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‘Social Sharing’ By Means of Distributed Computing: Some Results From A Study of [email protected] Hans-J ürgen Engelbrecht Massey University August 2005 [email protected] http://www.massey.ac.nz/~hengelbr/. 1. Introduction.

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social sharing by means of distributed computing some results from a study of seti@home

‘Social Sharing’ By Means of Distributed Computing: Some Results From A Study of [email protected]

Hans-Jürgen Engelbrecht

Massey University

August 2005

[email protected]

http://www.massey.ac.nz/~hengelbr/

1 introduction
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).
shareable goods
‘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).
shareable goods ctd
‘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).

2 seti@home
2. [email protected]
  • Prime example of a voluntary non-commercial Internet-based distributed computing project: [email protected]
  • Launched in May 1999.
  • Download screen saver.
  • Analysis of Arecibo radio telescope data.
  • [email protected] the most powerful special purpose supercomputer in the world.
seti@home ctd
[email protected] 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).
seti@home ctd1
[email protected] 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’).
3 explanatory variables
3. Explanatory variables
  • What determines [email protected] 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.
the digital access index dai
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).
the dai ctd
The DAI ctd.
  • Hypothesis:

The DAI is a positive and statistically significant determinant of [email protected] participation and its intensity.

    • This would mean: On average, [email protected] participation and its intensity across countries matches inter-country differences in ICT accessibility.
other explanatory variables
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 [email protected] participation and its intensity.
other explanatory variables ctd
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.
other explanatory variables ctd1
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”.

4 regression analysis
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.
regression results ctd
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 [email protected] participation and its intensity (see R2s).
  • HDI dropped from preferred regressions (DAI and HDI highly correlated).
5 the global seti@home digital divide
5. The global [email protected] 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 [email protected] digital divide!
    • Growth rates for ‘users’ and ‘results’ higher in ”the rest”.
6 concluding comments
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.
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