Entropy and the Predictability of Online Life
Name
Sinatra-2014-Entropy and the Pred.pdf
Size
270.7 KB
Format
Adobe PDF
Checksum (MD5)
1d9b90b865a954fd5b56551bd855d628
Author(s)
Sinatra, Roberta
Szell, Michael
Date Issued
January 2014
Journal
Entropy
Publisher
MDPI AG
Citation
Sinatra, Roberta, and Michael Szell. “Entropy and the Predictability of Online Life.” Entropy 16, no. 1 (January 16, 2014): 543–556.
Version
Final published version
Abstract
Using mobile phone records and information theory measures, our daily lives have been recently shown to follow strict statistical regularities, and our movement patterns are, to a large extent, predictable. Here, we apply entropy and predictability measures to two datasets of the behavioral actions and the mobility of a large number of players in the virtual universe of a massive multiplayer online game. We find that movements in virtual human lives follow the same high levels of predictability as offline mobility, where future movements can, to some extent, be predicted well if the temporal correlations of visited places are accounted for. Time series of behavioral actions show similar high levels of predictability, even when temporal correlations are neglected. Entropy conditional on specific behavioral actions reveals that in terms of predictability, negative behavior has a wider variety than positive actions. The actions that contain the information to best predict an individual’s subsequent action are negative, such as attacks or enemy markings, while the positive actions of friendship marking, trade and communication contain the least amount of predictive information. These observations show that predicting behavioral actions requires less information than predicting the mobility patterns of humans for which the additional knowledge of past visited locations is crucial and that the type and sign of a social relation has an essential impact on the ability to determine future behavior.
MIT Department
Massachusetts Institute of Technology. Department of Urban Studies and Planning
Massachusetts Institute of Technology. SENSEable City Laboratory
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.3390/e16010543
