在预测美国国家橄榄球联盟(NFL)的比赛时,新体育迷的“人类群”击败了职业高尔夫球手

Hans Schumann, Louis B. Rosenberg, G. Willcox
{"title":"在预测美国国家橄榄球联盟(NFL)的比赛时,新体育迷的“人类群”击败了职业高尔夫球手","authors":"Hans Schumann, Louis B. Rosenberg, G. Willcox","doi":"10.54941/ahfe1003287","DOIUrl":null,"url":null,"abstract":"The biological phenomenon of Swarm Intelligence (SI) enables social species to converge on group decisions by interacting in real-time systems. Studied in schools of fish, bee swarms, and bird flocks, biologists have shown for decades that SI can greatly amplify group intelligence in natural systems. Artificial Swarm Intelligence (ASI) is a computer-mediated technique developed in 2015 to enable networked human groups to form real-time systems that can deliberate and converge on decisions, predictions, estimations, and prioritizations. A unique combination of real-time HCI methods and AI algorithms, ASI technology (also called “Human Swarming” or “Swarm AI”) has been shown in many studies to amplify group intelligence in forecasting tasks, often enabling small groups of non-professionals to exceed expert level performance. In the current study, small groups of approximately 24 amateur sports fans used an online platform called Swarm to collaboratively make weekly predictions (against the spread) of every football game in four consecutive NFL seasons (2019 - 2022) for a total of 1027 forecasted games. Approximately 5 games per week (as forecast by the human swarm) were identified as “predictable” using statistical heuristics. Performance was compared against the Vegas betting markets and measured against accepted performance benchmarks for professional handicappers. It is well known that professional bettors rarely achieve more than 55% accuracy against the Vegas spread and that top experts in the world rarely exceed 58% accuracy. In this study the amateur sports fans achieved 62.5% accuracy against the spread when connected as real-time “swarms.” A statistical analysis of this result (across 4 NFL seasons) found that swarms outperformed the 55% accuracy benchmark for human experts with significance (p=0.002). These results confirmed for the first time that groups of amateurs, when connected in real-time using ASI, can consistently generate forecasts that exceeded expert level performance with a high degree of statistical certainty.Keywords: Swarm Intelligence, Artificial Swarm Intelligence, Collective Intelligence, Wisdom of Crowds, Hyperswarms,","PeriodicalId":405313,"journal":{"name":"Artificial Intelligence and Social Computing","volume":"55 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"'\\\"Human Swarms” of novice sports fans beat professional handicappers when forecasting NFL football games\",\"authors\":\"Hans Schumann, Louis B. Rosenberg, G. Willcox\",\"doi\":\"10.54941/ahfe1003287\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The biological phenomenon of Swarm Intelligence (SI) enables social species to converge on group decisions by interacting in real-time systems. Studied in schools of fish, bee swarms, and bird flocks, biologists have shown for decades that SI can greatly amplify group intelligence in natural systems. Artificial Swarm Intelligence (ASI) is a computer-mediated technique developed in 2015 to enable networked human groups to form real-time systems that can deliberate and converge on decisions, predictions, estimations, and prioritizations. A unique combination of real-time HCI methods and AI algorithms, ASI technology (also called “Human Swarming” or “Swarm AI”) has been shown in many studies to amplify group intelligence in forecasting tasks, often enabling small groups of non-professionals to exceed expert level performance. In the current study, small groups of approximately 24 amateur sports fans used an online platform called Swarm to collaboratively make weekly predictions (against the spread) of every football game in four consecutive NFL seasons (2019 - 2022) for a total of 1027 forecasted games. Approximately 5 games per week (as forecast by the human swarm) were identified as “predictable” using statistical heuristics. Performance was compared against the Vegas betting markets and measured against accepted performance benchmarks for professional handicappers. It is well known that professional bettors rarely achieve more than 55% accuracy against the Vegas spread and that top experts in the world rarely exceed 58% accuracy. In this study the amateur sports fans achieved 62.5% accuracy against the spread when connected as real-time “swarms.” A statistical analysis of this result (across 4 NFL seasons) found that swarms outperformed the 55% accuracy benchmark for human experts with significance (p=0.002). These results confirmed for the first time that groups of amateurs, when connected in real-time using ASI, can consistently generate forecasts that exceeded expert level performance with a high degree of statistical certainty.Keywords: Swarm Intelligence, Artificial Swarm Intelligence, Collective Intelligence, Wisdom of Crowds, Hyperswarms,\",\"PeriodicalId\":405313,\"journal\":{\"name\":\"Artificial Intelligence and Social Computing\",\"volume\":\"55 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Artificial Intelligence and Social Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.54941/ahfe1003287\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Artificial Intelligence and Social Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.54941/ahfe1003287","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

群体智能(SI)的生物现象使社会物种能够通过实时系统中的相互作用而收敛于群体决策。生物学家对鱼群、蜂群和鸟群进行了数十年的研究,证明SI可以极大地增强自然系统中的群体智能。人工群体智能(ASI)是2015年开发的一种计算机中介技术,它使网络化的人类群体能够形成实时系统,可以在决策、预测、估计和优先级方面进行深思熟虑和融合。实时HCI方法和人工智能算法的独特组合,ASI技术(也称为“人类蜂群”或“蜂群人工智能”)已在许多研究中被证明可以在预测任务中增强群体智能,通常使非专业人员的小团体超越专家水平的表现。在目前的研究中,大约24名业余体育迷组成的小组使用一个名为Swarm的在线平台,在连续四个NFL赛季(2019 - 2022)中协作每周预测每场足球比赛(针对传播),总共预测了1027场比赛。每周大约有5场游戏(根据人类群体的预测)通过统计启发式被确定为“可预测的”。他们的表现与拉斯维加斯博彩市场进行了比较,并与公认的职业高尔夫球手的表现基准进行了衡量。众所周知,专业投注者对赌城的赔率很少能超过55%,而世界顶级专家的赔率也很少能超过58%。在这项研究中,业余体育迷在实时“群体”连接时,对传播的准确率达到了62.5%。对这一结果(跨越4个NFL赛季)的统计分析发现,对于人类专家来说,群体的准确率超过了55%的基准,具有显著性(p=0.002)。这些结果首次证实,当使用ASI进行实时连接时,业余群体可以始终如一地产生超过专家水平的预测,并且具有高度的统计确定性。关键词:群体智能;人工群体智能;集体智能;
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
'"Human Swarms” of novice sports fans beat professional handicappers when forecasting NFL football games
The biological phenomenon of Swarm Intelligence (SI) enables social species to converge on group decisions by interacting in real-time systems. Studied in schools of fish, bee swarms, and bird flocks, biologists have shown for decades that SI can greatly amplify group intelligence in natural systems. Artificial Swarm Intelligence (ASI) is a computer-mediated technique developed in 2015 to enable networked human groups to form real-time systems that can deliberate and converge on decisions, predictions, estimations, and prioritizations. A unique combination of real-time HCI methods and AI algorithms, ASI technology (also called “Human Swarming” or “Swarm AI”) has been shown in many studies to amplify group intelligence in forecasting tasks, often enabling small groups of non-professionals to exceed expert level performance. In the current study, small groups of approximately 24 amateur sports fans used an online platform called Swarm to collaboratively make weekly predictions (against the spread) of every football game in four consecutive NFL seasons (2019 - 2022) for a total of 1027 forecasted games. Approximately 5 games per week (as forecast by the human swarm) were identified as “predictable” using statistical heuristics. Performance was compared against the Vegas betting markets and measured against accepted performance benchmarks for professional handicappers. It is well known that professional bettors rarely achieve more than 55% accuracy against the Vegas spread and that top experts in the world rarely exceed 58% accuracy. In this study the amateur sports fans achieved 62.5% accuracy against the spread when connected as real-time “swarms.” A statistical analysis of this result (across 4 NFL seasons) found that swarms outperformed the 55% accuracy benchmark for human experts with significance (p=0.002). These results confirmed for the first time that groups of amateurs, when connected in real-time using ASI, can consistently generate forecasts that exceeded expert level performance with a high degree of statistical certainty.Keywords: Swarm Intelligence, Artificial Swarm Intelligence, Collective Intelligence, Wisdom of Crowds, Hyperswarms,
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
期刊最新文献
Hepatitis predictive analysis model through deep learning using neural networks based on patient history A machine learning approach for optimizing waiting times in a hand surgery operation center Automated Decision Support for Collaborative, Interactive Classification Dynamically monitoring crowd-worker's reliability with interval-valued labels Detection of inappropriate images on smartphones based on computer vision techniques
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1