Sampling bias in systems with structural heterogeneity and limited internal diffusion

J. P. Onnela, N. F. Johnson, S. Gourley, G. Reinert, M. Spagat

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Complex-systems research is becomingly increasingly data-driven, particularly in the social and biological domains. Many of the systems from which sample data are collected feature structural heterogeneity at the mesoscopic scale (i.e. communities) and limited inter-community diffusion. Here we show that the interplay between these two features can yield a significant bias in the global characteristics inferred from the data. We present a general framework to quantify this bias, and derive an explicit corrective factor for a wide class of systems. Applying our analysis to a recent high-profile survey of conflict mortality in Iraq suggests a significant overestimate of deaths.

Original languageEnglish (US)
Article number28001
Issue number2
StatePublished - 2009

ASJC Scopus subject areas

  • Physics and Astronomy(all)


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