Sampling bias in systems with structural heterogeneity and limited internal diffusion

Journal article

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. Copyright © 2009 EPLA.

Authors: Onnela, J; Johnson, N; Gourley, S; Reinert, G; Spagat, M

• Journal: EPL
• Volume: 85
• Issue: 2
• Publication date: 2009-01-01
• DOI: 10.1209/0295-5075/85/28001
• EISSN: 1286-4854
• ISSN: 0295-5075
• Language: English