Human dynamics

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Human dynamics can refer to a branch of complex systems research in statistical physics or to a way of understanding and describing how people process information. Human Dynamics as Personality Dynamics: refers to a body of work that identifies fundamental distinctions in the way people naturally process derived from more than thirty-four years of original, ongoing research begun in 1979 by Dr. Sandra Seagal and her associates. Early research into these fundamental distinctions in people emerged as result of a discovery related to the human voice; namely three frequencies that corresponded to a high, middle and low frequency. These three frequencies-- the mental (objective), emotional (relational), and physical (practical), capacities of a person are termed, principles. Each personality dynamic is characterized by fundamentally different inner processes in the way they inherently learn, assimilate information, relate, communicate, approach tasks, problem solve, contribute to others, respond to stress and trauma, and maintain health and wellness.

An individual's personality dynamic remains constant throughout his or her life span, and each personality dynamic has unique requirements for personal growth and development. Of great significance is the fact that the personality dynamics appear to be so foundational they can be seen the world over, identified in babies as young as six months, and exist independent of age, culture, race or gender.

It is important to note that each personality dynamic is of equal value and every personality dynamic has an unbounded capacity for growth. However, the way in which the members of each personality dynamic function is completely different.

Human Dynamics as a branch of 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] its 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.


  1. ^ A.-L. Barabási, (2005). "The origin of bursts and heavy tails in human dynamics.". Nature 435 (7039): 207–211. arXiv:cond-mat/0505371. Bibcode:2005Natur.435..207B. doi:10.1038/nature03459. PMID 15889093. 
  2. ^ A. Vázquez, (2005). "Exact results for the Barabasi model of human dynamics.". Physical Review Letters 95 (24): 248701. arXiv:physics/0506126. Bibcode:2005PhRvL..95x8701V. doi:10.1103/PhysRevLett.95.248701. PMID 16384430. 
  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. arXiv:physics/0510117. Bibcode:2006PhRvE..73c6127V. doi:10.1103/PhysRevE.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. arXiv:cond-mat/0512278. Bibcode:2006PhyA..369..877H. doi:10.1016/j.physa.2005.12.035. 
  5. ^ J. G. Oliveira & A.-L. Barabási, (2005). "Human Dynamics: The Correspondence Patterns of Darwin and Einstein.". Nature 437 (7063): 1251. arXiv:physics/0511006. Bibcode:2005Natur.437.1251O. doi:10.1038/4371251a. PMID 16251946. 
  6. ^ a b Bruno Goncalves, Jose J. Ramasco, (2008). "Human dynamics revealed through Web analytics". Physical Review E 78: 026123. arXiv:0803.4018. Bibcode:2008PhRvE..78b6123G. doi:10.1103/PhysRevE.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. Bibcode:2006PhRvE..73f6132D. doi:10.1103/PhysRevE.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. arXiv:physics/0610104. Bibcode:2007PNAS..104.7332O. doi:10.1073/pnas.0610245104. PMC 1863470. PMID 17456605. 
  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. arXiv:physics/0702158. Bibcode:2007NJPh....9..179O. doi:10.1088/1367-2630/9/6/179. 
  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. Bibcode:2007NatPh...3..224G. doi:10.1038/nphys581. 

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Sense Networks