Law of corresponding states for open collaborations

Abstract

We study the relation between number of contributors and product size in Wikipedia and GitHub. In contrast to traditional production, this is strongly probabilistic, but is characterized by two quantitative nonlinear laws: a power-law bound to product size for increasing number of contributors, and the universal collapse of rescaled distributions. A variant of the random-energy model shows that both laws are due to the heterogeneity of contributors, and displays an intriguing finite-size scaling property with no equivalent in standard systems. The analysis uncovers the right intensive densities, enabling the comparison of projects with different numbers of contributors on equal grounds. We use this property to expose the detrimental effects of conflicting interactions in Wikipedia.

Strong disorder leads to scale invariance in complex biological systems

“…Extreme Value Theory (EVT) has already been used in many interesting contexts (e.g. [7][8][9][10][11][12]) and our results suggest that this approach may be a powerful tool for understanding dynamics in the cell.…”

Relating group size and posting activity of an online community of financial investors: Regularities and seasonal patterns

“…More importantly, while our work focused on a macro-level relation, clarifying the nature of the observed phenomenon would require a micro-level analysis. In this regard, it is worth noting that an interesting micro-level analysis has been provided by [10] to explain similar macro-level power relations found in other online platforms. In particular, [10] found a scaling law for the maximum number of contributors to a project of a given size both in Wikipedia and Github.…”

“…In this regard, it is worth noting that an interesting micro-level analysis has been provided by [10] to explain similar macro-level power relations found in other online platforms. In particular, [10] found a scaling law for the maximum number of contributors to a project of a given size both in Wikipedia and Github. Very interestingly, their explanation was based only on the high heterogeneity in contributors' activity and did not consider agent interactions.…”

“…In managerial and organizational settings these issues are key for the maximization of value creation [6]. Opinion dynamics, conflicts, consensus formation and, more in general, human interactions in collaborative environments have also been studied in statistical physics and network science [7][8][9][10][11][12].…”