Exploring Limits to Prediction in Complex Social Systems Travis Martin Jake M. Hofman University of Michigan Microsoft Research Dept. of Computer Science 641 6th Ave, Floor 7 Ann Arbor, MI New York, NY
[email protected] [email protected] Amit Sharma Ashton Anderson Duncan J. Watts Microsoft Research Microsoft Research Microsoft Research
[email protected] [email protected] [email protected] ABSTRACT wood awards nights, prediction is of longstanding interest to How predictable is success in complex social systems? In scientists, policy makers, and the general public [48, 39, 56]. spite of a recent profusion of prediction studies that ex- The science of prediction has made enormous progress in ploit online social and information network data, this ques- domains of physical and engineering science for which the tion remains unanswered, in part because it has not been behavior of the corresponding systems can be well approx- adequately specified. In this paper we attempt to clarify imated by relatively simple, deterministic equations of mo- the question by presenting a simple stylized model of suc- tion [58]. More recently, impressive gains have been made cess that attributes prediction error to one of two generic in predicting short-term weather patterns [5], demonstrating sources: insufficiency of available data and/or models on the that under some conditions and with appropriate e↵ort use- one hand; and inherent unpredictability of complex social ful predictions can be obtained even for extremely complex systems on the other. We then use this model to motivate and stochastic systems. an illustrative empirical study of information cascade size In light of this history it is only natural to suspect that prediction on Twitter.