Various studies have investigated the predictability of different aspects of human behavior such as mobility patterns, social interactions, and shopping and online behaviors. However, the existing researches have been often limited to a single or to the combination of few behavioral dimensions, and they have adopted the perspective of an outside observer who is unaware of the motivations behind the specific behaviors or activities of a given individual. The key assumption of this work is that human behavior is deliberated based on an individual’s own perception of the situation that s/he is in, and that therefore it should also be studied under the same perspective. Taking inspiration from works in ubiquitous and context-aware computing, we investigate the role played by four contextual dimensions (or modalities), namely time, location, activity being carried out, and social ties, on the predictability of individuals’ behaviors, using a month of collected mobile phone sensor readings and self-reported annotations about these contextual modalities from more than two hundred study participants. Our analysis shows that any target modality (e.g. location) becomes substantially more predictable when information about the other modalities (time, activity, social ties) is made available. Multi-modality turns out to be in some sense fundamental, as some values (e.g. specific activities like “shopping”) are nearly impossible to guess correctly unless the other modalities are known. Subjectivity also has a substantial impact on predictability. A location recognition experiment suggests that subjective location annotations convey more information about activity and social ties than objective information derived from GPS measurements. We conclude the paper by analyzing how the identified contextual modalities allow to compute the diversity of personal behavior, where we show that individuals are more easily identified by rarer, rather than frequent, context annotations. These results offer support in favor of developing innovative computational models of human behaviors enriched by a characterization of the context of a given behavior.

各种研究调查了人类行为不同方面的可预测性，例如移动模式、社交互动以及购物和在线行为。然而，现有的研究往往局限于单个或几个行为维度的组合，他们采用了外部观察者的视角，他们不知道特定个人特定行为或活动背后的动机。这项工作的关键假设是人类行为是根据个人对他/她所处情况的自己的看法来考虑的，因此也应该在相同的视角下进行研究。从无处不在的上下文感知计算工作中汲取灵感，我们研究了四个上下文维度（或模态）所扮演的角色，即时间、位置、正在开展的活动和社会联系，关于个人行为的可预测性，使用一个月收集的手机传感器读数和来自 200 多名研究参与者的关于这些上下文模式的自我报告的注释。我们的分析表明，当有关其他模态（时间、活动、社交关系）的信息可用时，任何目标模态（例如位置）都变得更加可预测。多模态在某种意义上是基本的，因为除非已知其他模态，否则几乎不可能正确猜测某些值（例如“购物”等特定活动）。主观性对可预测性也有重大影响。位置识别实验表明，主观位置注释比从 GPS 测量得出的客观信息传达了更多关于活动和社会关系的信息。我们通过分析识别的上下文模式如何允许计算个人行为的多样性来结束本文，我们表明个人更容易被稀有而不是频繁的上下文注释识别。这些结果为开发创新的人类行为计算模型提供了支持，该模型通过给定行为的上下文特征来丰富。而不是频繁的上下文注释。这些结果为开发创新的人类行为计算模型提供了支持，该模型通过给定行为的上下文特征来丰富。而不是频繁的上下文注释。这些结果为开发创新的人类行为计算模型提供了支持，该模型通过给定行为的上下文特征来丰富。