集体智慧：在猜谜游戏中聚集邻居的信息
Collective intelligence: aggregation of information from neighbors in a guessing game
摘要：
复杂系统表现出聚集信息和显示协调活动的能力。在社会系统中，不同个体的相互作用导致规范的出现，政治立场，观点，文化特征甚至科学进步的趋势。集体行为的例子可以在维基百科和Linux等活动中观察到，在这些活动中，个人为了社区的利益而聚集他们的知识，而公民科学则利用集体解决复杂问题的潜力。在这里，我们进行了一个在线实验，以调查的表现集体解决猜测问题，其中每个演员被赋予部分信息，并放置在一个互动网络的节点。我们测量的准确性的时间演变方面的集体的性能，发现两类网络，规则的格子和随机网络的性能没有统计差异。我们还确定，贝叶斯描述捕获的行为模式的个人遵循聚合信息从邻居做出决定。与其他简单的决策模型相比，玩家遵循的策略揭示了集体的次优表现。我们的贡献提供了基于个人的描述和集体现象之间的微观宏观联系的基础。
英文摘要：
Complex systems show the capacity to aggregate information and to display coordinated activity. In the case of social systems the interaction of different individuals leads to the emergence of norms, trends in political positions, opinions, cultural traits, and even scientific progress. Examples of collective behavior can be observed in activities like the Wikipedia and Linux, where individuals aggregate their knowledge for the benefit of the community, and citizen science, where the potential of collectives to solve complex problems is exploited. Here, we conducted an online experiment to investigate the performance of a collective when solving a guessing problem in which each actor is endowed with partial information and placed as the nodes of an interaction network. We measure the performance of the collective in terms of the temporal evolution of the accuracy, finding no statistical difference in the performance for two classes of networks, regular lattices and random networks. We also determine that a Bayesian description captures the behavior pattern the individuals follow in aggregating information from neighbors to make decisions. In comparison with other simple decision models, the strategy followed by the players reveals a suboptimal performance of the collective. Our contribution provides the basis for the micro-macro connection between individual based descriptions and collective phenomena.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
