學術產出-期刊論文
| 題名 | Agent-based modelling as a foundation for big data |
| 作者 | Chen, Shu-Heng 陳樹衡 Venkatachalam, Ragupathy |
| 貢獻者 | 經濟系 |
| 關鍵詞 | Big data; swarm; prediction markets; information aggregation; agent-based models; abduction |
| 日期 | 2017 |
| 上傳時間 | 22-十二月-2018 11:59:22 (UTC+8) |
| 摘要 | 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. |
| 關聯 | JOURNAL OF ECONOMIC METHODOLOGY,24(4), 362-383 |
| 資料類型 | article |
| DOI | http://dx.doi.org/10.1080/1350178X.2017.1388964 |
| dc.contributor | 經濟系 | |
| dc.creator (作者) | Chen, Shu-Heng | |
| dc.creator (作者) | 陳樹衡 | |
| dc.creator (作者) | Venkatachalam, Ragupathy | |
| dc.date (日期) | 2017 | |
| dc.date.accessioned | 22-十二月-2018 11:59:22 (UTC+8) | - |
| dc.date.available | 22-十二月-2018 11:59:22 (UTC+8) | - |
| dc.date.issued (上傳時間) | 22-十二月-2018 11:59:22 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/121480 | - |
| dc.description.abstract (摘要) | 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 (關聯) | JOURNAL OF ECONOMIC METHODOLOGY,24(4), 362-383 | |
| dc.subject (關鍵詞) | Big data; swarm; prediction markets; information aggregation; agent-based models; abduction | en_US |
| dc.title (題名) | Agent-based modelling as a foundation for big data | en_US |
| dc.type (資料類型) | article | |
| dc.identifier.doi (DOI) | 10.1080/1350178X.2017.1388964 | |
| dc.doi.uri (DOI) | http://dx.doi.org/10.1080/1350178X.2017.1388964 |