IDEAS home Printed from https://ideas.repec.org/a/taf/jecmet/v24y2017i4p362-383.html
   My bibliography  Save this article

Agent-based modelling as a foundation for big data

Author

Listed:
  • Shu-Heng Chen
  • Ragupathy Venkatachalam

Abstract

In this article, we propose a process-based definition of big data, as opposed to the size- and technology-based definitions. We argue that big data should be perceived as a continuous, unstructured and unprocessed dynamics of primitives, rather than as points (snapshots) or summaries (aggregates) of an underlying phenomenon. Given this, we show that big data can be generated through agent-based models but not by equation-based models. Though statistical and machine learning tools can be used to analyse big data, they do not constitute a big data-generation mechanism. Furthermore, agent-based models can aid in evaluating the quality (interpreted as information aggregation efficiency) of big data. Based on this, we argue that agent-based modelling can serve as a possible foundation for big data. We substantiate this interpretation through some pioneering studies from the 1980s on swarm intelligence and several prototypical agent-based models developed around the 2000s.

Suggested Citation

  • Shu-Heng Chen & Ragupathy Venkatachalam, 2017. "Agent-based modelling as a foundation for big data," Journal of Economic Methodology, Taylor & Francis Journals, vol. 24(4), pages 362-383, October.
    • Handle: RePEc:taf:jecmet:v:24:y:2017:i:4:p:362-383
    DOI: 10.1080/1350178X.2017.1388964
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/1350178X.2017.1388964
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/1350178X.2017.1388964?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Claudius Gräbner & Philipp Heimberger & Jakob Kapeller & Bernhard Schütz, 2020. "Structural change in times of increasing openness: assessing path dependency in European economic integration," Journal of Evolutionary Economics, Springer, vol. 30(5), pages 1467-1495, November.
    2. Li, Jiang-Cheng & Tao, Chen & Li, Hai-Feng, 2022. "Dynamic forecasting performance and liquidity evaluation of financial market by Econophysics and Bayesian methods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 588(C).
    3. Zhou, Wei & Zhong, Guang-Yan & Li, Jiang-Cheng, 2022. "Stability of financial market driven by information delay and liquidity in delay agent-based model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    4. Xiong Xiong & Yian Cui & Xiaocong Yan & Jun Liu & Shaoyi He, 2020. "Cost-benefit analysis of trading strategies in the stock index futures market," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-17, December.
    5. Claudius Gräbner & Philipp Heimberger & Jakob Kapeller & Bernhard Schütz, 2018. "Structural Change in Times of Increasing Openness," wiiw Working Papers 143, The Vienna Institute for International Economic Studies, wiiw.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:jecmet:v:24:y:2017:i:4:p:362-383. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RJEC20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.