arXiv 1602.010132016-02-02cs.SIphysics.data-anphysics.soc-ph

探索复杂社会系统中的预测极限

Exploring limits to prediction in complex social systems

Travis Martin, Jake M. Hofman, Amit Sharma, Ashton Anderson, Duncan J. Watts

备注：: 12 pages, 6 figures, Proceedings of the 25th ACM International World Wide Web Conference (WWW) 2016
DOI：: 10.1145/2872427.2883001

摘要：

在复杂的社会系统中，成功的可预测性如何？尽管最近有大量利用在线社交和信息网络数据的预测研究，但这个问题仍然没有答案，部分原因是它没有得到充分的说明。在本文中，我们试图澄清这个问题，提出了一个简单的程式化模型的成功，属性预测误差的两个通用来源之一：现有数据和/或模型的不足，一方面和复杂的社会系统的内在不可预测性。然后，我们使用这个模型来激励一个说明性的实证研究的信息级联规模预测Twitter上。尽管有关用户、内容和过去性能的信息量前所未有，但我们的最佳性能模型只能解释级联大小差异的不到一半。反过来，这一结果表明，即使有无限的数据，预测性能也会远远低于确定性精度。最后，我们探索这个潜在的约束理论上使用模拟的扩散过程的随机尺度自由网络类似Twitter。我们表明，虽然更高的预测能力在理论上是可能的，这样的性能需要一个同质的系统和完善的事前知识：即使是在估计产品质量或产品质量的微小变化的不确定性程度很小，导致更严格的限制界限的可预测性。我们的结论是，预测准确性的现实界限与我们根据经验获得的界限没有什么不同，而对于其他复杂的社会系统，这些界限更难获得数据，可能更低。

英文摘要：

How predictable is success in complex social systems? In spite of a recent profusion of prediction studies that exploit online social and information network data, this question remains unanswered, in part because it has not been adequately specified. In this paper we attempt to clarify the question by presenting a simple stylized model of success that attributes prediction error to one of two generic sources: insufficiency of available data and/or models on the one hand; and inherent unpredictability of complex social systems on the other. We then use this model to motivate an illustrative empirical study of information cascade size prediction on Twitter. Despite an unprecedented volume of information about users, content, and past performance, our best performing models can explain less than half of the variance in cascade sizes. In turn, this result suggests that even with unlimited data predictive performance would be bounded well below deterministic accuracy. Finally, we explore this potential bound theoretically using simulations of a diffusion process on a random scale free network similar to Twitter. We show that although higher predictive power is possible in theory, such performance requires a homogeneous system and perfect ex-ante knowledge of it: even a small degree of uncertainty in estimating product quality or slight variation in quality across products leads to substantially more restrictive bounds on predictability. We conclude that realistic bounds on predictive accuracy are not dissimilar from those we have obtained empirically, and that such bounds for other complex social systems for which data is more difficult to obtain are likely even lower.

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。

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