Sampling bias in systems with structural heterogeneity and limited internal diffusion
Physics and Society
2009-11-13 v3
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.
Cite
@article{arxiv.0807.4420,
title = {Sampling bias in systems with structural heterogeneity and limited internal diffusion},
author = {Jukka-Pekka Onnela and Neil F. Johnson and Sean Gourley and Gesine Reinert and Michael Spagat},
journal= {arXiv preprint arXiv:0807.4420},
year = {2009}
}