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“It Takes All Kinds”: A Simulation Modeling Perspective on Motivation and Coordination in Libre Software Development Projects

Author

Listed:
  • Jean-Michel Dalle

    (Institut pour le Management de la Recherche et de l'Innovation (IMRI))

  • Paul A. David

    (Knowledge Networks and Institutions for Innovation Program, Stanford University)

Abstract

This paper presents a stochastic simulation model to study implications of the mechanisms by which individual software developers’ efforts are allocated within large and complex open source software projects. It illuminates the role of different forms of “motivations-at-the-margin” in the micro-level resource allocation process of distributed and decentralized multi-agent engineering undertakings of this kind. We parameterize the model by isolating the parameter ranges in which it generates structures of code that share certain empirical regularities found to characterize actual projects. We find that, in this range, a variety of different motivations are represented within the community of developers. There is a correspondence between the indicated mixture of motivations and the distribution of avowed motivations for engaging in FLOSS development, found in the survey responses of developers who were participants in large projects.

Suggested Citation

  • Jean-Michel Dalle & Paul A. David, 2007. "“It Takes All Kinds”: A Simulation Modeling Perspective on Motivation and Coordination in Libre Software Development Projects," Discussion Papers 07-024, Stanford Institute for Economic Policy Research.
    • Handle: RePEc:sip:dpaper:07-024
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    References listed on IDEAS

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    More about this item

    Keywords

    free and open source software (FLOSS); libre software engineering; maintainability; reliability; functional diversity; modularity; developers’ motivations; user-innovation; peer-esteem; reputational reward systems; agent-based modeling; stochastic simulation; stigmergy; morphogenesis.;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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