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Agent-based modelling as a foundation for big data

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  • 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
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    Cited by:

    1. 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.
    2. 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.
    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. 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).

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