熵与网络生活的可预测性
Entropy and the Predictability of Online Life
摘要：
使用移动电话记录和信息理论测量，我们的日常生活最近被证明遵循严格的统计学规律，我们的运动模式在很大程度上是可预测的。在这里，我们应用熵和可预测性措施的行为动作和大量的玩家在虚拟宇宙中的一个大型多人在线游戏的移动性的两个数据集。我们发现，在虚拟人类生活中的运动遵循同样高水平的可预测性作为离线的流动性，未来的运动可以在一定程度上预测，以及如果访问的地方的时间相关性占。行为动作的时间序列显示出类似的高水平的可预测性，即使时间相关性被忽略。以特定行为为条件的熵揭示了在可预测性方面，消极行为比积极行为有更广泛的多样性。包含最能预测个人后续行动的信息的行动是消极的，如攻击或敌人标记，而友谊标记，贸易和通信的积极行动包含最少的预测信息。这些观察结果表明，预测行为动作需要的信息比预测人类的移动模式少，对于人类的移动模式，过去访问过的位置的额外知识是至关重要的，并且社会关系的类型和标志对确定未来行为的能力具有重要影响。
英文摘要：
Using mobile phone records and information theory measures, our daily lives have been recently shown to follow strict statistical regularities, and our movement patterns are to a large extent predictable. Here, we apply entropy and predictability measures to two data sets of the behavioral actions and the mobility of a large number of players in the virtual universe of a massive multiplayer online game. We find that movements in virtual human lives follow the same high levels of predictability as offline mobility, where future movements can to some extent be predicted well if the temporal correlations of visited places are accounted for. Time series of behavioral actions show similar high levels of predictability, even when temporal correlations are neglected. Entropy conditional on specific behavioral actions reveals that in terms of predictability negative behavior has a wider variety than positive actions. The actions which contain information to best predict an individual's subsequent action are negative, such as attacks or enemy markings, while positive actions of friendship marking, trade and communication contain the least amount of predictive information. These observations show that predicting behavioral actions requires less information than predicting the mobility patterns of humans for which the additional knowledge of past visited locations is crucial, and that the type and sign of a social relation has an essential impact on the ability to determine future behavior.
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。
