Bikas K Chakrabarti

Santiniketan

October, 2020

Paris

June, 2010

For a publicly edited version of my CV, see the Wikipedia entry.

Greetings! This is Bikas. My brief CV is as follows: Born in Dec. 1952, in Calcutta, to (Late) Bimal K. Chakrabarti and (Late) Pratima Chakrabarti. Got my Ph. D. Degree from Calcutta University in 1979. After that, I was post-doctoral fellow at Department of Theoretical Physics, University of Oxford and then at Institute for Theoretical Physics, University of Cologne. I joined Saha Institute of Nuclear Physics {SINP) as faculty in September 1983. A former director of SINP and at present INSA Scientist at SINP (2021-), also Honorary Visiting Professor of economics (2007-) at the Indian Statistical Institute. Emeritus Professor of SINP and of S.N. Bose National Centre for Basic Sciences. Was awarded S. S. Bhatnagar Prize (CSIR, India) in 1997 and was J. C. Bose National Fellow (DST, India) for 2011-2020. Married to Mrs. Kaberi Chakrabarti. Have two sons: Kalyan Sundar Chakrabarti and Anindya Sundar Chakrabarti.

I have professional interest in statistical physics, condensed matter physics, quantum annealing & computation, and their application to social sciences. See my papers, reviews or books page.

RESEARCH:

Authored/co-authored more than 200 papers in refereed journals, 17 reviews [1 in Encyclopedia of Condensed Matter Physics, 1 in Entropy, 1 in Eur. Phys. J. B, 3 in Frontiers in Physics, 3 in Phil. Trans. Royal Soc, A, 2 in Phys. Rep. & 4 in Rev. Mod. Phys.] and 10 books [3 from Cambridge Univ. Press, 2 from Oxford Univ. Press, 3 from Springer & 2 from Wiley-VCH]. For citations etc., see Google Scholar and ResearchGate.

A FEW REPRESENTATIVE REVIEW PAPERS & BOOKS:

Review Papers :

&

Books :

Supervised Ph.D. theses of: ♦S. S. Manna (1987) ♦A. K. Roy (1988) ♦P. Ray (1989) ♦M. Ghosh (1992) ♦P. Raychaudhuri (Sen) (1993) * K. Barat (1995) ♦M. Acharyya (1996) ♦A. Dutta (2000) ["Unquenched: A memoir honoring Amit Dutta (2024)" (Open Access)] ♦P. Bhattacharyya (2000) ♦A. Misra (2001) ♦A. Chakraborti (2003) ♦S. Pradhan (2005) ♦A. Chatterjee (2008) ♦A. Das (2008) ♦A. Ghosh (2014) ♦S. Biswas (2015) ♦A. Rajak (Jointly with A. Basu; 2016) ♦S. Mukherjee (2020) ♦ S. Banerjee (Jointly with Late M. Mitra, Economics, ISI; 2024).

Journal Editorial Board member of: ♦European Physical Journal B (past) ♦Frontiers in Physics (present) ♦Indian Journal of Physics (present) ♦Journal of Economic Interaction and Coordination (present) ♦Journal of Magnetism & Magnetic Materials (past) ♦Natural Science (past) ♦Pramana -- Journal of Physics (past) ♦Scientific Reports (past) ♦SciPost (past)

Book Series Editor of: ♦ Physics of Society: Econophysics & Sociophysics (with M. Gallegati, A. Kirman & H. E. Stanley) of Cambridge University Press (Past) ♦ Cambridge Elements in Econophysics (with R N Mantegna, M Gallegati & I Vodenska) of Cambridge University Press (present) ♦ Statistical Physics of Fracture & Brekdown (with P Ray), Wiley: I, II, III, IV (past) ♦ New Economic Windows series, Springer (present)

AWARDS & DISTINCTIONS:

Awards, Fellowships, etc

- Young Scientist Award of Indian National Science Academy (1984)
- Professeur Invité, University of Paris, Lab-PMTM, CNRS (1988)
- Shanti Swarup Bhatnagar Award, CSIR India (1997)
- Fellow, Indian Academy of Sciences, Bangalore (Elected, 1997)
- Fellow, Indian National Science Academy, New Delhi (Elected, 2003)
- Honorary Visiting Professor, Indian Statistical Institute, Kolkata (2007- )
- Outstanding Referee Award of the American Physical Society (2010)
- Professeur Invité, École Centrale Paris (2010)
- J C Bose National Fellow, DST India (2011-'20)
- Executive Editor, European Physical Journal B (2016-2018)
- Honorary Emeritus Professor, S. N. Bose National Centre for Basic Sciences, Kolkata (2018- 2021)
- INSA Scientist, Indian National Science Academy (2021 -)

Peer Recognition/Appreciation: Few representative examples

A) Quantum Annealing:

♦ "Idea of Quantum annealing” due to "tunnelling through infinitely high classical barriers separating infinitely many metastable states was indeed put forward even earlier, in Ray, Charabarti & Chakrabarti, Phys. Rev. B (1989)"[4th para, Introduction], Journal of Physics A Topical Review by Erio Tosatti et al. from ICTP Trieste (2008)

♦ In "Quantum Glasses", "where barriers to relaxation are tall and narrow, quantum mechanics can enhance the ability to traverse the free energy surface (Ray, Charabarti & Chakrabarti, Phys. Rev. B (1989))” [3rd sentence], Physical Review Letters by Gabriel Aeppli (ETH Zurich), Thomas Felix Rosenbaum (Caltech), et al. (2008)

♦ "The phenomenon of quantum tunneling suggests that it can be more efficient to explore the state space quantum mechanically in a quantum annealer [Ray, Chakrabarti & Chakrabarti, Phys. Re. B (1989); Finnila et al., Chem. Phys. Letts. (1994); Kadowaki & Nishimori, Phys. Rev. E (1998)]"[ 2nd para, 1st sentence], Nature Physics on Evidence for quantum annealing with more than one hundred qubits by Sergio Boixo, Daniel A. Lidar (Univ. Southern California) , John M. Martinis (Univ. California, California), Matthias Troyer (ETH Zurich), et al., (2014)

♦ "Quantum annealing [Ray, Chakrabarti & Chakrabarti, Physical Review B (1989); Finnila et al., Chemical Physics Letters (1994); Kadowaki & Nishimori, Physical Review E (1998); Farhi et al., Science (2001); Das & Chakrabarti, Reviews of Modern Physics (2008)] uses quantum tunneling instead of thermal excitations to escape from local minima, which can be advantageous in systems with tall but narrow barriers, which are easier to tunnel through than to thermally climb over." [see 2nd paragraph], write Matthias Troyer (ETH Zurich) et al., in their Science paper on Quantum versus classical annealing of Ising spin glasses (Open Access; 2015)

♦ "Quantum annealing [Finnila et al. Chem. Phys. Letts. (1994); Kadowaki & Nishimori, Phys. Rev. E (1998); Farhi et al., arXiv (2002); Brooke et al., Science (1999); Santoro et al., Science (2002)] is a technique inspired by classical simulated annealing [Ray, Chakrabarti & Chakrabarti, Phys. Rev. B (1989)] that aims to take advantage of quantum tunnelling." [1st sentence], Nature Communication on Computational multiqubit tunnelling in programmabnle quantum annealers by Sergio Boixo (Google California), Hartmut Neven (Google California) et al. (Open Access, 2016)

♦ "Quantum annealing aims at finding low-energy configurations ... by a controlled quantum adiabatic evolution ... to escape local minima through multiple tunneling events [Ray, Charabarti & Chakrabarti, Phys. Rev. B (1989); ...; Das & Chakrabarti, Rev. Mod. Phys. (2008)]" [1st sentence] ... [It can] lead to extremely powerful alternative computational devices [3rd sentence], Proc. Nat. Acad. Sc. on Efficiency of quantum vs. classical annealing innonconvex learning problemsby Riccardo Zecchina et al., ICTP Trieste (Open Access; 2018)

♦ "Earliest work in laying the foundation of Quantum Annealing [Ray, Chakrabarti & Chakrabarti, Phys. Rev. B (1989)], showing that quantum fluctuations can increase the ergodicity in a spin-glass model, by tunneling between ‘trapping’ minima, separated by narrow potential barriers." [3rd Paragraph of Introduction], ... Das & Chakrabarti [Rev. Mod. Phys., 2008] gives clear picture of fundamental physical properties and mechanism." [see 5th paragraph of the Introduction], Journal of Physics B on Unraveling the origin of higher success probabilities in quantum annealing versus semi-classical annealing by Helmut Ritsch et al., Inst. Theor. Phys., Univ. Innsbruck (Open Access; 2022)

♦ "Adiabatic Quantum Computing [Farhi et al., Science (2001), Das & Chakrabarti, Rev. Mod. Phys. (2008)] "[see Abstract] ... "has attracted intense interest [Das & Chakrabarti, Rev. Mod. Phys. (2008), Farhi et al., Science (2001)] owing to its potential speedup over classical algorithms" [see 2nd paragraph of the paper], Proc. Nat. Acad. Sc. by Frank Wilczek et al. from MIT & elsewhere (Open Access; 2023)

♦ "The quantum annealing algorithm was proposed in the 1990s with the aim of providing a fast heuristic for solving unconstrained combinatorial optimization problems [Kadowaki & Nishimori, Phys. Rev. E (1998); Ray, Chakrabarti & Chakrabarti, Phys. Rev. B (1989); Finnila et al., Chem. Phys. Lett. (1994); Farhi et al., Science (2001); Santoro et al., Science (2002)]." [Introduction, 1st line] ... "In the thirty years since its inception the quantum annealing algorithm has generated significant research interest, including the development of specialized quantum computing hardware for executing this algorithm at interesting scales. The subject area continues to be a vibrant research topic with significant ongoing efforts in improving the performance quantum annealing hardware and developing impactful demonstrations on long-standing optimization applications." [Conclusion], write Carleton Coffrin & Marc Vuffray (LANL, Los Alamos) in Quantum Annealing, Encyclopedia of Optimization, Springer (2024)

♦ "To overcome this difficulty [of exponentially growing time scales in NP-hard problems], quantum annealing and other inspired methods have garnered increasing interest because of their possession of quantum attributes that could offer potential solutions to the challenges inherent in combinatorial optimization problems [Ray, Chakrabarti & Chakrabarti, Phys. Rev. B (1989); Finnila et al., Chem. Phys. Lett. (1994); Kadowaki & Nishimori, Phys. Rev. E (1998); Farhi et al., Science (2001); Ushijima-Mwesigwa, et al., ACM Quantum Comput. (2021); Zeng et al., Commun. Phys. (2024); Mohseni, Mcmahon & Byrnes, Nat. Rev. Phys. (2022)]." [Introduction, 1st para] ... "Several quantum Ising machines have been proposed to achieve this purpose. This includes D-wave systems [Johnson, et al., Nature (2011); King et al., Nature (2018)] that utilize superconducting quantum interferometers [Kadowaki & Nishimori, Phys. Rev. E (1998); Brooke et al., Science (1999), Das & Chakrabarti, Rev. Mod. Phys, (2008)] to execute quantum annealing for adiabatic quantum computation [Farhi et al., Science (2001); Albash & Lidar, Rev. Mod. Phys. (2018)]." [Introduction, 2nd para], write Yoshihisa Yamamoto (Stanford University) et al., in their IEEE Access (2024) (open Access)

♦ "Adiabatic algorithms [Das & Chakrabarti, Rev. Mod. Phys. (2008)]: This is a heuristic algorithm based on physical insight which often works well but for which no convergence guarantees exist", write Ignacio Cirac et al., in their perspective review on Second Quantum Revolution: Quantum Computing & Information [Book: EPS Grand Challenges: Physics in Horizon 2050, Chapter 7.2.1, page 7-11](Open Access, 2024)

♦ "Quantum annealing [Finnila et al., Chem. Phys. Lett. (1994); Kadowaki & Nishimori, Phys. Rev. E (1998); Das & Chakrabarti, Rev. Mod. Phys, (2008)]; Chakrabarti et al., Phil. Trans. Royal Soc. A (2023)] is a metaheuristic that can obtain good solutions to combinatorial optimization problems eﬃciently. Combinatorial optimization problems are typically translated into the ground-state search of the Ising model. Implementing metaheuristics in hardware has attracted considerable attention since quantum annealers became available [Johnson et al., Nature (2011)]." (2nd paragraph) ... "... adiabatic theorem [Messiah, Quantum Mechanics, Dover (1958); Tanaka, Tamura & Chakrabarti, Quantum Spin Glasses, Annealing and Computation, Cambridge Univ. Press (2017)]..." (7th paragraph), write Tadashi Kadowaki (DENSO Corp., Tokyo), Shu Tanaka (Keio Univ., Tokyo), et al. in their J. Phys. Soc. Japan [Letter] (2025)

♦"One prominent analog quantum algorithm is quantum annealing [Das & Chakrabarti, Rev. Mod. Phys, (2008)]; Albash & Lidar, Rev. Mod. Phys. (2018); Hauke et al., Rep. Prog. Phys. (2020)], where a system goes through one or multiple Landau-Zener tunnelings to reach some many-body quantum state of interest, relevant to quantum simulation or optimization tasks. The role of dissipation in quantum annealing is still largely an open question ... " (2nd paragraph), write Daniel Lidar (Univ. Southern California) et al., in their Nature Communication (Open Access, 2025)

♦ "The Quantum Annealer built by D-Wave, known as Advantage, is currently the largest quantum computer in the world, featuring a topology called ‘Pegasus.’ This groundbreaking system opens new possibilities for solving highly complex problems. (Abstract; 1st two lines) .... Quantum annealing ... can be viewed as a variant of the well-known simulated annealing metaheuristic [Ayanzadeh, Halem & Finin, Sci. Rep., (May 2020)]. The concept of integrating a model of quantum annealing into a heuristic optimization framework has been independently proposed by several researchers, including Apolloni, Crvalho & de Falco [Stochastic Processes Appl. (Dec. 1989)], Ray, Chakrabarti & Chakrabarti [Phys. Rev. B (Jun. 1989)] and Kadowaki & Nishimori [Phys. Rev. E (Nov. 1998)] ." (Introduction, 6th paragraph), ... "collaborations with institutions like NASA and Google underscored the practical applications of D-Wave’s quantum solutions, especially in fields such as machine learning and large-scale optimization [Ghosh & Mukherjee, arXiv:1310.1339 (2013); Lucas, et al., Proc. Interservice Edu. Conf. (2013); Lidar, New Directions in the Quantum Control Landscape (2013)]" (Sec. II D-Wave Quantum Machines, 4th para, last line), write Manuel Mazzara et al., (Research Center of the Artificial Intelligence Institute of Innopolis University, Russia) in their IEEE Access (Open Access, 2025)

♦ "Noise-resilience of analog quantum algorithms: Analog quantum algorithms replace the sequence of gates by an explicitly time-dependent Hamiltonian. This describes computing by quantum annealing and protocols for quantum simulation [Das & Chakrabarti, Rev. Mod. Phys. (2008)]." , write Luis Pedro García-Pintos et al. (Univ. Oxford, Oxford; Harvard Univ., Cambridge; Univ. Maryland, Maryland; Los Alamos National Laboratory, Los Alamos; NASA Ames Laboratory, California; NIST, Maryland) in their Reports on Progress in Physics (Open Access, 2025)

♦ "Quantum annealing offers a way of solving optimisation problems on quantum devices [Das & Chakrabarti, Rev. Mod. Phys. (2008); Albash & Lidar, Rev. Mod. Phys. (2018)]. Quantum annealers address combinatorial problems with a discrete solution space. The solution to a given problem is encoded in the ground state of an Ising Hamiltonian [(Das & Chakrabarti, Rev. Mod. Phys. (2008)]. This Hamiltonian is then realised on a quantum device, the annealer, and its ground state prepared by slowly steering an initial state towards it."(Sec. 3.3), write Jeanette M. Lorenz (Univ. Munich), Veronika Eyring (German Aerospace Center & Univ. Bremen), et al. in their arXiv paper (Open Access, 2025)

♦ "... the adiabatic algorithm is often seen as a prerogative of analog simulators, and not circuit-based quantum computers, with the name of quantum annealing [Das & Chakrabarti, Rev. Mod. Phys. (2008); Ray, Chakrabarti, Chakrabarti, Phys. Rev. B (1989); Kadowaki & Nishimori, Phys. Rev. E (1998); Apolloni, Carvalho & De Falco, Stochastic Process. Appl. (1989); Das & Chakrabarti, Quantum Annealing & Related Optimization Methods (Springer, 2005); Santoro & Tosatti, J. Phys. A (2006)]." write Granet , Ghanem & Dreyer (Quantinuum, Munich) in their Phys. Rev. A (2025)

♦ "With the advent of new generations of quantum annnealers from D-Wave Systems Inc. comprising more than 5000 qubits, a promising approach is the use of quantum annealing (QA) ... . Inspired by the cooling of physical systems, QA uses quantum fluctuations caused by a transverse field to navigate the energy landscape of the spin system [Rajak et al., Phil. Trans. Royal Soc. (2023)]. If the quantum evolution is performed adiabatically, the system arrives in the ground state of the spin glass." (Introduction, 2nd para) .... This [spin glass] phase is characterized by the existence of exponentially many low-energy local minima, where a local minimum refers to a state for which flipping a single (or a few) spins always increases the energy. The energy spectrum of these local minima typically has small gaps, with macroscopic high and wide energy barriers separating them [Mukherjee & Chakrabarti, J. Phys. Soc. Jap. (2019)]. " (Sec. II), write Kristel Michielsen et al. (Supercomputing Centre, Forschungszentrum Julich) in their arXiv paper (Open Access, 2025).

♦ "Quantum processing units have garnered significant attention and investment for their potential to provide transformative speed-ups on complex problems [Shor, in Proc. 35th Ann. Symp. on Foundations of Computer Sc. (IEEE, New York, 1994); Grover, Phys. Rev. Lett. (1997)]. While current hardware capabilities are still maturing, this has spurred focused research on small-scale optimization problems, laying a strong foundation for future scalability. Variational approaches have emerged as a powerful framework for leveraging quantum systems, guiding their dynamics toward final states that encode problem solutions [Das & Chakrabarti, Rev. Mod. Phys. (2008)]" (Introduction, first three sentences), write Leclerc (PASQAL, Paris) et al., in their Phys. Rev. A (2025)

♦ ”Quantum annealing (QA) [Kadwaki & Nishimori, Phys. Rev. E (1998); Farhi et al., arXiv (2000); Farhi et al., Science (2001); Ray, Chakrabarti & Chakrabarti, Phys. Rev. B (1989); Finnila et al., Chem. Phys. Lett. (1994); Aspuru-Guzik et al., Science (2005)] is a promising method for obtaining a ground state of a problem Hamiltonian. In QA, the ground state of a simple “driver” Hamiltonian is prepared, and a time-dependent Hamiltonian which changes from the driver Hamiltonian to the problem Hamiltonian is employed. If an adiabatic condition is satisfied, there is a theoretical guarantee that the ground state of the problem Hamiltonian can be obtained from such a unitary dynamics." (Introduction, 1st 3 sentences), write Yoshida et al. (Tokyo Univ. Sc., Tokyo, Osaka Univ., Osaka, Chuo Univ., Tokyo) in their arXiv preprint (Open Access, 2025)

♦”Indeed, it has been established that quantum annealing shows convergence to the optimal (ground) state with larger probability than simulated annealing in a variety of cases if the same annealing schedule is used [Kadowaki & Nishimori, Phys. Rev. E (1998); Brooke et al., Science (1999); Farhi et al., arXiv (2000); Martonak, Santoro & Tosatti, Phys. Rev. B (2002); Kadowaki, arXiv (2002); Farhi, Goldstone & Gutmann, arXiv (2002); Santoro et al., Science (2002); Santoro & Tosatti, J. Phys. A (2006); Das & Chakrabarti, Rev. Mod. Phys. (2008)]. The intuition for the enhanced performance is that quantum ﬂuctuations allow for tunneling events through particularly spiky peaks of the energy landscape [Ray, Chakrabarti & Chakrabarti, Phys. Rev. B (1989); Denchev et al., Phys. Rev. X (2016)], which in contrast are not possible when using classical simulated annealing." (Introduction, 2nd para), write Schlömer (Munich Univ, Munich) & Sachdev ( Harvard Univ., Harvard, Cambridge), in their Annals of Physics (2025)

♦ First five sentences of the paper by Garcia-de-Andoin et al. (Univ. Basque Country, Leioa; Max-Planck-Institut für Quantenoptik, Garching; Univ. Vienna, Vienna; ...) reads: "Quantum computing was first introduced as a way of simulating quantum systems with a controllable device [Benioff, J. Stat. Phys. (1980), Feynman, Int. J. Theor. Phys. (1982)]. There are two main protocols to control these quantum systems. On the one hand, analog quantum computing acts on the system by a continuous variation of some parameters [Das & Chakrabarti, Rev. Mod. Phys. (2008)]. Its main advantage is the robustness against noise at the cost of a limited set of control actions over the system. On the other hand, digital quantum computing acts on a state with a series of discrete unitary evolutions or gates [Deutsch, Barenco & Ekert, Proc. Roy. Soc. Lond. A (1995 )]" (arXiv, 2025)

♦ "The Ising model [Barahona, J. Phys. A (1982); Chakrabarti, Phys. Rev. B (1981)] ... can be used to elegantly express classical optimization problems [Chakrabarti, Phys. Rev. B (1981)], including all the textbook NP-hard problems [Lucas, Front. Phys. (2014)]. .... Quantum Annealing [is] a physical process that attempts to implement algorithms for Adiabatic Quantum Computation (AQC), first proposed by Chakrabarti [1981] [Chakrabarti, Phys. Rev. B (1981] , with many subsequent developments [Brooke et al. , Science (1999); Finnila et al., Chem. Phys. Lett. (1994); Kadowaki & Nishimori, Phys. Rev. E (1998); Ray, Chakrabarti & Chakrabarti, Phys. Rev. B (1989) ; Wu et al., Phs. Rev. Lett. (1991)]. It incorporates algorithms and hardware designed to solve computational problems via quantum evolution towards the ground states of final Hamiltonians that encode classical optimization problems, without necessarily insisting on universality or adiabaticity .... The idea is to think of NP-complete problems as Energy Constraint Computation problems, with mathematical formalism in the Ising model, and solve them in AQC or quantum annealing [Das and Chakrabarti, Rev. Mod. Phys. (2028); Farhi et al., Science (2001)].", write (in secs. 3 & 7) Li et al. (Iowa State Univ., Univ. Kansas, Indiana Univ. ) in their Proc. ACM Program. Lang. (Open Access, 2025)

♦ "The idea of quantum annealing traces its origins from the pioneering works in [Kadowaki & Nishimori, Phys. Rev. E (1998); Ray, Chakrabarti & Chakrabarti, Phys. Rev. B (1989)], where quantum fluctuations have been introduced into the simulated annealing process with the aim to converge to the global minimum quicker than classical simulated annealing (see [Das & Chakrabarti, Rev. Mod. Phys. (2008); Albash & Lidar, Rev. Mod. Phys. (2018)] for comprehensive reviews on quantum annealing).", write (Introduction, 3rd para) Nutricati et al. (Univ. College London; Univ. Oxford) in their Quantum Sci. Technol. (Open Access, 2025).

♦ "The goal of exploiting quantum effects to solve computational problems more efficiently than classical devices has led to the fast-paced development of quantum technologies. A remarkably simple, yet potentially powerful, approach is quantum annealing [Rajak et al. , Phil. Trans. R. Soc. A (2023)], which uses adiabatic quantum evolution to tackle problems in combinatorial optimization.", write (Introduction, 1st two sentences) Munoz-Arias & Poggi (Univ. de Sherbrooke, Sherbrooke; Univ. Strathclyde, Glasgow; Sandia Nat. Lab., California) in their Quantum Sci. Technol. (Open Access, 2025).

B) Econophysics:

♦“Influential” & “Elegant” papers from “Kolkata School”, Reviews of Modern Physics by Victor Yakovenko (Dept. Phys., Univ. Maryland) & J. Barkley Rosser (Dept. Econ., James Madison Univ.) [see pp. 1705, 1711 & throughout] , Rev. Mod. Phys. (2009)

♦ See Wikipedia entry on Kinetic exchange models of markets for contributions in modelling the income and wealth distributions (hosted in 2010)

♦"Father of Econophysics", Thesis by Christophe Schinckus, , Dept. History and Philosophy of Science, University of Cambridge [see pp. 15,16] (Open Access; 2018)

♦ "A number of disciplines have wholeheartedly embraced mathematical tools and models from physics ... For example, the physicist Bikas Chakrabarti has applied the kinetic theory of gas to models of markets ... His is not a one-off example; the list ... includes luminaries such as Jan Tinbergen, the first ever recipient of the Nobel Prize in economics.", write Mariza Uzunova Dang et al. in their Oxford IB book (in p. 211) on Theory of Knowledge, Oxford Univ. Press (2020)

♦"Historic conference in Kolkata (India, 1995)": One of the six landmark events in the last one hundred and twenty years of physics applications "in economics and finance [since] the doctoral dissertation by Louis Bachelier in 1900", Entropy Spl. Issue on Econophysics Editorial by Ryszard Kutner, Christophe Schinckus & H. Eugene Stanley [see Fig. 2 & its caption] (Open Access; 2022)

♦ "Foundational paper": "25 years of random asset exchange modeling", European Physical Journal B Topical Review, by Max Greenberg (Dept. Economics, University of Massachusetts Amherst) & H. Oliver Gao (Dept. Systems Engineering, Cornell University) [see Abstract & throughout] , European Physical Journal B (View-only Open Access; 2024)

♦"During the 20th century, several mathematicians and economists have applied kinetic theory to economics: From Josef Steindl (1965) ..., to Benoit Mandelbrot (1960, 1963) ..., to Emmanuel Farjoun & Moshé Machover (1983) ..., to Chakrabarti et al. (2006) of the Calcutta Group, who applied kinetic model to income & wealth distribution.", writes Gianfranco Tusset, Dept. Economics, University of Padua, in his paper [see Section 2 , paragraph 2], in European Journal of History of Economic Thought (2024)

♦ "Random exchange kinetic models are widely employed to describe the conservative dynamics of large interacting systems. Due to their simplicity and generality, they are quite popular in several fields, from statistical mechanics to biophysics and economics. ... an H -theorem is proven, ensuring that such equilibrium condition is reached by a monotonic increase in the Boltzmann entropy." (Abstract) . ... [These models] "have been used in several contexts, from biology ... or the dynamics in flocks ... to financial applications (see [Dragulescu & Yakovenko, Euro. Phys. J. B (2000); Chakraborti & Chakrabarti , Euro. Phys. J. B (2000); Chatterjee , Chakrabarti & Manna, Phys. Scr. (2003); Greenberg & Gao, Euro. Phys. J. B (2024)]." (Introduction), write Angelo Vulpiani (Univ. Rome) et al, in their Journal of Statistical Mechanics: Theory & Experiment (Open Access; 2025)

♦ Kolkata Game: See Wikipedia entry on Kolkata Paise Restaurant Problem for game theoretic modelling of decentralized social optimizations (hosted 2025)

♦Kolkata Index for social inequality in a) CC model, Cui et al., EPL (2023), b) Wright Capitalism model, Borba et al., Physica A (2025), c) Extnded CC model, Lin & Cui, Journal of Simulation (2025)

♦First time in India, Econophysics course was floated in the academic year (2024 -2025) for ISI-Calcutta MS students in Quantitative Economics: Of the 16 students, 6 students finally took the course, offered by Sitabhra Sinha of IMSc (came every month for a week for the last six months, spending his own Project money!) and Bikas K Chakrabarti (SINP, ISICAL). Parongama Sen (Calcutta Univ.) was Course Moderator. A Forwarded message from Dean of Studies, ISI Kolkata :

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From: V Gaurav <vgauravkumar9445@gmail.com> Date: Fri, May 16, 2025 at 1:34 PM Subject: Feedback for econophysics course To: Dean of Studies <dean@isical.ac.in>, DEAN'S OFFICE ISI <deanofficeisi@gmail.com> Cc: Anuj Bhowmik (Head, ERU) <anujbhowmik09@gmail.com>, samarjit <samarjit@isical.ac.in>, <samarjitd@gmail.com>

Dear Sir, We, a group of six students from the MSQE 2 batch who enrolled in the Econophysics elective this semester, would like to express our sincere appreciation for the course and the instructors involved. The course was intellectually stimulating, well-structured, and provided us with a unique interdisciplinary perspective that connected economics with concepts from statistical physics and complex systems.

The faculty members were exceptional in their teaching and mentorship, making intricate topics accessible and encouraging us to think beyond traditional economic frameworks. The lectures were engaging and thought-provoking, and the course has significantly enriched our academic journey. ... Thank you for providing us with such a valuable opportunity to engage with this course.

Warm regards: V Gaurav, Himank Aggarwal, M kruthik, Neelarka Roy, Vivekananda Varma Allam, Ayan chakrabarty

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♦ "Econophysics: An Introduction (Sinha, Chatterjee, Chakraborti & Chakrabarti, Wiley, 2010)” has been the only suggested textbook for the Econophysics course (Teacher: Diego Garlaschelli) offered for last one and half a decade by the Leiden University physics department (where the inaugural-year [1969] economics Nobel Laureate Jan Tinbergen did undergraduate study and Ph. D. research in statistical physics of economics under Paul Ehrenfest): Course started in 2012 [See Prospectus for 2012-2013, for 2013-2014, for 2014-2015, for 2015-2016, for 2016-2017, for 2017-2018, for 2018-2019, for 2019-2020, for 2020-2021, for 2021-2022, for 2022-2023, for 2023-2024, for 2024-2025, and is contunuing till date; see Prospectus for 2025-2026].

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CONTACT:

E-mails:

bikask.chakrabarti[at]saha.ac.in

bikask.chakrabarti[at]gmail.com