Social physics

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Social physics or sociophysics is a field of science which uses mathematical tools inspired by physics to understand the behavior of human crowds. In a modern commercial use, it can also refer to the analysis of social phenomena with big data.

Contents

Social physics is closely related to econophysics, which uses physics methods to describe economics. [1]

History

The earliest mentions of a concept of social physics began with the English philosopher Thomas Hobbes. In 1636 he traveled to Florence, Italy, and met physicist-astronomer Galileo Galilei, known for his contributions to the study of motion. [2] It was here that Hobbes began to outline the idea of representing the "physical phenomena" of society in terms of the laws of motion. [2] In his treatise De Corpore , Hobbes sought to relate the movement of "material bodies" [3] to the mathematical terms of motion outlined by Galileo and similar scientists of the time period. Although there was no explicit mention of "social physics", the sentiment of examining society with scientific methods began before the first written mention of social physics.

Later, French social thinker Henri de Saint-Simon’s first book, the 1803 Lettres d’un Habitant de Geneve, introduced the idea of describing society using laws similar to those of the physical and biological sciences. [4] His student and collaborator was Auguste Comte, a French philosopher widely regarded as the founder of sociology, who first defined the term in an essay appearing in Le Producteur, a journal project by Saint-Simon. [4] Comte defined social physics:

Social physics is that science which occupies itself with social phenomena, considered in the same light as astronomical, physical, chemical, and physiological phenomena, that is to say as being subject to natural and invariable laws, the discovery of which is the special object of its researches.

After Saint-Simon and Comte, Belgian statistician Adolphe Quetelet, proposed that society be modeled using mathematical probability and social statistics. Quetelet's 1835 book, Essay on Social Physics: Man and the Development of his Faculties, outlines the project of a social physics characterized by measured variables that follow a normal distribution, and collected data about many such variables. [5] A frequently repeated anecdote is that when Comte discovered that Quetelet had appropriated the term "social physics", he found it necessary to invent a new term, "sociologie" ("sociology") because he disagreed with Quetelet's collection of statistics.

There have been several “generations” of social physicists. [6] The first generation began with Saint-Simon, Comte, and Quetelet, and ended with the late 1800s with historian Henry Adams. In the middle of the 20th century, researchers such as the American astrophysicist John Q. Stewart and Swedish geographer Reino Ajo, [7] who showed that the spatial distribution of social interactions could be described using gravity models. Physicists such as Arthur Iberall use a homeokinetics approach to study social systems as complex self-organizing systems. [8] [9] For example, a homeokinetics analysis of society shows that one must account for flow variables such as the flow of energy, of materials, of action, reproduction rate, and value-in-exchange. [10] More recently there have been a large number of social science papers that use mathematics broadly similar to that of physics, and described as “computational social science”. [11]

In the late 1800s, Adams separated “human physics” into the subsets of social physics or social mechanics (sociology of interactions using physics-like mathematical tools) [12] and social thermodynamics or sociophysics (sociology described using mathematical invariances similar to those in thermodynamics). [13] This dichotomy is roughly analogous to the difference between microeconomics and macroeconomics.

Examples

Ising model and voter dynamics

A 5x5 representational grid of an Ising model. Each space holds a spin and the red bars indicate communication between neighbors. Ising model 5x5 g.svg
A 5x5 representational grid of an Ising model. Each space holds a spin and the red bars indicate communication between neighbors.

One of the most well-known examples in social physics is the relationship of the Ising model and the voting dynamics of a finite population. The Ising model, as a model of ferromagnetism, is represented by a grid of spaces, each of which is occupied by a Spin (physics), numerically ±1. Mathematically, the final energy state of the system depends on the interactions of the spaces and their respective spins. For example, if two adjacent spaces share the same spin, the surrounding neighbors will begin to align,[ citation needed ] and the system will eventually reach a state of consensus. In social physics, it has been observed that voter dynamics in a finite population obey the same mathematical properties of the Ising model. In the social physics model, each spin denotes an opinion, e.g. yes or no, and each space represents a "voter".[ citation needed ] If two adjacent spaces (voters) share the same spin (opinion), their neighbors begin to align with their spin value; if two adjacent spaces do not share the same spin, then their neighbors remain the same. [14] Eventually, the remaining voters will reach a state of consensus as the "information flows outward". [14]

Example of social validation in the Sznajd model. If two neighbors agree (top), then their neighbors agree with them. If two neighbors disagree (bottom), their neighbors begin to disagree as well. SznajdModelSketch.svg
Example of social validation in the Sznajd model. If two neighbors agree (top), then their neighbors agree with them. If two neighbors disagree (bottom), their neighbors begin to disagree as well.

The Sznajd model is an extension of the Ising model and is classified as an econophysics model. It emphasizes the alignment of the neighboring spins in a phenomenon called "social validation". [15] It follows the same properties as the Ising model and is extended to observe the patterns of opinion dynamics as a whole, rather than focusing on just voter dynamics.  

Potts model and cultural dynamics

The Potts model is a generalization of the Ising model and has been used to examine the concept of cultural dissemination as described by American political scientist Robert Axelrod. Axelrod's model of cultural dissemination states that individuals who share cultural characteristics are more likely to interact with each other, thus increasing the number of overlapping characteristics and expanding their interaction network. [16] The Potts model has the caveat that each spin can hold multiple values, unlike the Ising model that could only hold one value. [17] [18] [19] Each spin, then, represents an individual's "cultural characteristics... [or] in Axelrod’s words, 'the set of individual attributes that are subject to social influence'". [19] It is observed that, using the mathematical properties of the Potts model, neighbors whose cultural characteristics overlap tend to interact more frequently than with unlike neighbors, thus leading to a self-organizing grouping of similar characteristics. [18] [17] Simulations done on the Potts model both show Axelrod's model of cultural dissemination agrees with the Potts model as an Ising-class model. [18]

Recent work

In modern use “social physics” refers to using “big data” analysis and the mathematical laws to understand the behavior of human crowds. [20] The core idea is that data about human activity (e.g., phone call records, credit card purchases, taxi rides, web activity) contain mathematical patterns that are characteristic of how social interactions spread and converge. These mathematical invariances can then serve as a filter for analysis of behavior changes and for detecting emerging behavioral patterns. [21]

Social physics has recently been applied to analyze the COVID-19 pandemics. [22] It has been demonstrated that the large difference in the spread of COVID-19 between countries is due to differences in responses to social stress. The combination of traditional epidemic models with social physics models of the classical general adaptation syndrome triad, "anxiety-resistance-exhaustion", accurately describes the first two waves of the COVID-19 epidemic for 13 countries. [22] The differences between countries are concentrated in two kinetic constants: the rate of mobilization and the rate of exhaustion.

Recent books about social physics include MIT Professor Alex Pentland’s book Social Physics  [23] or Nature editor Mark Buchanan’s book The Social Atom. [24] Popular reading about sociophysics include English physicist Philip Ball’s Why Society is a Complex Matter, [25] Dirk Helbing's The Automation of Society is next or American physicist Laszlo Barabasi’s book Linked. [26]

See also

Related Research Articles

The Ising model, named after the physicists Ernst Ising and Wilhelm Lenz, is a mathematical model of ferromagnetism in statistical mechanics. The model consists of discrete variables that represent magnetic dipole moments of atomic "spins" that can be in one of two states. The spins are arranged in a graph, usually a lattice, allowing each spin to interact with its neighbors. Neighboring spins that agree have a lower energy than those that disagree; the system tends to the lowest energy but heat disturbs this tendency, thus creating the possibility of different structural phases. The model allows the identification of phase transitions as a simplified model of reality. The two-dimensional square-lattice Ising model is one of the simplest statistical models to show a phase transition.

Ernst Ising was a German physicist, who is best remembered for the development of the Ising model. He was a professor of physics at Bradley University until his retirement in 1976.

In statistical mechanics, the Potts model, a generalization of the Ising model, is a model of interacting spins on a crystalline lattice. By studying the Potts model, one may gain insight into the behaviour of ferromagnets and certain other phenomena of solid-state physics. The strength of the Potts model is not so much that it models these physical systems well; it is rather that the one-dimensional case is exactly solvable, and that it has a rich mathematical formulation that has been studied extensively.

The Kramers–Wannier duality is a symmetry in statistical physics. It relates the free energy of a two-dimensional square-lattice Ising model at a low temperature to that of another Ising model at a high temperature. It was discovered by Hendrik Kramers and Gregory Wannier in 1941. With the aid of this duality Kramers and Wannier found the exact location of the critical point for the Ising model on the square lattice.

Igor R. Klebanov is an American theoretical physicist. Since 1989, he has been a faculty member at Princeton University where he is currently a Eugene Higgins Professor of Physics and the director of the Princeton Center for Theoretical Science. In 2016, he was elected to the National Academy of Sciences. Since 2022, he is the director of the Simons Collaboration on Confinement and QCD Strings.

Extremal optimization (EO) is an optimization heuristic inspired by the Bak–Sneppen model of self-organized criticality from the field of statistical physics. This heuristic was designed initially to address combinatorial optimization problems such as the travelling salesman problem and spin glasses, although the technique has been demonstrated to function in optimization domains.

Quantum annealing (QA) is an optimization process for finding the global minimum of a given objective function over a given set of candidate solutions, by a process using quantum fluctuations. Quantum annealing is used mainly for problems where the search space is discrete with many local minima; such as finding the ground state of a spin glass or the traveling salesman problem. The term "quantum annealing" was first proposed in 1988 by B. Apolloni, N. Cesa Bianchi and D. De Falco as a quantum-inspired classical algorithm. It was formulated in its present form by T. Kadowaki and H. Nishimori in 1998 though an imaginary-time variant without quantum coherence had been discussed by A. B. Finnila, M. A. Gomez, C. Sebenik and J. D. Doll in 1994.

Dante R. Chialvo is a professor at Universidad Nacional de San Martin. Together with Per Bak, they put forward concrete models considering the brain as a critical system. Initial contributions focussed on mathematical ideas of how learning could benefit from criticality. Further work provided experimental evidence for this conjecture both at large and small scale. He was named Fulbright Scholar in 2005 and elected as a Fellow of the American Physical Society in 2007 and as Member of the Academia de Ciencias de America Latina in 2022.

<span class="mw-page-title-main">Arthur Iberall</span> American physicist

Arthur S. Iberall was an American physicist/hydrodynamicist and engineer who pioneered homeokinetics, the physics of complex, self-organizing systems. He was the originator of the concept of lines of non-extension on the human body which was used to create workable space suits.

<span class="mw-page-title-main">Piers Coleman</span> British-American physicist

Piers Coleman is a British-born theoretical physicist, working in the field of theoretical condensed matter physics. Coleman is professor of physics at Rutgers University in New Jersey and at Royal Holloway, University of London.

<span class="mw-page-title-main">Steven T. Bramwell</span> British scientist

Steven T. Bramwell is a British physicist and chemist who works at the London Centre for Nanotechnology and the Department of Physics and Astronomy, University College London. He is known for his experimental discovery of spin ice with M. J. Harris and his calculation of a critical exponent observed in two-dimensional magnets with P. C. W. Holdsworth. A probability distribution for global quantities in complex systems, the "Bramwell-Holdsworth-Pinton (BHP) distribution", is named after him.

<span class="mw-page-title-main">Quantum simulator</span> Simulators of quantum mechanical systems

Quantum simulators permit the study of a quantum system in a programmable fashion. In this instance, simulators are special purpose devices designed to provide insight about specific physics problems. Quantum simulators may be contrasted with generally programmable "digital" quantum computers, which would be capable of solving a wider class of quantum problems.

Joseph Alan Rudnick is an American physicist and professor in the Department of Physics and Astronomy at UCLA. Rudnick currently serves as the senior dean of the UCLA College of Letters and Science and dean of the Division of Physical Sciences. He previously served as the chair of the Department of Physics and Astronomy. His research interests include condensed-matter physics, statistical mechanics, and biological physics.

<span class="mw-page-title-main">Sznajd model</span>

The Sznajd model or United we stand, divided we fall (USDF) model is a sociophysics model introduced in 2000 to gain fundamental understanding about opinion dynamics. The Sznajd model implements a phenomenon called social validation and thus extends the Ising spin model. In simple words, the model states:

Serge Galam is a French physicist and Scientist Emeritus at CNRS.

Arthur Brooks Harris, called Brooks Harris, is an American physicist.

In statistical mechanics, probability theory, graph theory, etc. the random cluster model is a random graph that generalizes and unifies the Ising model, Potts model, and percolation model. It is used to study random combinatorial structures, electrical networks, etc. It is also referred to as the RC model or sometimes the FK representation after its founders Cees Fortuin and Piet Kasteleyn. The random cluster model has a critical limit, described by a conformal field theory.

Schelling's model of segregation is an agent-based model developed by economist Thomas Schelling. Schelling's model does not include outside factors that place pressure on agents to segregate such as Jim Crow laws in the United States, but Schelling's work does demonstrate that having people with "mild" in-group preference towards their own group could still lead to a highly segregated society via de facto segregation.

Replica cluster move in condensed matter physics refers to a family of non-local cluster algorithms used to simulate spin glasses. It is an extension of the Swendsen-Wang algorithm in that it generates non-trivial spin clusters informed by the interaction states on two replicas instead of just one. It is different from the replica exchange method, as it performs a non-local update on a fraction of the sites between the two replicas at the same temperature, while parallel tempering directly exchanges all the spins between two replicas at different temperature. However, the two are often used alongside to achieve state-of-the-art efficiency in simulating spin-glass models.

References

  1. ↑ Tsekov, Roumen (2023). "Social Thermodynamics 2.0". arXiv: 2307.05984 .
  2. 1 2 Robertson, George Croom (1911). "Hobbes, Thomas"  . Encyclopædia Britannica . Vol. 13 (11th ed.). pp. 545–552.
  3. ↑ Duncan, Stewart (2021), "Thomas Hobbes", in Zalta, Edward N. (ed.), The Stanford Encyclopedia of Philosophy (Spring 2021 ed.), Metaphysics Research Lab, Stanford University, retrieved 2021-02-24
  4. 1 2 Iggers, Georg G. (1959). "Further Remarks about Early Uses of the Term "Social Science"". Journal of the History of Ideas. 20 (3): 433–436. doi:10.2307/2708121. JSTOR   2708121.
  5. ↑ Quetelet, Adolphe (1835). Sur l'homme et le Développement de ses Facultés, ou Essai de Physique Sociale [Essay on Social Physics: Man and the Development of his Faculties] (in French). Vol. 1–2. Paris: Imprimeur-Libraire.
  6. ↑ Iberall, Arthur (1984) [Presented at Annual Conference of the International Society for the Comparative Study of Civilizations (ISCSC), Syracuse, May 1980]. "Contributions to a Physical Science for the Study of Civilizations". Journal of Social and Biological Structures. 7 (3): 259–283. doi:10.1016/S0140-1750(84)80037-8.
  7. ↑ Ajo, Reino (1953). Contributions to "Social Physics": a Programme Sketch with Special Regard to National Planning. Royal University of Lund.
  8. ↑ Iberall, A (1985). "Outlining social physics for modern societies - locating culture, economics, and politics: The Enlightenment reconsidered". Proc Natl Acad Sci USA. 82 (17): 5582–84. Bibcode:1985PNAS...82.5582I. doi: 10.1073/pnas.82.17.5582 . PMC   390594 . PMID   16593594.
  9. ↑ Iberall, A; Hassler, F; Soodak, H; Wilkinson, D (2000). "Invitation to an Enterprise: From Physics to World History to Civilizations' Study". Comparative Civilizations Review. 42: 4–22.
  10. ↑ Iberall, Arthur S. (2016), Homeokinetics: The Basics, Medfield, MA: Strong Voices Publishing, ISBN   978-0-990-53614-7
  11. ↑ Lazer, D., Pentland, A., et al Science 2010
  12. ↑ Ball, Phillip (2002) [A talk presented at ‘Horizons in Complex Systems’ Messina, Sicily, Italy, 2001]. "The physical modelling of society: a historical perspective" (PDF). Physica A. 314 (1–4): 1–14. Bibcode:2002PhyA..314....1B. doi:10.1016/s0378-4371(02)01042-7.
  13. ↑ Wilson, Alan (2005). "Notes on Some Concepts in Social Physics". Regional Science. 22 (1): 159–193. doi: 10.1111/j.1435-5597.1969.tb01460.x . S2CID   60841600.
  14. 1 2 Sznajd-Weron, Katarzyna (2005-03-31). "Sznajd model and its applications". Acta Physica Polonica B. 36 (8): 2537. arXiv: physics/0503239 . Bibcode:2005AcPPB..36.2537S.
  15. ↑ Castellano, Claudio; Fortunato, Santo; Loreto, Vittorio (2009-05-11). "Statistical physics of social dynamics". Reviews of Modern Physics. 81 (2): 591–646. arXiv: 0710.3256 . Bibcode:2009RvMP...81..591C. doi:10.1103/RevModPhys.81.591. S2CID   118376889.
  16. ↑ Axelrod, Robert (2016-07-01). "The Dissemination of Culture: A Model with Local Convergence and Global Polarization". Journal of Conflict Resolution. 41 (2): 203–226. doi:10.1177/0022002797041002001. S2CID   54639070.
  17. 1 2 Klemm, Konstantin; Eguíluz, Víctor M.; Toral, Raúl; Miguel, Maxi San (2003-04-15). "Global culture: A noise-induced transition in finite systems". Physical Review E. 67 (4): 045101. arXiv: cond-mat/0205188 . Bibcode:2003PhRvE..67d5101K. doi:10.1103/PhysRevE.67.045101. hdl: 10261/15315 . PMID   12786417. S2CID   6976248.
  18. 1 2 3 Gandica, Y.; Medina, E.; Bonalde, I. (2013-12-15). "A thermodynamic counterpart of the Axelrod model of social influence: The one-dimensional case". Physica A: Statistical Mechanics and Its Applications. 392 (24): 6561–6570. arXiv: 1208.4381 . Bibcode:2013PhyA..392.6561G. doi:10.1016/j.physa.2013.08.033. hdl: 10316/27254 . ISSN   0378-4371. S2CID   17918682.
  19. 1 2 Mihăilescu, Luca Mircea. "Simulation of Potts Model on a Dynamically Rewired Network".{{cite journal}}: Cite journal requires |journal= (help)
  20. ↑ GEORGE, Gerard; HAAS, Martine R.; PENTLAND, Alex (2014). "Big Data and Management: From the Editors". Academy of Management Journal. 57 (2): 321–326. doi:10.5465/amj.2014.4002.
  21. ↑ "Predictive Analytics". IBM .
  22. 1 2 Kastalskiy, IA; Pankratova, EV; Mirkes, EM; et al. (2021). "Social stress drives the multi-wave dynamics of COVID-19 outbreaks". Scientific Reports. 11 (1): 22497. arXiv: 2106.08966 . Bibcode:2021NatSR..1122497K. doi: 10.1038/s41598-021-01317-z . PMC   8602246 . PMID   34795311.
  23. ↑ Pentland, Alex (2014). Social physics: how good ideas spread: the lessons from a new science. New York, USA: The Penguin Press. ISBN   978-1-59420-565-1.
  24. ↑ Buchanan, Mark (2007). The Social Atom - why the Rich get Richer, Cheaters get Caught, and Your Neighbor Usually Looks Like You. Bloomsbury USA. pp. x–xi. ISBN   9781596917316.
  25. ↑ Ball, Philip (2012). Why Society is a Complex Matter: Meeting Twenty-First Century Challenges with a New Kind of Science. Springer.
  26. ↑ Barabási, Albert-László (2002). Linked: The New Science of Networks. Perseus Books Group.

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