Media Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Sci Rep. 2013;3:1645. doi: 10.1038/srep01645.
We consider hundreds of thousands of individual economic transactions to ask: how predictable are consumers in their merchant visitation patterns? Our results suggest that, in the long-run, much of our seemingly elective activity is actually highly predictable. Notwithstanding a wide range of individual preferences, shoppers share regularities in how they visit merchant locations over time. Yet while aggregate behavior is largely predictable, the interleaving of shopping events introduces important stochastic elements at short time scales. These short- and long-scale patterns suggest a theoretical upper bound on predictability, and describe the accuracy of a Markov model in predicting a person's next location. We incorporate population-level transition probabilities in the predictive models, and find that in many cases these improve accuracy. While our results point to the elusiveness of precise predictions about where a person will go next, they suggest the existence, at large time-scales, of regularities across the population.
我们考虑了数十万笔的个人交易，以探讨：消费者在其光顾商家的模式上的可预测性如何？我们的研究结果表明，从长期来看，我们的许多看似可选的活动实际上是高度可预测的。尽管存在广泛的个人偏好，但购物者在随时间推移访问商家地点的方式上存在一定的规律。然而，尽管总体行为在很大程度上是可预测的，但购物事件的交织在短时间尺度上引入了重要的随机因素。这些短时间和长时间尺度的模式表明了预测的理论上限，并描述了马尔可夫模型在预测一个人下一个地点的准确性。我们在预测模型中纳入了人口层面的转移概率，并发现，在许多情况下，这些概率会提高准确性。尽管我们的研究结果表明，对一个人接下来会去哪里进行精确预测是难以捉摸的，但它们表明，在较大的时间尺度上，人群中存在一定的规律。
