论文标题
共享完整与平均社会信息对社会影响和估计准确性的影响
Impact of sharing full versus averaged social information on social influence and estimation accuracy
论文作者
论文摘要
社交网络和推荐系统的最新发展大大增加了人类社区中共有的社会信息量，从而挑战了人类对其进行处理的能力。结果，共享汇总的社会信息形式变得越来越流行。但是，尚不清楚共享汇总信息是否可以改善人们的判断，而不是共享全部可用信息。在这里，我们比较当完全共享社交信息与首先平均然后共享的社交信息时，在估计任务中的性能进行比较。我们发现，在这两种情况下，估计准确性的提高都是可比的。但是，我们的结果揭示了受试者行为的重要差异：（i）受试者在获得平均水平时要比收到所有估计值更多地遵循社会信息，而这种影响随着平均水平的估计数量而增加； （ii）当受试者比其个人估计高于较低时的个人估计时，受试者更遵循社会信息。在收到所有估计的情况下，这种效果比获得平均水平时要强。我们介绍了一个阐明这些影响的模型，并确认它们对于解释所有处理中估计准确性的提高的重要性。
The recent developments of social networks and recommender systems have dramatically increased the amount of social information shared in human communities, challenging the human ability to process it. As a result, sharing aggregated forms of social information is becoming increasingly popular. However, it is unknown whether sharing aggregated information improves people's judgments more than sharing the full available information. Here, we compare the performance of groups in estimation tasks when social information is fully shared versus when it is first averaged and then shared. We find that improvements in estimation accuracy are comparable in both cases. However, our results reveal important differences in subjects' behaviour: (i) subjects follow the social information more when receiving an average than when receiving all estimates, and this effect increases with the number of estimates underlying the average; (ii) subjects follow the social information more when it is higher than their personal estimate than when it is lower. This effect is stronger when receiving all estimates than when receiving an average. We introduce a model that sheds light on these effects, and confirms their importance for explaining improvements in estimation accuracy in all treatments.
