1. 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. 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. 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. 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. 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. 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. 7 SETI@home ctd. SETI country data available for: Dec. 10 th, 2002; Dec. 11 th, 2003; Dec. 13 th, 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. 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. 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. 10 Components of the Digital Access Index (DAI), 2002:

11. 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. 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. 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. 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. 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. 16 Regression results

17. 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 R 2 s). HDI dropped from preferred regressions (DAI and HDI highly correlated).

18. 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. 19 Developed & advanced countries versus ‘the rest’:

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

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

22. 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.