A range of applications, from predicting the spread of human and electronic viruses to city planning and resource management in mobile communications, depend on our ability to foresee the whereabouts and mobility of individuals, raising a fundamental question: To what degree is human behavior predictable? Here we explore the limits of predictability in human dynamics by studying the mobility patterns of anonymized mobile phone users. By measuring the entropy of each individual’s trajectory, we find a 93% potential predictability in user mobility across the whole user base. Despite the significant differences in the travel patterns, we find a remarkable lack of variability in predictability, which is largely independent of the distance users cover on a regular basis.
･･･ despite our deep-rooted desire for change and spontaneity, our daily mobility is, in fact, characterized by a deep-rooted regularity.
Limits of Predictability in Human Mobility
by Chaoming Song, Zehui Qu, Nicholas Blumm and Albert-László Barabási
Science
http://www.barabasilab.com/pubs/CCNR-ALB_Publications/201002-19_Science-Predictability/201002-19_Science-Predictability.pdf
･･･ the combination of the empirically determined user entropy and Fano’s inequality indicates that there is a potential 93% average predictability in user mobility, an exceptionally high value rooted in the inherent regularity of human behavior. Yet it is not the 93% predictability that we find the most surprising. Rather, it is the lack of variability in predictability across the population. Indeed, given the fat-tailed distribution of the distances over which users travel on a regular basis, most individuals are well localized in a finite neighborhood, but a few travel widely. Furthermore, a number of demographic and external parameters, from age to population density and the number of towers visited, vary widely from user to user. It is not unreasonable to expect, therefore, that predictability should also vary widely: For people who travel little, it should be easier to foresee their location, whereas those who regularly cover hundreds of kilometers should have a low predictability. Despite this inherent population heterogeneity, the maximal predictability varies very little—indeed P(Πmax) is narrowly peaked at 93%, and we see no users whose predictability would be under 80%.
Although making explicit predictions on user whereabouts is beyond our goals here, appropriate data-mining algorithms (19, 20, 27) could turn the predictability identified in our study into actual mobility predictions. Most important, our results indicate that when it comes to processes driven by human mobility, from epidemic modeling to urban planning and traffic engineering, the development of accurate predictive models is a scientifically grounded possibility, with potential impact on our well-being and public health. At a more fundamental level, they also indicate that, despite our deep-rooted desire for change and spontaneity, our daily mobility is, in fact, characterized by a deep-rooted regularity.
(sk)
だいたいの人が、家と職場とか、家と学校というように、たった２か所のうちのどちらかにいる。だから、誰かがどこにいるかというようなことは、普段の行動パターンを知っていれば、予測可能だという。
平均で93％の確率で、誰かがいる場所を特定することができるということらしい。もっとも予測しにくい人でさえ、80％の確率で特定できるそうだ。
「私たちの変化や自発性への深い願望にもかかわらず、私たちの毎日の動きは深い規則性に根ざしている (despite our deep-rooted desire for change and spontaneity, our daily mobility is, in fact, characterized by a deep-rooted regularity)」という最後の文章が、とても気になる。
私たちは、それほど自由にはできていない。「不自由が心地よい」という池谷祐二の指摘は、当たっているのかもしれない。