Surges of Collective Human Activity Emerge from Simple Pairwise Correlations
Human populations exhibit complex behaviors—characterized by long-range correlations and surges in activity—across a range of social, political, and technological contexts. Yet it remains unclear where these collective behaviors come from or if there even exists a set of unifying principles. Indeed,...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Physical Society
2019-02-01
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| Series: | Physical Review X |
| Online Access: | http://doi.org/10.1103/PhysRevX.9.011022 |
| _version_ | 1819117522831540224 |
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| author | Christopher W. Lynn Lia Papadopoulos Daniel D. Lee Danielle S. Bassett |
| author_facet | Christopher W. Lynn Lia Papadopoulos Daniel D. Lee Danielle S. Bassett |
| author_sort | Christopher W. Lynn |
| collection | DOAJ |
| description | Human populations exhibit complex behaviors—characterized by long-range correlations and surges in activity—across a range of social, political, and technological contexts. Yet it remains unclear where these collective behaviors come from or if there even exists a set of unifying principles. Indeed, existing explanations typically rely on context-specific mechanisms, such as traffic jams driven by work schedules or spikes in online traffic induced by significant events. However, analogies with statistical mechanics suggest a more general mechanism: that collective patterns can emerge organically from fine-scale interactions within a population. Here, across four different modes of human activity, we show that the simplest correlations in a population—those between pairs of individuals—can yield accurate quantitative predictions for the large-scale behavior of the entire population. To quantify the minimal consequences of pairwise correlations, we employ the principle of maximum entropy, making our description equivalent to an Ising model whose interactions and external fields are notably calculated from past observations of population activity. In addition to providing accurate quantitative predictions, we show that the topology of learned Ising interactions resembles the network of interhuman communication within a population. Together, these results demonstrate that fine-scale correlations can be used to predict large-scale social behaviors, a perspective that has critical implications for modeling and resource allocation in human populations. |
| first_indexed | 2024-12-22T05:34:20Z |
| format | Article |
| id | doaj.art-66b78c172ac743239521e251e3049b25 |
| institution | Directory Open Access Journal |
| issn | 2160-3308 |
| language | English |
| last_indexed | 2024-12-22T05:34:20Z |
| publishDate | 2019-02-01 |
| publisher | American Physical Society |
| record_format | Article |
| series | Physical Review X |
| spelling | doaj.art-66b78c172ac743239521e251e3049b252022-12-21T18:37:21ZengAmerican Physical SocietyPhysical Review X2160-33082019-02-019101102210.1103/PhysRevX.9.011022Surges of Collective Human Activity Emerge from Simple Pairwise CorrelationsChristopher W. LynnLia PapadopoulosDaniel D. LeeDanielle S. BassettHuman populations exhibit complex behaviors—characterized by long-range correlations and surges in activity—across a range of social, political, and technological contexts. Yet it remains unclear where these collective behaviors come from or if there even exists a set of unifying principles. Indeed, existing explanations typically rely on context-specific mechanisms, such as traffic jams driven by work schedules or spikes in online traffic induced by significant events. However, analogies with statistical mechanics suggest a more general mechanism: that collective patterns can emerge organically from fine-scale interactions within a population. Here, across four different modes of human activity, we show that the simplest correlations in a population—those between pairs of individuals—can yield accurate quantitative predictions for the large-scale behavior of the entire population. To quantify the minimal consequences of pairwise correlations, we employ the principle of maximum entropy, making our description equivalent to an Ising model whose interactions and external fields are notably calculated from past observations of population activity. In addition to providing accurate quantitative predictions, we show that the topology of learned Ising interactions resembles the network of interhuman communication within a population. Together, these results demonstrate that fine-scale correlations can be used to predict large-scale social behaviors, a perspective that has critical implications for modeling and resource allocation in human populations.http://doi.org/10.1103/PhysRevX.9.011022 |
| spellingShingle | Christopher W. Lynn Lia Papadopoulos Daniel D. Lee Danielle S. Bassett Surges of Collective Human Activity Emerge from Simple Pairwise Correlations Physical Review X |
| title | Surges of Collective Human Activity Emerge from Simple Pairwise Correlations |
| title_full | Surges of Collective Human Activity Emerge from Simple Pairwise Correlations |
| title_fullStr | Surges of Collective Human Activity Emerge from Simple Pairwise Correlations |
| title_full_unstemmed | Surges of Collective Human Activity Emerge from Simple Pairwise Correlations |
| title_short | Surges of Collective Human Activity Emerge from Simple Pairwise Correlations |
| title_sort | surges of collective human activity emerge from simple pairwise correlations |
| url | http://doi.org/10.1103/PhysRevX.9.011022 |
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