Human dynamics

Last updated

Human dynamics refer to a branch of complex systems research in statistical physics such as the movement of crowds and queues and other systems of complex human interactions including statistical modelling of human networks, including interactions over communications networks.

Contents

Academic research

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] [5] its experimental verification in several different activities [5] [6] and the beginning of interest in using proxy tools, such as web server logs., [7] [8] [9] cell phone records [10] [11] and even the rate at which registration to a major international conference occurs [12] and the distance and rate people around the globe commute from home to work. [13]

In recent years there has been a growing appetite for access to new data sources [14] that might prove useful in quantifying and understanding human behavior both at the individual and collective scales. [15]

Other usage

The term Human Dynamics or Human Dynamics as Personality Dynamics has also been used to describe a framework for understanding people and is applied in a technique aimed at education and team building. It is pioneered by Sandra Seagal and David Horne. [16] It is a study of the ways in which we process information related to the balance of Physical, Emotional, and Mental components of our experiences. [17] It has been subject to some skepticism, having been described in a Dutch newspaper as a personality course with esoteric (occult) roots. [18] [19]

See also

Sense Networks

Related Research Articles

Doubly special relativity (DSR) – also called deformed special relativity or, by some, extra-special relativity – is a modified theory of special relativity in which there is not only an observer-independent maximum velocity, but also, an observer-independent maximum energy scale and/or a minimum length scale. This contrasts with other Lorentz-violating theories, such as the Standard-Model Extension, where Lorentz invariance is instead broken by the presence of a preferred frame. The main motivation for this theory is that the Planck energy should be the scale where as yet unknown quantum gravity effects become important and, due to invariance of physical laws, this scale should remain fixed in all inertial frames.

<span class="mw-page-title-main">Scale-free network</span> Network whose degree distribution follows a power law

A scale-free network is a network whose degree distribution follows a power law, at least asymptotically. That is, the fraction P(k) of nodes in the network having k connections to other nodes goes for large values of k as

<span class="mw-page-title-main">Self-organized criticality</span> Concept in physics

Self-organized criticality (SOC) is a property of dynamical systems that have a critical point as an attractor. Their macroscopic behavior thus displays the spatial or temporal scale-invariance characteristic of the critical point of a phase transition, but without the need to tune control parameters to a precise value, because the system, effectively, tunes itself as it evolves towards criticality.

<span class="mw-page-title-main">Complex network</span> Network with non-trivial topological features

In the context of network theory, a complex network is a graph (network) with non-trivial topological features—features that do not occur in simple networks such as lattices or random graphs but often occur in networks representing real systems. The study of complex networks is a young and active area of scientific research inspired largely by empirical findings of real-world networks such as computer networks, biological networks, technological networks, brain networks, climate networks and social networks.

In physics, the Tsallis entropy is a generalization of the standard Boltzmann–Gibbs entropy.

In complex network theory, the fitness model is a model of the evolution of a network: how the links between nodes change over time depends on the fitness of nodes. Fitter nodes attract more links at the expense of less fit nodes.

<span class="mw-page-title-main">Percolation threshold</span> Threshold of percolation theory models

The percolation threshold is a mathematical concept in percolation theory that describes the formation of long-range connectivity in random systems. Below the threshold a giant connected component does not exist; while above it, there exists a giant component of the order of system size. In engineering and coffee making, percolation represents the flow of fluids through porous media, but in the mathematics and physics worlds it generally refers to simplified lattice models of random systems or networks (graphs), and the nature of the connectivity in them. The percolation threshold is the critical value of the occupation probability p, or more generally a critical surface for a group of parameters p1, p2, ..., such that infinite connectivity (percolation) first occurs.

<span class="mw-page-title-main">Christopher T. Hill</span> American theoretical physicist

Christopher T. Hill is an American theoretical physicist at the Fermi National Accelerator Laboratory who did undergraduate work in physics at M.I.T., and graduate work at Caltech. Hill's Ph.D. thesis, "Higgs Scalars and the Nonleptonic Weak Interactions" (1977) contains one of the first detailed discussions of the two-Higgs-doublet model and its impact upon weak interactions.

Within quantum technology, a quantum sensor utilizes properties of quantum mechanics, such as quantum entanglement, quantum interference, and quantum state squeezing, which have optimized precision and beat current limits in sensor technology. The field of quantum sensing deals with the design and engineering of quantum sources and quantum measurements that are able to beat the performance of any classical strategy in a number of technological applications. This can be done with photonic systems or solid state systems.

A strangelet is a hypothetical particle consisting of a bound state of roughly equal numbers of up, down, and strange quarks. An equivalent description is that a strangelet is a small fragment of strange matter, small enough to be considered a particle. The size of an object composed of strange matter could, theoretically, range from a few femtometers across to arbitrarily large. Once the size becomes macroscopic, such an object is usually called a strange star. The term "strangelet" originates with Edward Farhi and Robert Jaffe in 1984. Strangelets can convert matter to strange matter on contact. Strangelets have been suggested as a dark matter candidate.

Thomas Carlos Mehen is an American physicist. His research has consisted of primarily Quantum chromodynamics (QCD) and the application of effective field theory to problems in hadronic physics. He has also worked on effective field theory for non-relativistic particles whose short range interactions are characterized by a large scattering length, as well as novel field theories which arise from unusual limits of string theory.

<span class="mw-page-title-main">Active matter</span> Matter behavior at system scale

Active matter is matter composed of large numbers of active "agents", each of which consumes energy in order to move or to exert mechanical forces. Such systems are intrinsically out of thermal equilibrium. Unlike thermal systems relaxing towards equilibrium and systems with boundary conditions imposing steady currents, active matter systems break time reversal symmetry because energy is being continually dissipated by the individual constituents. Most examples of active matter are biological in origin and span all the scales of the living, from bacteria and self-organising bio-polymers such as microtubules and actin, to schools of fish and flocks of birds. However, a great deal of current experimental work is devoted to synthetic systems such as artificial self-propelled particles. Active matter is a relatively new material classification in soft matter: the most extensively studied model, the Vicsek model, dates from 1995.

Maya Paczuski is the head and founder of the Complexity Science Group at the University of Calgary. She is a well-cited physicist whose work spans self-organized criticality, avalanche dynamics, earthquake, and complex networks. She was born in Israel in 1963, but grew up in the United States. Maya Paczuski received a B.S. and M.S. in Electrical Engineering and Computer Science from M.I.T. in 1986 and then went on to study with Mehran Kardar, earning her Ph.D in Condensed matter physics from the same institute.

<span class="mw-page-title-main">Modern searches for Lorentz violation</span> Overview about the modern searches for Lorentz violation

Modern searches for Lorentz violation are scientific studies that look for deviations from Lorentz invariance or symmetry, a set of fundamental frameworks that underpin modern science and fundamental physics in particular. These studies try to determine whether violations or exceptions might exist for well-known physical laws such as special relativity and CPT symmetry, as predicted by some variations of quantum gravity, string theory, and some alternatives to general relativity.

<span class="mw-page-title-main">Baruch Barzel</span> Israeli physicist and mathematician

Baruch Barzel is an Israeli physicist and applied mathematician at Bar-Ilan University, a member of the Gonda Multidisciplinary Brain Research Center and of the Bar-Ilan Data Science Institute. His main research areas are statistical physics, complex systems, nonlinear dynamics and network science.

Raymond Ethan Goldstein FRS FInstP is Schlumberger Professor of Complex Physical Systems in the Department of Applied Mathematics and Theoretical Physics (DAMTP) at the University of Cambridge and a Fellow of Churchill College, Cambridge.

Hyperuniform materials are characterized by an anomalous suppression of density fluctuations at large scales. More precisely, the vanishing of density fluctuations in the long-wave length limit distinguishes hyperuniform systems from typical gases, liquids, or amorphous solids. Examples of hyperuniformity include all perfect crystals, perfect quasicrystals, and exotic amorphous states of matter.

Shortcuts to adiabaticity (STA) are fast control protocols to drive the dynamics of system without relying on the adiabatic theorem. The concept of STA was introduced in a 2010 paper by Xi Chen et al. Their design can be achieved using a variety of techniques. A universal approach is provided by counterdiabatic driving, also known as transitionless quantum driving. Motivated by one of authors systematic study of dissipative Landau-Zener transition, the key idea was demonstrated earlier by a group of scientists from China, Greece and USA in 2000, as steering an eigenstate to destination. Counterdiabatic driving has been demonstrated in the laboratory using a time-dependent quantum oscillator.

Applying classical methods of machine learning to the study of quantum systems is the focus of an emergent area of physics research. A basic example of this is quantum state tomography, where a quantum state is learned from measurement. Other examples include learning Hamiltonians, learning quantum phase transitions, and automatically generating new quantum experiments. Classical machine learning is effective at processing large amounts of experimental or calculated data in order to characterize an unknown quantum system, making its application useful in contexts including quantum information theory, quantum technologies development, and computational materials design. In this context, it can be used for example as a tool to interpolate pre-calculated interatomic potentials or directly solving the Schrödinger equation with a variational method.

Jens Horst Gundlach is a German physicist.

References

  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. S2CID   4419475.
  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. S2CID   15664101.
  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 (3): 036127. arXiv: physics/0510117 . Bibcode:2006PhRvE..73c6127V. doi:10.1103/PhysRevE.73.036127. PMID   16605618. S2CID   14321672.
  4. Cesar A. Hidalgo (2006). "Conditions for the emergence of scaling in the inter-event time of uncorrelated and seasonal systems". Physica A. 369 (2): 877–883. arXiv: cond-mat/0512278 . Bibcode:2006PhyA..369..877H. doi:10.1016/j.physa.2005.12.035. S2CID   119340950.
  5. 1 2 M. Formentin; A. Lovison; A. Maritan; G. Zanzotto (2014). "Hidden scaling patterns and universality in written communication". Physical Review E. 90 (1): 012817. arXiv: 1311.3601 . Bibcode:2014PhRvE..90a2817F. doi:10.1103/PhysRevE.90.012817. PMID   25122352. S2CID   24022721.
  6. 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. S2CID   4428375.
  7. Bruno Goncalves; Jose J. Ramasco (2008). "Human dynamics revealed through Web analytics". Physical Review E. 78 (2): 026123. arXiv: 0803.4018 . Bibcode:2008PhRvE..78b6123G. doi:10.1103/PhysRevE.78.026123. PMID   18850913. S2CID   4975393.
  8. Bruno Goncalves; Jose J. Ramasco (2009). "Towards the Characterization of Individual Users through Web Analytics". Complex Sciences. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Vol. 5. pp. 2247–2254. arXiv: 0901.0498 . Bibcode:2009cosc.conf.2247G. doi:10.1007/978-3-642-02469-6_102. ISBN   978-3-642-02468-9. S2CID   15251450.
  9. Z. Dezsö; E. Almaas; A. Lukács; B. Rácz; I. Szakadát; A.-L. Barabási (2006). "Dynamics of information access on the web" (PDF). Physical Review E. 73 (6): 066132. Bibcode:2006PhRvE..73f6132D. doi:10.1103/PhysRevE.73.066132. hdl: 2047/d20000691 . PMID   16906939.
  10. J.-P. Onnela; J. Saramäki; J. Hyvönen; G. Szabó; D. Lazer; K. Kaski; J. Kertész; 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.
  11. Jukka-Pekka Onnela; Jari Saramäki; Jörkki Hyvönen; Gábor Szabó; M Argollo de Menezes; Kimmo Kaski; Albert-László Barabási; János Kertèsz (2007). "Analysis of a large-scale weighted network of one-to-one human communication". New Journal of Physics. 9 (6): 179. arXiv: physics/0702158 . Bibcode:2007NJPh....9..179O. doi:10.1088/1367-2630/9/6/179. S2CID   119382956.
  12. Valentina Alfi; Giorgio Parisi; Luciano Pietronero (2007). "Conference registration: how people react to a deadline". Nature Physics. 3 (11): 746. Bibcode:2007NatPh...3..746A. doi: 10.1038/nphys761 .
  13. 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". Proceedings of the National Academy of Sciences. 106 (51): 21484–21489. arXiv: 0907.3304 . Bibcode:2009PNAS..10621484B. doi: 10.1073/pnas.0906910106 . PMC   2793313 . PMID   20018697.
  14. Marta C. González; Albert-László Barabási (2007). "Complex networks: From data to models". Nature Physics. 3 (4): 224–225. Bibcode:2007NatPh...3..224G. doi:10.1038/nphys581. PMC   7096906 . PMID   32226461.
  15. M. Formentin; A. Lovison; A. Maritan; G. Zanzotto (2015). "New activity pattern in human interactive dynamics". Journal of Statistical Mechanics: Theory and Experiment. 2015 (9): P09006. arXiv: 1405.5726 . Bibcode:2015JSMTE..09..006F. doi:10.1088/1742-5468/2015/09/P09006. S2CID   16975015.
  16. Seagal, Sandra; Horne, David (1997). Human Dynamics: A New Framework for Understanding People and Realizing the Potential in Our Organizations. Pegasus Communications Inc. ISBN   1-883823-06-4.
  17. Seagal, Sandra (23 January 2016). "Human Dynamics". thesystemsthinker.com. Retrieved 19 January 2021.
  18. "Skepsis bekritiseert 'occulte' schooltraining". Nederlands Dagblad (in Dutch). 10 November 2009. Retrieved 4 September 2014.
  19. Nanninga, Rob (2009). "Human Dynamics. Occulte psychologie op school". Skepter (in Dutch). 22 (1).