Sampling bias in systems with structural heterogeneity and limited internal diffusion
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...
| Main Authors: | , , , , |
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| Format: | Journal article |
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2009
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| _version_ | 1797094282056695808 |
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| author | Onnela, J Johnson, N Gourley, S Reinert, G Spagat, M |
| author_facet | Onnela, J Johnson, N Gourley, S Reinert, G Spagat, M |
| author_sort | Onnela, J |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-03-07T04:11:55Z |
| format | Journal article |
| id | oxford-uuid:c81b7b36-f820-461a-be89-2b8e2e6dae8b |
| institution | University of Oxford |
| last_indexed | 2024-03-07T04:11:55Z |
| publishDate | 2009 |
| record_format | dspace |
| spelling | oxford-uuid:c81b7b36-f820-461a-be89-2b8e2e6dae8b2022-03-27T06:49:53ZSampling bias in systems with structural heterogeneity and limited internal diffusionJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:c81b7b36-f820-461a-be89-2b8e2e6dae8bSaïd Business School - Eureka2009Onnela, JJohnson, NGourley, SReinert, GSpagat, MComplex-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. |
| spellingShingle | Onnela, J Johnson, N Gourley, S Reinert, G Spagat, M Sampling bias in systems with structural heterogeneity and limited internal diffusion |
| title | Sampling bias in systems with structural heterogeneity and limited internal diffusion |
| title_full | Sampling bias in systems with structural heterogeneity and limited internal diffusion |
| title_fullStr | Sampling bias in systems with structural heterogeneity and limited internal diffusion |
| title_full_unstemmed | Sampling bias in systems with structural heterogeneity and limited internal diffusion |
| title_short | Sampling bias in systems with structural heterogeneity and limited internal diffusion |
| title_sort | sampling bias in systems with structural heterogeneity and limited internal diffusion |
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