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arXiv · 0807.4420

Sampling bias in systems with structural heterogeneity and limited internal diffusion

Jukka-Pekka Onnela·Neil F. Johnson·Sean Gourley·Gesine Reinert·Michael Spagat
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Abstract

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.

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Jukka-Pekka Onnela, Neil F. Johnson, Sean Gourley, Gesine Reinert, Michael Spagat. 2008-07-28. Sampling bias in systems with structural heterogeneity and limited internal diffusion. https://doi.org/10.1209/0295-5075/85/28001

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