‘Social Sharing’ By Means of Distributed Computing: Some Results From A Study of SETI@home

1. ‘Social Sharing’ By Means of Distributed Computing: Some Results From A Study of SETI@home Hans-Jürgen Engelbrecht Massey University August 2005 H.Engelbrecht@massey.ac.nz http://www.massey.ac.nz/~hengelbr/

2. 1. Introduction • Information and Communication Technologies (ICT) are General Purpose Technologies. • One of many associated innovations: Distributed computing, grid computing. • Enables non-commercial sharing of physical, rivalrous goods via the Internet: Such ‘social sharing’ is a form of economic production (Benkler, 2004).

3. ‘Shareable goods’ • Sharing of computing power and bandwidth. • Two features of ‘shareable goods’ (Benkler, 2004): • They are lumpy (PCs come in discrete units). • They are of ‘mid-grained’ granularity (PCs are widely privately owned and systematically have slack capacity).

4. ‘Shareable goods’ ctd. • What determines the extent of ‘social sharing’? Technological conditions, but also cultural practices and tastes (Benkler, 2004) and social and legal conditions (David, 2004).

5. 2. SETI@home • Prime example of a voluntary non-commercial Internet-based distributed computing project: SETI@home. • Launched in May 1999. • Download screen saver. • Analysis of Arecibo radio telescope data. • SETI@home the most powerful special purpose supercomputer in the world.

6. SETI@home ctd. • Worldwide phenomenon (except for Mauritius, Palestine and Vatican City). • Incentives build into client interface, e.g. user and results data. • By Dec. 2004, there had been: • More than 5 million contributors. • Providing over 2 million years of CPU time (more than 1000 years of CPU time during the last day alone).

7. SETI@home ctd. • SETI country data available for: Dec. 10th, 2002; Dec. 11th, 2003; Dec. 13th, 2004. • Dependent variables used in the regression model: • SETI participants per capita. • SETI results per capita(measures actual outcomes and is arguably a better Internet-intensity variable than ‘hours of use’).

8. 3. Explanatory variables • What determines SETI@home cross-country participation and its intensity? • Aim: To include as many countries as possible. • Therefore, modelling is severely restricted and I use only a few key explanatory variables in the regressions: • ITU’s ‘Digital Access Index’ (DAI). • GDP per capita (gdp). • The ‘Human Development Index’ (HDI). • Country group dummy variables.

9. The Digital Access Index (DAI) • ITU: The DAI tries to measure “the overall ability of individuals in a country to access and use ICTs…”. It provides the first truly global ICT ranking. • The DAI is a composite index made up of 8 underlying indicators to capture: • infrastructure (fixed telephone & mobile telephone subscribers), • affordability (Internet access price), • ‘knowledge’ (adult literacy, school enrolment), • quality (broadband subscribers, international Internet bandwidth), • actual usage of ICTs (Internet users).

10. Components of the Digital Access Index (DAI), 2002:

11. The DAI ctd. • Hypothesis: The DAI is a positive and statistically significant determinant of SETI@home participation and its intensity. • This would mean: On average, SETI@home participation and its intensity across countries matches inter-country differences in ICT accessibility.

12. Other explanatory variables • GDP per capita (in PPP adjusted US $): • Traditional proxy for ‘standard of living’. Key explanatory variable in numerous ICT and Internet diffusion studies. • It is expected to be a positive and statistically significant determinant of SETI@home participation and its intensity.

13. Other explanatory variables ctd. • The HDI: • A composite index which has emerged as the preferred measure of ‘development’. • It measures important dimensions of human development neglected by gdp, such as: living a long and health life and being educated. • It is best included alongside DAI and gdp as an additional explanatory variable.

14. Other explanatory variables ctd. • Country group dummy variables: • ITU’s “developed & advanced countries” versus ‘the rest’. • Alternatively: 6 regional dummy variables (similar to Caselli and Coleman II, 2001). See “Appendix: Country List”.

15. 4. Regression analysis • Matching data for 172 countries. • Dependent variables alternatively in 2004 levels and 2002-2004 changes. • Most regressions estimated in double-log form. • OLS with White’s heteroscedasticity correction. • Box-Cox regressions.

16. Regression results

17. Regression results ctd. • Increasing DAI and gdp by 1% increases dependent variables by a similar %tage (elasticity of ‘change in results per capita’ with respect to DAI somewhat lower). • DAI, gdp, and the general divide between rich&poor countries can explain most of the cross-country variation in SETI@home participation and its intensity (see R2s). • HDI dropped from preferred regressions (DAI and HDI highly correlated).

18. 5. The global SETI@home digital divide • By Dec. 2004, developed & advanced countries (about 15% of the sample population) accounted for over 90% of submitted results. • But: Indications of a slowly narrowing global SETI@home digital divide! • Growth rates for ‘users’ and ‘results’ higher in ”the rest”.

19. Developed & advanced countries versus ‘the rest’:

20. Developed & advanced countries versus ‘the rest’ ctd.:

21. Developed & advanced countries versus ‘the rest’ ctd.:

22. 6. Concluding comments • Further research needed: • For a less heterogeneous group of countries. This would allow more sophisticated modelling. • More sophisticated models are needed to enable more specific policy conclusions. • Will non-commercial ‘social sharing’ via the Internet become a dominant mode of economic production? • There is huge potential for it, but commercial distributed computing might greatly affect its realization.