Singh Vivek Kumar, Bozkaya Burcin, Pentland Alex
Media Lab, Massachusetts Institute of Technology, 20 Amherst St, Cambridge, Massachusetts, United States of America; School of Communication and Information, Rutgers University, New Brunswick, New Jersey, United States of America.
School of Management, Sabanci University, Tuzla, Istanbul, Turkey.
PLoS One. 2015 Aug 28;10(8):e0136628. doi: 10.1371/journal.pone.0136628. eCollection 2015.
Traditional financial decision systems (e.g. credit) had to rely on explicit individual traits like age, gender, job type, and marital status, while being oblivious to spatio-temporal mobility or the habits of the individual involved. Emerging trends in geo-aware and mobile payment systems, and the resulting "big data," present an opportunity to study human consumption patterns across space and time. Taking inspiration from animal behavior studies that have reported significant interconnections between animal spatio-temporal "foraging" behavior and their life outcomes, we analyzed a corpus of hundreds of thousands of human economic transactions and found that financial outcomes for individuals are intricately linked with their spatio-temporal traits like exploration, engagement, and elasticity. Such features yield models that are 30% to 49% better at predicting future financial difficulties than the comparable demographic models.
传统金融决策系统（如信贷）不得不依赖年龄、性别、工作类型和婚姻状况等明确的个人特征，而忽略了个体的时空流动性或相关习惯。地理感知和移动支付系统的新兴趋势以及由此产生的“大数据”，为研究人类跨时空的消费模式提供了契机。从动物行为研究中获得灵感，这些研究报告了动物时空“觅食”行为与其生活结果之间的显著联系，我们分析了数十万笔人类经济交易的语料库，发现个人的财务结果与其探索、参与和弹性等时空特征有着复杂的联系。与可比的人口统计模型相比，这些特征产生的模型在预测未来财务困难方面要好30%至49%。
