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摘要翻译：
关于信念形成的研究在群体成员之间的交流是否以及何时会提高数字估计的准确性方面产生了矛盾的发现，如经济预测、医疗诊断和工作候选人评估。虽然一些证据表明，精心中介的过程，如“德尔菲方法”比非结构化讨论产生更准确的信念，但其他证据认为非结构化讨论优于中介过程。还有一些人认为独立的个体产生了最准确的信念。本文展示了信仰形成的网络理论如何解决这些不一致，即使在非正式会话中，群体缺乏明显的结构。突现的影响网络结构与讨论前的信念分布相互作用，调节沟通对信念形成的影响。因此，交流有时会增加有时会降低群体中平均信念的准确性。中介过程和非结构化交流的效果不同，因此每种交流形式的相对利益既取决于群体动力学，也取决于交互前信念的统计特性。这些结果解决了以往研究中的矛盾，并为团队和组织提供了实用的建议。
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英文标题：
《Network Structures of Collective Intelligence: The Contingent Benefits
of Group Discussion》
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作者：
Joshua Becker, Abdullah Almaatouq, Em\H{o}ke-\'Agnes Horv\'at
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最新提交年份：
2021
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分类信息：
一级分类：Economics 经济学
二级分类：General Economics 一般经济学
分类描述：General methodological, applied, and empirical contributions to economics.
对经济学的一般方法、应用和经验贡献。
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一级分类：Computer Science 计算机科学
二级分类：Social and Information Networks 社会和信息网络
分类描述：Covers the design, analysis, and modeling of social and information networks, including their applications for on-line information access, communication, and interaction, and their roles as datasets in the exploration of questions in these and other domains, including connections to the social and biological sciences. Analysis and modeling of such networks includes topics in ACM Subject classes F.2, G.2, G.3, H.2, and I.2; applications in computing include topics in H.3, H.4, and H.5; and applications at the interface of computing and other disciplines include topics in J.1--J.7. Papers on computer communication systems and network protocols (e.g. TCP/IP) are generally a closer fit to the Networking and Internet Architecture (cs.NI) category.
涵盖社会和信息网络的设计、分析和建模，包括它们在联机信息访问、通信和交互方面的应用，以及它们作为数据集在这些领域和其他领域的问题探索中的作用，包括与社会和生物科学的联系。这类网络的分析和建模包括ACM学科类F.2、G.2、G.3、H.2和I.2的主题；计算应用包括H.3、H.4和H.5中的主题；计算和其他学科接口的应用程序包括J.1-J.7中的主题。关于计算机通信系统和网络协议（例如TCP/IP）的论文通常更适合网络和因特网体系结构（CS.NI）类别。
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一级分类：Quantitative Finance 数量金融学
二级分类：Economics 经济学
分类描述：q-fin.EC is an alias for econ.GN. Economics, including micro and macro economics, international economics, theory of the firm, labor economics, and other economic topics outside finance
q-fin.ec是econ.gn的别名。经济学，包括微观和宏观经济学、国际经济学、企业理论、劳动经济学和其他金融以外的经济专题
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英文摘要：
Research on belief formation has produced contradictory findings on whether and when communication between group members will improve the accuracy of numeric estimates such as economic forecasts, medical diagnoses, and job candidate assessments. While some evidence suggests that carefully mediated processes such as the "Delphi method" produce more accurate beliefs than unstructured discussion, others argue that unstructured discussion outperforms mediated processes. Still others argue that independent individuals produce the most accurate beliefs. This paper shows how network theories of belief formation can resolve these inconsistencies, even when groups lack apparent structure as in informal conversation. Emergent network structures of influence interact with the pre-discussion belief distribution to moderate the effect of communication on belief formation. As a result, communication sometimes increases and sometimes decreases the accuracy of the average belief in a group. The effects differ for mediated processes and unstructured communication, such that the relative benefit of each communication format depends on both group dynamics as well as the statistical properties of pre-interaction beliefs. These results resolve contradictions in previous research and offer practical recommendations for teams and organizations.
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PDF链接：
https://arxiv.org/pdf/2009.07202
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