of networks and behavior. Network dynamics nor behavior can solely Rev. Modern. Phys., 74(1), be appointed to describe real world net- 74-97. works. Through coevolution they both form the basis for collective social behavior.
On Kiva.org Success on Kiva.org, a loan made by multiple loaners, according to the theory of coevolution of networks and behavior, can only be sufficiently described if both the effects of behavior on the network and the effects of the network on behavior are researched.
RESEARCH DATA
Who will be subject of this research
KIVA.ORG API gathering and selecting data A self-reliant way to gather data on cooperation on Kiva.org is by connecting scripts to their application programming interface. DEFINITION
Data types
An application programming interface (API) is a source code-based specification intended to be used as an interface by software components to communicate with each other. (Wikipedia, 2012)
Kiva.org contains various types of data. That of the lenders, their teams and the borrowers. A lender’s profile on Kiva.org contains extensive statistics on the lender’s lending behavior. Team summaries on Kiva.org contain aggregated statistics reflecting team-level lending REFERENCES behaviors. Team memberships describe the Wikipedia (2012), Application programming interface. social network that team members form. Retreived from http:// A borrowers profile on Kiva.org contains exen.wikipedia.org/wiki/Aptensive data on the loan in a ‘loan overview’. plication_programming_in-
Method
terface.
With these data the required perspective can be attained. Respondents can be selected (randomly). Their loaning behavior as well as the group memberships for the respondents can then be obtained. Respondent ID - unique identifier (e.g. 1) Loan IDs - string of IDs (e.g. 1,2,3,4) Group IDs - string of group IDs (e.g. 1,2,3,4) These data can then be matched for patterns. For instance, for each respondent with Loan ID: 1 group Membership IDs can be matched to check for matching memberships that can explain Loan ID correspondence between respondents.
“An API may include specifications for routines, data structures, object classes, and variables.” (Wikipedia, 2012)