论文标题
线程的智慧和说服力
The Wisdom and Persuadability of Threads
论文作者
论文摘要
在线讨论线程是个人决策和集体判断的重要手段，例如“人群的智慧”。目前，人们对人群的智慧的实证研究对社会信息所扮演的角色持矛盾态度。尽管一些发现表明，由于相关的判断错误，社会信息破坏了人群的准确性，但其他发现则表明准确性有所提高。我们通过实验研究线程的准确性，其中参与者对难度的幅度估计值进行了不同，同时看到了不同数量的先前估计值。我们证明，对于艰巨的任务，看到先前的估计有助于人群的智慧。但是，如果参与者只看到极端的估计，那么智慧很快就会变成愚蠢。使用高斯混合模型，我们为每个参与者分配了有说服力的得分，并表明说服性随着任务难度和提供的社会信息数量而增加。在过滤的线程中，我们看到高度说服的参与者和怀疑论者之间存在越来越多的差距。
Online discussion threads are important means for individual decision-making and for aggregating collective judgments, e.g. the `wisdom of crowds'. Empirical investigations of the wisdom of crowds are currently ambivalent about the role played by social information. While some findings suggest that social information undermines crowd accuracy due to correlated judgment errors, others show that accuracy improves. We investigate experimentally the accuracy of threads in which participants make magnitude estimates of varying difficulty while seeing a varying number of previous estimates. We demonstrate that, for difficult tasks, seeing preceding estimates aids the wisdom of crowds. If, however, participants only see extreme estimates, wisdom quickly turns into folly. Using a Gaussian Mixture Model, we assign a persuadability score to each participant and show that persuadability increases with task difficulty and with the amount of social information provided. In filtered threads, we see an increasing gap between highly persuadable participants and skeptics.
