|A bounded confidence approach to understanding user participation in peer production systems|
|Published in||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Keyword(s)||Unknown (Extra: Bimodal distribution, Commons-based peer production, Dynamic population, Global sensitivity analysis, Life span, Online communities, Opinion dynamics, Peer production, Small groups, User participation, Wikipedia, Information science, Sensitivity analysis, Online systems)|
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A bounded confidence approach to understanding user participation in peer production systems is a 2011 conference paper written in English by Ciampaglia G.L. and published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
Commons-based peer production does seem to rest upon a paradox. Although users produce all contents, at the same time participation is commonly on a voluntary basis, and largely incentivized by achievement of project's goals. This means that users have to coordinate their actions and goals, in order to keep themselves from leaving. While this situation is easily explainable for small groups of highly committed, like-minded individuals, little is known about large-scale, heterogeneous projects, such as Wikipedia. In this contribution we present a model of peer production in a large online community. The model features a dynamic population of bounded confidence users, and an endogenous process of user departure. Using global sensitivity analysis, we identify the most important parameters affecting the lifespan of user participation. We find that the model presents two distinct regimes, and that the shift between them is governed by the bounded confidence parameter. For low values of this parameter, users depart almost immediately. For high values, however, the model produces a bimodal distribution of user lifespan. These results suggest that user participation to online communities could be explained in terms of group consensus, and provide a novel connection between models of opinion dynamics and commons-based peer production.
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