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Title: | Agent-based modelling as a foundation for big data |
Authors: | Chen, Shu-Heng 陳樹衡 Venkatachalam, Ragupathy |
Contributors: | 經濟系 |
Keywords: | Big data; swarm; prediction markets; information aggregation; agent-based models; abduction |
Date: | 2017 |
Issue Date: | 2018-12-22 11:59:22 (UTC+8) |
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 equationbased models. Though statistical and machine learning tools can be used to analyse big data, they do not constitute a big data-generation mechanism. Furthermore, agentbased 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 agentbased models developed around the 2000s. |
Relation: | JOURNAL OF ECONOMIC METHODOLOGY,24(4), 362-383 |
Data Type: | article |
DOI: | http://dx.doi.org/10.1080/1350178X.2017.1388964 |
DCField |
Value |
Language |
dc.contributor (Contributor) | 經濟系 | |
dc.creator (Authors) | Chen, Shu-Heng | |
dc.creator (Authors) | 陳樹衡 | |
dc.creator (Authors) | Venkatachalam, Ragupathy | |
dc.date (Date) | 2017 | |
dc.date.accessioned | 2018-12-22 11:59:22 (UTC+8) | - |
dc.date.available | 2018-12-22 11:59:22 (UTC+8) | - |
dc.date.issued (Issue Date) | 2018-12-22 11:59:22 (UTC+8) | - |
dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/121480 | - |
dc.description.abstract (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 equationbased models. Though statistical and machine learning tools can be used to analyse big data, they do not constitute a big data-generation mechanism. Furthermore, agentbased 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 agentbased models developed around the 2000s. | en_US |
dc.format.extent | 567720 bytes | - |
dc.format.mimetype | application/pdf | - |
dc.relation (Relation) | JOURNAL OF ECONOMIC METHODOLOGY,24(4), 362-383 | |
dc.subject (Keywords) | Big data; swarm; prediction markets; information aggregation; agent-based models; abduction | en_US |
dc.title (Title) | Agent-based modelling as a foundation for big data | en_US |
dc.type (Data Type) | article | |
dc.identifier.doi (DOI) | 10.1080/1350178X.2017.1388964 | |
dc.doi.uri | http://dx.doi.org/10.1080/1350178X.2017.1388964 | |