Stop Thinking, Just Do!

Sung-Soo Kim's Blog

Understanding Human Dynamics


25 January 2014

Human Dynamics

Human Dynamics is a transdisciplinary research field focusing on the understanding of dynamic patterns, relationships, narratives, changes, and transitions of human activities, behaviors, and communications. Human dynamics is a branch of complex systems research in statistical physics. Its main goal is to understand human behavior using methods originally developed in statistical physics. Research in this area started to gain momentum in 2005 after the publication of A.-L. Barabási’s seminal paper The origin of bursts and heavy tails in human dynamics.[1] that introduced a queuing model that was alleged to be capable of explaining the long tailed distribution of inter event times that naturally occur in human activity.

This paper spurred a burst of activity in this new area leading to not only further theoretical development of the Barabasi model,[2][3][4] it’s experimental verification in several different activities[5] and the beginning of interest in using proxy tools, such as web server logs.[6][7][8] , cell phone records[9][10] and even the rate at which registration to a major international conference occurs[6] and the distance and rate people around the globe commute from home to work.[11]

In recent years there has been a growing appetite for access to new data sources[12] that might prove useful in quantifying and understanding human behavior on a collective scale.

Human Dynamics Lab, MIT

Modeling human dynamics of face-to-face interaction networks


[1] A.-L. Barabási, (2005). “The origin of bursts and heavy tails in human dynamics.”. Nature 435 (7039): 207–211.
[2] A. Vázquez, (2005). “Exact results for the Barabasi model of human dynamics.”. Physical Review Letters 95 (24): 248701.
[3] A. Vázquez, J. G. Oliveira, Z. Dezsö, K.-I. Goh, I. Kondor & A.-L. Barabási, (2006). “Modeling bursts and heavy tails in human dynamics”. Physical Review E 73: 036127.
[4] Cesar A. Hidalgo, (2006). “Conditions for the emergence of scaling in the inter-event time of uncorrelated and seasonal systems”. Physica A 369: 877–883.
[5] J. G. Oliveira & A.-L. Barabási, (2005). “Human Dynamics: The Correspondence Patterns of Darwin and Einstein.”. Nature 437 (7063): 1251.
[6] to: a b Bruno Goncalves, Jose J. Ramasco, (2008). “Human dynamics revealed through Web analytics”. Physical Review E 78: 026123.
[7] Bruno Goncalves, Jose J. Ramasco, (2009). “Towards the characterization of individual users through Web analytics”. ArXiv. physics.soc-ph: 0901.0498.
[8] Z. Dezsö, E. Almaas, A. Lukács, B. Rácz, I. Szakadát & A.-L. Barabási, (2006). “Dynamics of information access on the web”. Physical Review E 73: 066132.
[9] J.-P. Onnela, J. Saramäki, J. Hyvönen, G. Szabó, D. Lazer, K. Kaski, J. Kertész, and A.-L. Barabási, (2007). “Structure and tie strengths in mobile communication networks”. PNAS 104 (18): 7332–7336.
[10] Jukka-Pekka Onnela, Jari Saramäki, Jörkki Hyvönen, Gábor Szabó, M Argollo de Menezes, Kimmo Kaski, Albert-László Barabási and János Kertèsz, (2007). “Analysis of a large-scale weighted network of one-to-one human communication”. New Journal of Physics Physics 9: 179.
[11] Duygu Balcan, Vittoria Colizza, Bruno Goncalves, Hao Hu, Jose J. Ramasco, Alessandro Vespignani, (2009). “Title: Multiscale mobility networks and the large scale spreading of infectious diseases”. ArXiv. q-bio: 0907.3304.
[12] Marta C. González and Albert-László Barabási, (2007). “Complex networks: From data to models”. Nature Physics 3 (4): 224–225.

comments powered by Disqus