利用群人工智能提高预测精度

Louis B. Rosenberg, N. Pescetelli
{"title":"利用群人工智能提高预测精度","authors":"Louis B. Rosenberg, N. Pescetelli","doi":"10.1109/INTELLISYS.2017.8324329","DOIUrl":null,"url":null,"abstract":"In the natural world, many species amplify the accuracy of their decision-making abilities by working together real-time closed-loop systems that converge on optimal solutions in synchrony. Known as Swarm Intelligence (SI), the process has been deeply studied in schools of fish, flocks of birds, and swarms of bees. The present study looks at the ability of human groups to make decisions as an Artificial Swarm Intelligence (ASI) by forming similar real-time closed-loop systems online. More specifically, the present study tasked groups of typical sports fans with predicting English Premier League matches over a period of five consecutive weeks by working together in real-time as swarm-based systems. Results showed that individuals, who averaged 55% accuracy when predicting games alone, were able to amplify their accuracy to 72% when predicting together as real-time swarms. This corresponds to 131% amplification in predictive accuracy across five consecutive weeks (50 games).","PeriodicalId":131825,"journal":{"name":"2017 Intelligent Systems Conference (IntelliSys)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":"{\"title\":\"Amplifying prediction accuracy using Swarm A.I.\",\"authors\":\"Louis B. Rosenberg, N. Pescetelli\",\"doi\":\"10.1109/INTELLISYS.2017.8324329\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the natural world, many species amplify the accuracy of their decision-making abilities by working together real-time closed-loop systems that converge on optimal solutions in synchrony. Known as Swarm Intelligence (SI), the process has been deeply studied in schools of fish, flocks of birds, and swarms of bees. The present study looks at the ability of human groups to make decisions as an Artificial Swarm Intelligence (ASI) by forming similar real-time closed-loop systems online. More specifically, the present study tasked groups of typical sports fans with predicting English Premier League matches over a period of five consecutive weeks by working together in real-time as swarm-based systems. Results showed that individuals, who averaged 55% accuracy when predicting games alone, were able to amplify their accuracy to 72% when predicting together as real-time swarms. This corresponds to 131% amplification in predictive accuracy across five consecutive weeks (50 games).\",\"PeriodicalId\":131825,\"journal\":{\"name\":\"2017 Intelligent Systems Conference (IntelliSys)\",\"volume\":\"58 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"15\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 Intelligent Systems Conference (IntelliSys)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/INTELLISYS.2017.8324329\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 Intelligent Systems Conference (IntelliSys)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INTELLISYS.2017.8324329","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 15

摘要

在自然界中,许多物种通过实时闭环系统协同工作来提高决策能力的准确性,这些系统同步收敛于最佳解决方案。这个过程被称为群体智能(SI),已经在鱼群、鸟群和蜂群中得到了深入的研究。目前的研究着眼于人类群体通过在线形成类似的实时闭环系统来作为人工群体智能(ASI)做出决策的能力。更具体地说,目前的研究要求一组典型的体育迷在连续五周的时间里,通过基于群体的系统实时合作,预测英超联赛的比赛。结果显示,个体在单独预测游戏时的平均准确率为55%,而当他们作为实时群体一起预测时,他们的准确率可以提高到72%。这相当于连续5周(50场游戏)的预测准确率提高了131%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Amplifying prediction accuracy using Swarm A.I.
In the natural world, many species amplify the accuracy of their decision-making abilities by working together real-time closed-loop systems that converge on optimal solutions in synchrony. Known as Swarm Intelligence (SI), the process has been deeply studied in schools of fish, flocks of birds, and swarms of bees. The present study looks at the ability of human groups to make decisions as an Artificial Swarm Intelligence (ASI) by forming similar real-time closed-loop systems online. More specifically, the present study tasked groups of typical sports fans with predicting English Premier League matches over a period of five consecutive weeks by working together in real-time as swarm-based systems. Results showed that individuals, who averaged 55% accuracy when predicting games alone, were able to amplify their accuracy to 72% when predicting together as real-time swarms. This corresponds to 131% amplification in predictive accuracy across five consecutive weeks (50 games).
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信