Econophysics

Econophysics is an interdisciplinary research field, applying theories and methods originally developed by physicists in order to solve problems in economics, usually those including uncertainty or stochastic processes and nonlinear dynamics. Its application to the study of financial markets has also been termed statistical finance referring to its roots in statistical physics.

History

Physicists’ interest in the social sciences is not new; Daniel Bernoulli, as an example, was the originator of utility-based preferences. One of the founders of neoclassical economic theory, former Yale University Professor of Economics Irving Fisher, was originally trained under the renowned Yale physicist, Josiah Willard Gibbs.^[1] Likewise, Jan Tinbergen, who won the first Nobel Prize in economics in 1969 for having developed and applied dynamic models for the analysis of economic processes, studied physics with Paul Ehrenfest at Leiden University.

Econophysics was started in the mid-1990s by several physicists working in the subfield of statistical mechanics. Unsatisfied with the traditional explanations and approaches of economists - which usually prioritized simplified approaches for the sake of soluble theoretical models over agreement with empirical data - they applied tools and methods from physics, first to try to match financial data sets, and then to explain more general economic phenomena.

One driving force behind econophysics arising at this time was the sudden availability of large amounts of financial data, starting in the 1980s. It became apparent that traditional methods of analysis were insufficient - standard economic methods dealt with homogeneous agents and equilibrium, while many of the more interesting phenomena in financial markets fundamentally depended on heterogeneous agents and far-from-equilibrium situations.

The term “econophysics” was coined by H. Eugene Stanley in the mid 1990s, to describe the large number of papers written by physicists in the problems of (stock and other) markets, and first appeared in a conference on statistical physics in Calcutta in 1995 and its following publications. The inaugural meeting on Econophysics was organised 1998 in Budapest by János Kertész and Imre Kondor.

Currently, the almost regular meeting series on the topic include: Econophysics Colloquium, ESHIA/ WEHIA, ECONOPHYS-KOLKATA, APFA

If "econophysics" is taken to denote the principle of applying statistical mechanics to economic analysis, as opposed to a particular literature or network, priority of innovation is probably due to Farjoun and Machover (1983). Their book Laws of Chaos: A Probabilistic Approach to Political Economy proposes dissolving (their words) the transformation problem in Marx's political economy by re-conceptualising the relevant quantities as random variables.

If, on the other side, "econophysics" is taken to denote the application of physics to economics, one can already consider the works of Léon Walras and Vilfredo Pareto as part of it. Indeed, as shown by Ingrao and Israel, general equilibrium theory in economics is based on the physical concept of mechanical equilibrium.

Econophysics has nothing to do with the "physical quantities approach" to economics, advocated by Ian Steedman and others associated with Neo-Ricardianism. Notable econophysicists are Jean-Philippe Bouchaud, Bikas K Chakrabarti, Dirk Helbing, János Kertész, Matteo Marsili, Joseph L. McCauley, Enrico Scalas, Didier Sornette, H. Eugene Stanley, Victor Yakovenko and Yi-Cheng Zhang.

Basic tools

Basic tools of econophysics are probabilistic and statistical methods often taken from statistical physics.

Physics models that have been applied in economics include percolation models, chaotic models developed to study cardiac arrest, and models with self-organizing criticality as well as other models developed for earthquake prediction.^[2] Moreover, there have been attempts to use the mathematical theory of complexity and information theory, as developed by many scientists among whom are Murray Gell-Mann and Claude E. Shannon, respectively.

Since economic phenomena are the result of the interaction among many heterogeneous agents, there is an analogy with statistical mechanics, where many particles interact; but it must be taken into account that the properties of human beings and particles significantly differ.

Another good example is Random Matrix Theory, which can be used to identify the noise in financial correlation matrices. It has been shown that this technique can significantly improve the performance of portfolios, e.g., in applied in Portfolio Optimization^[3] . Thus, this practice is commonly encountered in the praxis of quantitative finance.

There are, however, various other tools from physics that have so far been used with mixed success, such as fluid dynamics, classical mechanics and quantum mechanics (including so-called classical economy, quantum economy and quantum finance), and the path integral formulation of statistical mechanics.

There are also analogies between finance theory and diffusion theory. For instance, the Black-Scholes equation for option pricing is a diffusion-advection equation.

Impact on mainstream economics and finance

Papers on econophysics have been published primarily in journals devoted to physics and statistical mechanics, rather than in leading economics journals. Mainstream economists have generally been unimpressed by this work.^[4] Some Heterodox economists, including Mauro Gallegati, Steve Keen and Paul Ormerod, have shown more interest, but also criticized trends in econophysics.

In contrast, econophysics is having some impact on the more applied field of quantitative finance, whose scope and aims significantly differ from those of economic theory. Various econophysicists have introduced models for price fluctuations in financial markets or original points of view on established models.^[5][6] Also several scaling laws have been found in various economic data.^[7][8][9]

See also

Physics portal

• Bose–Einstein condensation (network theory)
• Complexity economics
• Complex network
• Detrended fluctuation analysis
• Kinetic exchange models of markets
• Long-range dependency
• Network theory
• Network science
• Thermoeconomics

References

1. ^ Yale Economic Review, Retrieved October-25-09
2. ^ Didier Sornette (2003). Why Stock Markets Crash?. Princeton University Press.
3. ^ Vasiliki Plerou, Parameswaran Gopikrishnan, Bernd Rosenow, Luis Amaral, Thomas Guhr and H. Eugene Stanley (2002). "Random matrix approach to cross correlations in financial data". Physical Review E 65 (6): 066126. doi:10.1103/PhysRevE.65.066126.
4. ^ Philip Ball (2006). "Econophysics: Culture Crash". Nature 441 (7094): 686–688. Bibcode 2006Natur.441..686B. doi:10.1038/441686a. PMID 16760949.
5. ^ Jean-Philippe Bouchaud, Marc Potters (2003). Theory of Financial Risk and Derivative Pricing. Cambridge University Press.
6. ^ Enrico Scalas (2006). "The application of continuous-time random walks in finance and economics". Physica A 362 (2): 225–239. Bibcode 2006PhyA..362..225S. doi:10.1016/j.physa.2005.11.024.
7. ^ Y. Liu, P. Gopikrishnan, P. Cizeau, M. Meyer, C.-K. Peng, and H. E. Stanley (1999). "Statistical properties of the volatility of price fluctuations". Physical Review E 60 (2): 1390. doi:10.1103/PhysRevE.60.1390.
8. ^ M. H. R. Stanley, L. A. N. Amaral, S. V. Buldyrev, S. Havlin, H. Leschhorn, P. Maass, M. A. Salinger, H. E. Stanley (1996). "Scaling behaviour in the growth of companies". Nature 379 (6568): 804. doi:10.1038/379804a0. http://havlin.biu.ac.il/Publications.php?keyword=Scaling+behaviour+in+the+growth+of+companies&year=*&match=all.
9. ^ K. Yamasaki, L. Muchnik, S. Havlin, A. Bunde, and H.E. Stanley (2005). "Scaling and memory in volatility return intervals in financial markets". PNAS 102 (26): 9424–8. doi:10.1073/pnas.0502613102. PMC 1166612. PMID 15980152. http://havlin.biu.ac.il/Publications.php?keyword=Scaling+and+memory+in+volatility+return+intervals+in+financial+markets&year=*&match=all.

Further reading

Textbooks

• Rosario N. Mantegna, H. Eugene Stanley, An Introduction to Econophysics: Correlations and Complexity in Finance, Cambridge University Press (Cambridge, 1999)
• Vladimir N Pokrovskii, Econodynamics. The Theory of Social Production, Springer: Berlin (2011).
• Nicholas Georgescu-Roegen, The Entropy Law and the Economic Process, Harvard University Press: Cambridge, Massachusetts, 1971.
• Johannes Voit, The Statistical Mechanics of Financial Markets, Springer; 3rd edition (Berlin 2005)
• Sitabhra Sinha, Arnab Chatterjee, Anirban Chakraborti, Bikas K Chakrabarti. Econophysics: An Introduction, Wiley-VCH (2010)
• Jean-Philippe Bouchaud, Marc Potters, Theory of Financial Risk and Derivative Pricing, Cambridge University Press (2003)
• Bikas K Chakrabarti, Anirban Chakraborti, Arnab Chatterjee, Econophysics and Sociophysics : Trends and Perspectives, Wiley-VCH, Berlin (2006)
• Joseph McCauley, Dynamics of Markets, Econophysics and Finance, Cambridge University Press (Cambridge, 2004)
• Bertrand Roehner, Patterns of Speculation - A Study in Observational Econophysics, Cambridge University Press (Cambridge, 2002)
• Arnab Chatterjee, Sudhakar Yarlagadda, Bikas K Chakrabarti, Econophysics of Wealth Distributions, Springer-Verlag Italia (Milan, 2005)
• Keith Meyer, Extending and simulating the quantum binomial options pricing model, University of Manitoba (Winnipeg, 2009)
• Hagen Kleinert, Path Integrals in Quantum Mechanics, Statistics, Polymer Physics, and Financial Markets, 3rd edition, World Scientific (Singapore, 2004) also available online on physik.fu-berlin.de
• Emmanuel Farjoun and Moshe Machover, Laws of Chaos; A Probabilistic Approach to Political Economy, London: Verso, 1983. Probabilisticpoliticaleconomy.net
• Philip Mirowski, More Heat than Light - Economics as Social Physics, Physics as Nature's Economics, Cambridge University Press (Cambridge, UK, 1989)
• Bruna Ingrao and Giorgio Israel, The Invisible Hand - Economic Equilibrium in the History of Science, The MIT Press (Cambridge, MA, 1990).
• Didier Sornette, Why Stock Markets Crash: Critical Events in Complex Financial Systems, Princeton University Press (2004).
• Giovanni Naldi, Lorenzo Pareschi, Giuseppe Toscani, Mathematical modelling of collective behavior in socio-economic and life sciences, Birkhauser, (2010).
• Reiner Kümmel, The Second Law of Economics: Energy, Entropy, and the Origins of Wealth, Springer: Berlin (2011).

Journal articles, PhD theses

• Mauro Gallegati, Steve Keen, Thomas Lux and Paul Ormerod, Worrying Trends in Econophysics, Physica A 370, 1-6 (2006).
• Special issue of the Journal of Economic Dynamics and Control, January 2008: 'Applications of statistical physics in economics and finance'.
• W.K. Bertram, Modelling asset dynamics via an empirical investigation of Australian Stock Exchange data, PhD Thesis (2005).
• Mantegna, Rosario N.; Kertesz, Janos (2010). "Focus on Statistical Physics Modelling in Economics and Finance". New Journal of Physics. http://iopscience.iop.org/1367-2630/focus/Focus%20on%20Statistical%20Physics%20Modelling%20in%20Economics%20and%20Finance.
• Special issue of 'SCIENCE AND CULTURE', September–October 2010, Volume 76, N.9-10 'On 15 Years of Econophysics Research'.
• Tobias Preis; H. Eugene Stanley (2011). "Bubble trouble: Can a Law Describe Bubbles and Crashes in Financial Markets?". Physics World 24 (5): 29–32. http://tobiaspreis.de/publications/ps_pw_2011.pdf.
• Tobias Preis, T.; Johannes J. Schneider; H. Eugene Stanley (2011). "Switching processes in financial markets". Proceedings of the National Academy of Sciences 108 (19): 7674–7678. doi:10.1073/pnas.1019484108. http://www.pnas.org/cgi/doi/10.1073/pnas.1019484108.

Lectures

• Economic Fluctuations and Statistical Physics: Quantifying Extremely Rare and Much Less Rare Events, Eugene Stanley, Vicedolectures.net
• Applications of Statistical Physics to Understanding Complex Systems, Eugene Stanley, Videolectures.net
• Financial Bubbles, Real Estate Bubbles, Derivative Bubbles, and the Financial and Economic Crisis, Didier Sornette, Videolectures.net
• Financial crises and risk management, Didier Sornette, Videolectures.net
• Bubble trouble: how physics can quantify stock-market crashes, Tobias Preis, Physics World Online Lecture Series

Communication physics

Telecommunication is the transmission of information over significant distances to communicate. In earlier times, telecommunications involved the use of visual signals, such as beacons, smoke signals, semaphore telegraphs, signal flags, and optical heliographs, or audio messages via coded drumbeats, lung-blown horns, or sent by loud whistles, for example. In the modern age of electricity and electronics, telecommunications now also includes the use of electrical devices such as telegraphs, telephones, and teleprinters, the use of radio and microwave communications, as well as fiber optics and their associated electronics, plus the use of the orbiting satellites and the Internet.

A revolution in wireless telecommunications began in the first decade of the 20th century with pioneering developments in wireless radio communications by Nikola Tesla and Guglielmo Marconi. Marconi won the Nobel Prize in Physics in 1909 for his efforts. Other highly notable pioneering inventors and developers in the field of electrical and electronic telecommunications include Charles Wheatstone and Samuel Morse (telegraph), Alexander Graham Bell (telephone), Edwin Armstrong, and Lee de Forest (radio), as well as John Logie Baird and Philo Farnsworth (television).

The world's effective capacity to exchange information through two-way telecommunication networks grew from 281 petabytes of (optimally compressed) information in 1986, to 471petabytes in 1993, to 2.2 (optimally compressed) exabytes in 2000, and to 65 (optimally compressed) exabytes in 2007^[1]. This is the informational equivalent of 2 newspaper pages per person per day in 1986, and 6 entire newspapers per person per day by 2007.^[2] Given this growth, telecommunications play an increasingly important role in the world economy and the worldwide telecommunication industry's revenue was estimated to be $3.85 trillion in 2008.^[3] The service revenue of the global telecommunications industry was estimated to be $1.7 trillion in 2008, and is expected to touch $2.7 trillion by 2013.^[3]

Optical communication is any form of telecommunication that uses light as the transmission medium.

An optical communication system consists of a transmitter, which encodes a message into an optical signal, a channel, which carries the signal to its destination, and a receiver, which reproduces the message from the received optical signal.

A computer network, often simply referred to as a network, is a collection of hardware components and computers interconnected by communication channels that allow sharing of resources and information.^[1]

Networks may be classified according to a wide variety of characteristics such as the medium used to transport the data, communications protocol used, scale, topology, and organizational scope.

The rules and data formats for exchanging information in a computer network are defined bycommunications protocols. Well-known communications protocols are Ethernet, a hardware and Link Layer standard that is ubiquitous in local area networks, and the Internet Protocol Suite, which defines a set of protocols for internetworking, i.e. for data communication between multiple networks, as well as host-to-host data transfer, and application-specific data transmission formats.

Computer networking is sometimes considered a sub-discipline of electrical engineering,telecommunications, computer science, information technology or computer engineering, since it relies upon the theoretical and practical application of these disciplines.

See also

Telecommunication portal

• Busy Override
• Outline of telecommunication
• Telecoms resilience
• Wavelength-division multiplexing(WDM)
• Wired communication
• Active networks
• Nanoscale networks
• Dual-tone multi-frequency signaling
• Push-button telephone
• Telecommunications Industry Association

Physics of Digital imaging

Digital imaging or digital image acquisition is the creation of digital images, typically from a physical scene. The term is often assumed to imply or include the processing, compression, storage, printing, and display of such images. The most usual method is by digital photography with a digital camera but other methods are also employed.

Digital imaging was developed in the 1960s and 1970s, largely to avoid the operational weaknesses of film cameras, for scientific and military missions including the KH-11 program. As digital technology became cheaper in later decades it replaced the old film methods for many purposes.

Methods

A digital photograph may be created directly from a physical scene by a camera or similar device. Alternatively, a digital image may be obtained from another image in an analog medium, such as photographs, photographic film, or printed paper, by an image scanner or similar device. Many technical images—such as those acquired with tomographic equipment, side-scan sonar, or radio telescopes—are actually obtained by complex processing of non-image data. Weather radar maps as seen on television news are a commonplace example. The digitalization of analog real-world data is known as digitizing, and involves sampling (discretization) and quantization.

Finally, a digital image can also be computed from a geometric model or mathematical formula. In this case the name image synthesis is more appropriate, and it is more often known as rendering.

Digital image authentication is an issue ^[1] for the providers and producers of digital images such as health care organizations, law enforcement agencies and insurance companies. There are methods emerging in forensic photography to analyze a digital image and determine if it has been altered.

See also

• Digital image processing
• Digital photography
• Dynamic imaging
• Image editing
• Image retrieval
• Graphics file format
• Graphic image development
• Society for Imaging Science and Technology, (IS&T)
• Film recorder
• Photoplotter

References

1. ^ Digital image authentication for evidence

External links

• Cornell University. Digital imaging tutorial
• Digital Imaging Definitions/Glossary/Terms. Digital Imaging Definitions
• Dartmouth, Hany Farid. Digital Image Forensics

Computational physics

Computational physics is the study and implementation of numerical algorithms to solve problems in physics for which a quantitative theory already exists. It is often regarded as a subdiscipline of theoretical physics but some consider it an intermediate branch between theoretical and experimental physics.

Physicists often have a very precise mathematical theory describing how a system will behave. Unfortunately, it is often the case that solving the theory's equations ab initio in order to produce a useful prediction is not practical. This is especially true with quantum mechanics, where only a handful of simple models admit closed-form, analytic solutions. In cases where the equations can only be solved approximately, computational methods are often used.

Applications of computational physics

Computation now represents an essential component of modern research in accelerator physics, astrophysics, fluid mechanics, lattice field theory/lattice gauge theory (especially lattice quantum chromodynamics), plasma physics (see plasma modeling), solid state physics and soft condensed matter physics. Computational solid state physics, for example, uses density functional theoryto calculate properties of solids, a method similar to that used by chemists to study molecules.

As these topics are explored, many more general numerical and mathematical problems are encountered in the process of calculating physical properties of the modeled systems. These include, but are not limited to

• Solving differential equations
• Evaluating integrals
• Stochastic methods, especially Monte Carlo methods
• Specialized partial differential equation methods, for example the finite difference method and the finite element method
• The matrix eigenvalue problem – the problem of finding eigenvalues of very large matrices, and their corresponding eigenvectors (eigenstates in quantum physics)
• The pseudo-spectral method

Computational physics also encompasses the tuning of the software/hardware structure to solve problems. Approaches to solving the problems are often very demanding in terms of processing power and/or memory requests.

See also

• Molecular dynamics
• Computational fluid dynamics
• Computational Magnetohydrodynamics
• DCOMP Division of Computational Physics of the American Physical Society
• Important publications in computational physics
• Computational Science
• Mathematical physics
• Open Source Physics, computational physics libraries and pedagogical tools
• Plasma modeling

External links

* C20 IUPAP Commission on Computational Physics
* APS DCOMP
* IoP CPG (UK)
* SciDAC: Scientific Discovery through Advanced Computing
* Open Source Physics
* SCINET Scientific Software Framework

Applied Physics

Applied physics is a general term for physics which is intended for a particular technological or practical use.^[1] It is usually considered as a bridge or a connection between "pure" physics and engineering.^[2]

"Applied" is distinguished from "pure" by a subtle combination of factors such as the motivation and attitude of researchers and the nature of the relationship to the technology or science that may be affected by the work.^[3] It usually differs from engineering in that an applied physicist may not be designing something in particular, but rather is using physics or conducting physics research with the aim of developing new technologies or solving an engineering problem. This approach is similar to that of applied mathematics. In other words, applied physics is rooted in the fundamental truths and basic concepts of the physical sciences but is concerned with the utilization of these scientific principles in practical devices and systems.^[4]

Applied physicists can also be interested in the use of physics for scientific research. For instance, people working on accelerator physics seek to build better accelerators for research in theoretical physics.

Journals by publisher

• American Institute of Physics
  • Journal of Applied Physics
  • Applied Physics Letters
• Japan Society of Applied Physics
  • Japanese Journal of Applied Physics
  • Applied Physics Express
• IOP Publishing
  • Journal of Physics D: Applied Physics
• Springer
  • Applied Physics (journal)
  • Applied Physics A
  • Applied Physics B

Institutions/organizations

• International Union of Pure and Applied Physics
• Harvard School of Engineering and Applied Sciences
• Applied Physics Laboratory, John Hopkins University
• School of Pure and Applied Physics, Mahatma Gandhi University
• Institute of Applied Physics and Computational Mathematics, Beijing, China
• Institute of Applied Physics, National Academy of Sciences of Ukraine

References

1. ^ "Applied Physics". ArticleWorld. http://www.articleworld.org/index.php/Applied_physics. Retrieved 10 September 2011.
2. ^ "Applied Physics at Caltech - Overview". Caltech. http://www.aph.caltech.edu/overview.html. Retrieved 10 September 2011.
3. ^ "General Information on Applied Physics". Stanford Department of Applied Physics. Archived from the original on 7 March 2007. http://web.archive.org/web/20070307121331/http://www.stanford.edu/dept/app-physics/general/.
4. ^ "Department of Applied Physics / Major in Pure and Applied Physics". Waseda University. http://www.sci.waseda.ac.jp/english/global/faculty/advanced/index03.html. Retrieved 10 September 2011.