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Agent-Based Modelling as a Foundation for Big Data

Chen, Shu-Heng and Venkatachalam, Ragupathy. 2017. Agent-Based Modelling as a Foundation for Big Data. Journal of Economic Methodology, 24(4), pp. 362-383. ISSN 1350-178X [Article]

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Abstract or Description

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 continu- ous, 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.

Item Type:

Article

Identification Number (DOI):

https://doi.org/10.1080/1350178X.2017.1388964

Keywords:

Big Data, Swarm, Prediction Markets, Information Aggregation, Agent-based Models, Abduction

Departments, Centres and Research Units:

Institute of Management Studies

Dates:

DateEvent
22 August 2017Accepted
24 October 2017Published Online

Item ID:

21750

Date Deposited:

06 Oct 2017 13:09

Last Modified:

31 May 2019 01:26

Peer Reviewed:

Yes, this version has been peer-reviewed.

URI:

http://research.gold.ac.uk/id/eprint/21750

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