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Cited by 2,210Complex Systems and Time Series AnalysisComplex Network Analysis TechniquesHuman Mobility and Location-Based Analysis

The origin of bursts and heavy tails in human dynamics

Open access
Barabási, Albert-László·2005·Nature
DOI 10.1038/nature03459
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Abstract

The dynamics of many social, technological and economic phenomena are driven by individual human actions, turning the quantitative understanding of human behaviour into a central question of modern science. Current models of human dynamics, used from risk assessment to communications, assume that human actions are randomly distributed in time and thus well approximated by Poisson processes. In contrast, there is increasing evidence that the timing of many human activities, ranging from communication to entertainment and work patterns, follow non-Poisson statistics, characterized by bursts of rapidly occurring events separated by long periods of inactivity. Here I show that the bursty nature of human behaviour is a consequence of a decision-based queuing process: when individuals execute tasks based on some perceived priority, the timing of the tasks will be heavy tailed, with most tasks being rapidly executed, whereas a few experience very long waiting times. In contrast, random or priority blind execution is well approximated by uniform inter-event statistics. These finding have important implications, ranging from resource management to service allocation, in both communications and retail.

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  • Aggregate trend claims are hard to identify because adoption coincides with activity bursts that inflate volume comparisons.

    “Aggregate trend claims are hard to identify because adoption coincides with activity bursts [2] that inflate volume comparisons.”

    From The New Shape of Search: How Conversational AI Recomposes Information Seeking (2026) · cites this paper as [2]

  • Whether AI creates or destroys search demand is a counterfactual that observational cross-surface data cannot identify against burst-selected adoption.

    “Whether AI creates or destroys search demand is a counterfactual that observational cross-surface data cannot identify against burst-selected adoption [2]”

    From The New Shape of Search: How Conversational AI Recomposes Information Seeking (2026) · cites this paper as [2]

    Section 1 explains bursty human activity through priority-based task selection, but does not address AI’s effect on search demand. Searches for counterfactual identification, observational cross-surface data, and burst-selected adoption found no passage connecting those ideas or stating that such data cannot identify the effect. (LLM, conf=0.74)

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  • The topology does not identify demand created or destroyed, which an observational panel cannot recover against burst-selection.

    “The topology does not identify demand created or destroyed, which an observational panel cannot recover against burst-selection [2, 11].”

    From The New Shape of Search: How Conversational AI Recomposes Information Seeking (2026) · cites this paper as [2]

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Cites

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Research works printed but not linked: 15

  1. Haight, F.A.,Handbook of the Poisson Distribution (Wiley, New York, 1967).
  2. Reynolds, P.,Call Center Staffing (The Call Center School Press, Lebanon, TN, 2003).
  3. Greene, J.H.,Production and Inventory Control Handbook (McGraw-Hill, New York, 3 ed, 1997).
  4. Anderson, H.R.,Fixed Broadband Wireless System Design (Wiley, New York, 2003).
  5. Dewes, C., Wichmann, A., & Feldman, A., An Analysis of Internet Chat Systems, Proc. 2003 ACM SIGCOMM Conf. on Internet Measurement (IMC-03), ACM Press, New York, 2003).
  6. Kleban, S.D. & Clearwater, S.H., Hierarchical Dynamics, Interarrival Times and Performance, Proc. of SC'03, November 15-21, 2003, Phonenix, AZ, USA.
  7. Paxson, V. & Floyd, S., Wide-Area Traffic: The Failure of Poisson Modeling, IEEE/ACM Tansactions in Networking 3, 226 (1995).
  8. Masoliver,J. , Montero, M. & Weiss, G.H., Continuous-time random-walk model for financial distributions, Phys. Rev. E 67, 021112 (2003).
  9. Eckmann,J-P., Moses, E. & Sergi, D., Entropy of dialogues creates coherent structures in e-mail traffic, Proc. Nat. Acad. Sci. USA 101, 14333-14337 (2004).
  10. Ebel, H., Mielsch,L.-I., & Bornholdt, S., Scale-free topology of e-mail networks, Phys. Rev. E 66, R35103 (2002).
  11. Henderson, T., & Nhatti, S., Modelling user behavior in networked games, Proc. ACM Mul- timedia 2001, Ottawa, Canada, 212-220, 30 September-5 October (2001).
  12. Crovella, M. & Bestavros, A., Self-similarity in World Wide Web traffic: evidence and possible causes, IEEE/ACM Trans. Netw. 5, 835-846 (1997).
  13. Jensen, H.J. Self-Organised Criticality (Cambridge Univerity Press, Cambridge, 1998).
  14. Park, K. & Willinger, W. Self-similar networks traffic and performance evaluation (Wiley, New York, 2000).
  15. Kleinberg, J., Bursty and Hierarchical Structure in Streams, Proc. of the 8th ACM SIGKDD Int'l. Conf. on Knowledge Discovery and Data Mining, 91-101 (2002).

Cited by

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Authors
Barabási, A.-L.
Venue
Nature
Publisher
Springer Science and Business Media LLC
Published
2005-05
Volume
435
Issue
7039
Pages
207-211
ISSN
  • 0028-0836 (ISSN-L)
  • 1476-4687
DOI
10.1038/nature03459
OpenAlex
W1972309850
Cited by
2,210
References
1
Citations by year
2026: 70 · 2025: 60 · 2024: 57 · 2023: 77 · 2022: 102 · 2021: 103 · 2020: 124 · 2019: 116 · 2018: 154 · 2017: 168 · 2016: 155 · 2015: 182 · 2014: 143 · 2013: 179 · 2012: 127
FWCI
100.74
Type
journal article
Language
en
Open access
Licence
www.springer.com/tdm (tdm)
Locations
  • Nature (publishedVersion)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Complex Systems and Time Series Analysis
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
  • Social Sciences › Transportation › Human Mobility and Location-Based Analysis
Keywords
human dynamics, burstiness, heavy-tailed distributions, power law, Poisson process, human behavior, resource management, service allocation
MeSH terms
Behavior, Commerce, Communication, Decision Making, Humans, Human Activities, Models, Biological, Random Allocation, Time Factors, Models, Statistical, Poisson Distribution, Risk Assessment, Internet, Resource Allocation

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