software

Our knowledge about what makes digital commons work is terribly under-theorized.  Yes, there are famous works by Lawrence Lessig and Yochai Benkler, and there are lots of projects and websites that are based on commoning such as like Wikipedia, free software, Arduino, open access journals, among countless others.  But can we identify core principles for organizing digital commons?  Can we use that knowledge to engineer the evolution of new commons?  Identifying such principles just might let us move beyond “openness” as the ultimate goal of online life, to a more sustainable goal, the self-governed commons.

It has been a pleasure to discover that some computer scientists are actively exploring how Elinor Ostrom’s principles for successful commons might be applied to the design of software.  Consider this intriguing essay title: "Axiomatization of Socio-Economic Principles for Self-Organizing Institutions: Concepts, Experiments and Challenges,“ which appeared in the ACM Transactions on Autonomous and Adaptive Systems, in December 2012.  

The piece is by British electrical and electronic engineer Jeremy Pitt and two co-authors, Julia Schaumeier and Alexander Artikis. The abstract is here.  Unfortunately, the full article is behind a paywall, consigning it to a narrow readership.  I shall quote from the abstract here because it hints at the general thinking of tech experts who realize that the social and the technical must be artfully blended:

We address the problem of engineering self-organising electronic institutions for resource allocation in open, embedded and resource-constrained systems.  In such systems, there is decentralised control, competition for resources and an expectation of both intentional and unintentional errors.  The ‘optimal’ distribution of resources is then less important than the sustainability of the distribution mechanism, in terms of endurance and fairness, based on collective decision-making and tolerance of unintentional errors.  In these circumstances, we propose to model resource allocation as a common-pool resource management problem, and develop a formal characterization of Elinor Ostrom’s socio-economic principles for enduring institutions. 

The tech world frequently talks about open source software as a collaborative endeavor, but it is less apt to use the word “commons,” let alone engage in rigorous empirical analysis for understanding how software commons actually work.  The arrival of Internet Success:  A Study of Open-Source Software Commons (MIT Press) is therefore a welcome event.  This book is the first large-scale empirical study to look at the social, technical and institutional aspects of free, libre and open source software (often known as “FLOSS”).  It uses extensive firsthand survey research, statistical analysis and commons frameworks for studying this under-theorized realm.

While most people may associate open source software with Linux, there are in fact tens of thousands of open source projects in existence.  Many consist of no more than two or three participants, and may have only an irregular existence.  However, many thousands of others attract a small but spirited team, and still others are huge, robust social ecosystems in their own right.

The authors of Internet Success, UMass Professor Charles M. Schweik and consultant Robert C. English, looked at the large universe of FLOSS projects hosted on SourceForge.net, a website that functions as a kind of clearinghouse for over 260,000 FLOSS projects (as of February 2011) and 2.7 registered software developers.  The site provides most of the tools that developers need to find colleagues and build a new FLOSS program – a Web repository of code, bug-tracking utilities, online forums, email mailing lists, a wiki, file downloading services, etc. 

While SourceForge is not the only such site for FLOSS projects, it is the largest and arguably representative of the universe of such projects.  With support from the National Science Foundation, Schweik and English set out to study the pool of software development projects on SourceForge to try to determine why some succeed, why others fail and why others simply languish.  They explain in excruciating technical, social science detail how they assembled and analyzed their datasets, which originate in a vast collection of SourceForge data on more than 130,000 projects as well as their own survey questionnaire of programmers.