Despite many efforts, the behavior of a crowd is not fully understood. The advent of modern communication means has made it an even more challenging problem, as crowd dynamics could be driven by both human-to-human and human-technology interactions. Here, we study the dynamics of a crowd controlled game (Twitch Plays Pokémon), in which nearly a million players participated during more than two weeks. Unlike other online games, in this event all the players controlled exactly the same character and thus it represents an exceptional example of a collective mind working to achieve a certain goal. We dissect the temporal evolution of the system dynamics along the two distinct phases that characterized the game. We find that having a fraction of players who do not follow the crowd’s average behavior is key to succeed in the game. The latter finding can be well explained by an nth order Markov model that reproduces the observed behavior. Secondly, we analyze a phase of the game in which players were able to decide between two different modes of playing, mimicking a voting system. We show that the introduction of this system clearly polarized the community, splitting it in two. Finally, we discuss one of the peculiarities of these groups in the light of the social identity theory, which appears to describe well some of the observed dynamics.

中文翻译：

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人群控制游戏中集体社会行为的动态

尽管付出了许多努力，人群的行为仍未得到充分理解。现代交流手段的出现使它成为一个更具挑战性的问题，因为人与人之间以及人与技术之间的互动都可能推动人群动态变化。在这里，我们研究了人群控制游戏（Twitch PlaysPokémon）的动态，该游戏在两周多的时间内参与了近100万名玩家。与其他在线游戏不同，在这种情况下，所有玩家都控制完全相同的角色，因此它代表了集体意识为实现某个目标而努力的一个杰出示例。我们剖析了表征游戏的两个不同阶段的系统动力学的时间演变。我们发现，让一部分玩家不遵循人群的平均行为是游戏成功的关键。再现观察到的行为的n阶马尔可夫模型。其次，我们分析了游戏的一个阶段，在该阶段中，玩家能够模仿投票系统，在两种不同的游戏模式之间做出决定。我们表明，该系统的引入明显使社区两极化，将其分为两部分。最后，我们根据社会认同理论讨论了这些群体的特殊性之一，这似乎很好地描述了一些观察到的动态。