Econophysics and sociophysics: their milestones & challenges. Part 2

Kutner, Ryszard

Artykuł - szczegóły

Tytuł artykułu

Econophysics and sociophysics: their milestones & challenges. Part 2

Autorzy

Kutner Ryszard

Treść / Zawartość

Pełne teksty:

Pobierz

Identyfikatory

Warianty tytułu

Języki publikacji

Abstrakty

We continue to discuss the milestones of econophysics and sociophysics. We chose them in the context of the challenges posed by contemporary socio-economic reality. We indicate their role in building research areas in econophysics and sociophysics. This part is devoted primarily to complexity, incredibly complex networks, and phase transitions, particularly critical phenomena and processes, agent-based modeling, risk issues in the context of financial markets, and elements of modern sociophysics.

Kontynuujemy omawianie kamieni milowych ekonofizyki i socjofizyki. Wybraliśmy je w kontekście wyzwań jakie niesie ze sobą współczesna rzeczywistość społeczno-ekonomiczna. Wskazujemy na ich rolę w budowaniu obszarów badawczych ekonofizyki i socjofizyki. Ta część poświęcona jest przede wszystkim złożoności, a w tym sieciom złożonym, przemianom fazowym a szczególnie zjawiskom i procesom krytycznym, modelowaniu agentowemu, zagadnieniom ryzyka w kontekście rynków finansowych oraz elementom współczesnej socjofizyki.

Słowa kluczowe

science of complexity complex networks scaling-laws power-laws critical phenomena financial currency cryptocurrency company markets agent modeling market risk systemic risk

nauka o złożoności sieci złożone prawa skalowania prawa potęgowe zjawiska krytyczne rynki finansowe rynki walutowe rynki firm modelowanie agentowe ryzyko rynkowe ryzyko systemowe

Wydawca

Polskie Towarzystwo Fizyczne

Czasopismo

Postępy Fizyki

Rocznik

2022

Tom

T. 73, z. 4

Strony

16--26

Opis fizyczny

Bibliogr. 106 poz., rys.

Twórcy

autor

Kutner Ryszard

Faculty of Physics, University of Warsaw

https://orcid.org/0000-0002-4949-8781

Bibliografia

[1] Ryszard Kutner, Marcel Ausloos, Dariusz Grech, Tiziana Di Matteo, Christophe Schinckus, and H.Eugene Staanley: ‘Econophysics and sociophysics: Their milestones & challenges’, Physica A: Statistical Mechanics and its Applications 516, 240-253 (2019)
[2] Physica A, VSI: “Econophysics and sociophysics In turbulent world”, Marcel Ausloos, Dariusz Grech, Tiziana Di Matteo, Ryszard Kutner, Christophe Schinckus, and H. Eugene Staanley (Eds.)
3] Entropy, SI open access: “Three Risky Decades: A Time for Econophysics?”, Ryszard Kutner, Christophe Schinckys, and H. Eugene Stanley (Eds.)
[4] R. Kutner: Kamienie milowe & wyzwania ekonofizyki a także socjofizyki cz. 1, Postępy Fizyki 73 (1), 11 (2022).
[5] A.L. Bárabási, Network Science, (Cambridge Univ. Press, Cambridge, 2017).
[6] R.N. Mantegna and H.E. Stanley, An Introduction to Econophysics. Correlations and Complexity in Finance, (Cambridge Univ. Press, Cambridge, 2002).
[7] F. Chin, D. Houck, Algorithms for updating minima spanning trees, J. Comp. System Sciences 16(3), 333 (1978).
[8] R.N. Mantegna, Hierarchical structure in financial markets, Eur. Phys. J. B 11(1), 193 (1999).
[9] P.L. Graham, P. Hell, On the history of the minimum spanning tree problem, Annals Hist. Comp., 7(1), 43 (1985).
[10] H. Yaman, O.E. Karşan, M.Ç. Pinar, The robust spanning tree problem with interval data, Oper. Esearch Lett. 29, 31 (2001).
[11] Th. Kirschstein, S. Liebscher, C. Becker, Robust estimation of location and scatter by pruning the minimum spanning tree, J. Multivariete Anal. 120, 173 (2013).
[12] A. Sienkiewicz, T. Gubiec, R. Kutner, and Z.R. Struzik, Structural and topological phase transition on the German Stock Exchange, Physica A 392, 5963 (2013).
[13] M. Tumminello, T. Aste, T. Di Matteo and R. N. Mantegna, A tool for filtering information in complex systems, Edited by H. Eugene Stanley, PNAS 102, 10421 (2005).
[14] T. Aste, T. Di Matteo, and S. T. Hyde, Complex networks on hyperbolic surfaces, Physica A 346, 20 (2005).
[15] T. Aste, R. Gramatica, and T. Di Matteo, Exploring complex networks via topological embedding on surfaces, Phys. Rev. E 86, 036109 (2012).
[16] Won-Min Song, T. Di Matteo, and T. Aste, Hierarchical information clustering by means of topologicallyembedded graphs, PLoS One 7(3), e31929 (2012).
[17] F. Pozzi, T. Di Matteo and T. Aste, Spread of risk across financial markets: better to invest in the peripheries, Scientific Reports 3, 1665 (2013).
[18] N. Musmeci, T. Aste, and T. Di Matteo, Relation between financial market structure and the Real economy: comparison between clustering methods, PLoS ONE 10(3), e0116201 (2015).
[19] N. Musmeci, T. Aste, and T. Di Matteo, Risk diversification: a study of persistence with a filtered correlation-network approach, J. Network Theory In Finance 1(1), 1 (2015).
[20] R. Morales, T. Di Matteo, and T. Aste, Dependency structure and scaling properties of financial time series are related, Scientific Reports 4 (2014) 4589. DOI: 10.1038/srep04589.
[21] R. J. Buonocore, T. Di Matteo, and R. N. Mantegna, On the interplay between multiscaling and crosscorrelation, (2017) arXiv:1802.01113 [q-fin.ST].
[22] N. Musmeci, T. Aste, and T. Di Matteo, Interplay between past market correlation structure chan ges and future volatility outbursts, Scientific Reports 6, 36320 (2016).
[23] T. Aste and T. Di Matteo, Sparse causality Network retrieval from short time series, Complexity 2017, Article ID 4518429, 13 pages(2017).
[24] M. Gligor and M. Ausloos, Convergence and luster structures in EU area according to fluctuations In macroeconomic indices, Journal of Economic Integration 23(2), 297-330 (2008).
[25] M. Gligor and M. Ausloos Cluster structure of EU-15 countries derived from the correlation matrix analysis of macroeconomic index fluctuations, Eur. Phys. J. B 57 (2), 139-146 (2007)
[26] Econophysics of Systemic Risk and Network Dynamics edited by F. Abergel, B.K. Chakrabarti, A. Chakraborti, and A. Ghosh, (Springer-Verlag, London 2013)
[27] Y. Malevergne and D. Sornette, Extreme Financial Risks. From Dependence to Risk Management, (Springer-Verlag, Heidelberg 2006).
[28] Uncertainty and Risk. Mental, Formal, Experimental Representations, M. Abdellaoui, R.D. Luce, M.J. Machina, and B. Munier (Eds) (Springer-Verlag, Heidelberg 2007).
[29] J. Masoliver and J. Perelló, First-passage and risk evaluation under stochastic volatility, Phys. Rev. E 80, 016108 (2009).
[30] J. Masoliver and J. Perelló, Extreme times for volatility processes, Phys. Rev. E 75, 046110 (2007).
[31] R. Cont, Empirical Properties of Asset Returns: Stylized Facts and Statistical Issues, Quant. Finance 1,223 (2001).
[32] J.-P. Bouchaud, The Endogenous Dynamics of Markets: Price Impact, Feedback Loops and Instabilitiesin Lessons from the 2008 Crisis, edited by A. Berd (Risk Books, Incisive. Media, London, 2011).
[33] A. Abergel, J.-P. Bouchaud, Th. Foucault, Ch. Lehalle, and M. Rosenbaum Market microstructure. Confronting many viewpoints, (J. Wiley and Sons, 2012).
[34] F. Slanina, Essentials of Econophysics Modelling, (Oxford University Press, Oxford 2014).
[35] D. Sornette, Physics and financial economics (1776-2014): Puzzles, Ising and agent-based models, Reports on Progress in Physics 77 (6): 062001 (2014).
[36] Ch. Schinckus, 1996-2016: Two decades of econophysics: Between methodological diversification and conceptual coherence, Eur. Phys. J. Special Topics 225, 3299 (2016).
[37] M. Ausloos, H. Dawid, and U. Merlone, Spatial Interactions in Agent-Based Modeling in Complexity and Geographical Economics: Topics and Tools, P. Commendatore, S. Kayam, I. Kubin (Eds.), (Springer-Verlag, Heidelberg 2015), p. 353.
[38] J.D. Farmer and D. Foley, The economy needs agentbased modelling, Nature 457, 957 (2009).
[39] M.W. Macy and R. Willer, From Factoras to Actors: Computational Sociology and Agent-Based Modeling, Annu. Rev. Sociol. 28 (2002) 143.
[40] F.C. Billari, Th. Fent, A. Prskawetz, J. Scheffran, (Eds.) Agent-Based Computational Modelling. Applications in Demography, Social, Economic and Environmental Sciences, (Springer-Verlag, Heidelberg 2006).
[41] F. Abergel, H. Aoyama, B.K. Chakrabarti, A. Chakraborti, A. Ghosh (Eds.)Econophysics of AgentBased Models, (Springer-Verlag, 2013).
[42] G. Kim, H. Markowitz, Investment Rules, Margin, And Market Volatility, Journal of Portfolio Management 16, 45-52 (1989).
[43] E. Samonidou, E. Zschischang, D. Stauffer, T. Lux, Microscopic models of financial markets, Rep. Prog. Phys. 70, 409 (2007).
[44] M. Levy, H. Levy, and S. Solomon, A microscopic model of stock market: cycles, booms and crashes, Econ. Lett. 45, 103 (1994).
[45] T. Lux and M. Marchesi, Scaling and criticality in a stochastic multi-agent model of financial markets,Nature 397, 498 (1999).
[46] G. Iori, Avalanche dynamics and trading friction effect on stock market returns, Int. J. Mod. Phys. C 10, 1149 (1999).
[47] R. Cont, J.-P. Bouchaud, Herd behaviour and aggregate fluctuations in financial markets, Macroecon. Dyn. 4, 170 (2000).
[48] D. Stauffer, Percolation models of financial market dynamics, Adv. Complex Syst. 4 19 (2001).
[49] S. Bornholdt, Expectation bubbles in a spin model of markets: intermittency from frustation across scales, Int. J. Mod. Phys. C 12 667 (2001).
[50] T. Kaizoji, Speculative bubbles and crashes in Stock markets: an interacting-agent model of speculative activity, Physica A 287 493 (2000)
[51] M. Denys, T. Gubiec, and R. Kutner, Reinterpreta tion of Sieczka-Hołyst financial market model, Acta Phys. Pol. A 123(3) 513 (2013).
[52] V. Gontis, Interplay between Endogenous and Exogenous Fluctuations in Financial Markets. Acta Phys. Pol. A 129, 1023 (2016).
[53] G. Dhesi and M. Ausloos, Modelling and Measuring the Irrational behaviour of Agents in Financial Markets: Discovering the Psychological Soliton, Chaos Solitons & Fractals 88, 119 (2016).
[54] N. Vandewalle, M. Ausloos, Ph. Boveroux, A. Minguet, How the financial crash of 1987 could have been predicted, Eur. Phys. J. B 4 (1998) 139.
[55] N. Vandewalle, Ph. Boveroux, A. Minguet, and M. Ausloos,The crash of October 1987 seen as a phase transition: amplitude and universality, Physica A 225(1), 201 (1998).
[56] P. Sieczka, D. Sornette, and J. Hołyst, The Lehman Brothers effect and bankruptcy cascades, Eur. Phys. J. B 82: 257 (2011).
[57] F. Schweitzer, G. Fagiolo, D. Sornette, F. VegaRedondo, A. Vespignani, and D.R. White, Economic Networks: The New Challenges, Science 325, 422 (2009).
[58] M. Scheffer, J. Bascompte, W.A. Brock, V. Brovkin, S.R. Carpenter, V. Dakos, H. Held, E.H. van Nes, M. Rietkerk, and G. Sugihara, Early-warning signals for critical transitions, Nature 461, 53 (2009).
[59] M. Kozłowska, M. Denys, M. Wiliński, G. Link, T. Gubiec, T.R. Werner, R. Kutner, and Z.R. Struzik, Dynamic bifurcations on financial markets, Chaos, Solitons and Fractals 88, 126 (2016).
[60] M. Ausloos, P. Clippe, J. Miśkiewicz, and A. Pe ̧kalski, A (reactive) lattice-gas approach to economic cycles, Physica A 344, 1 (2004).
[61] M. Ausloos, J. Miśkiewicz, and M. Sanglier, The durations of recession and prosperity: does their distribution follow a power or an exponential law?, Physica A 339, 548 (2004).
[62] M. Karpiarz, P. Fronczak, and A. Fronczak, International Trade Network: Fractal Properties and Globalization Puzzle, Phys. Rev. Lett. 113, 248701 (2014).
[63] J.M.C. Santos Silva and T. Silvana, The Log of Gravity, Rev. of Economics and Statistics 88 (4), 641 (2006).
[64] M. Ausloos, P. Clippe, and A. Pe ̧kalski, Model of macroeconomic evolution in stable regionally dependent economic fields, Physica A 337, 269 (2004).
[65] M. Ausloos, P. Clippe, and A. Pe ̧kalski, Evolution of economic entities under heterogeneous political/environmental conditions within a Bak-Sneppenlike dynamics, Physica A 332, 394 (2004).
[66] P. Bak and K. Sneppen, Punctuated equilibrium and criticality in a simple model of evolution, Phys. Rev. Lett. 71(24), 4083 (1993).
[67] A. Quetelet, Mémoire sur les lois des naissances et de la mortalité à Bruxelles, Nouveaux mémoires de l’Académie royale des sciences et belles-lettres de Bruxelles 1826, 3: 495 (in French).
[68] A. Quetelet, Sur l’hommeetle développment de ses facultés, ou Essai de physique sociele, Guillaumin et Cie, Paris, 1835.
[69] B.K. Chakrabarti, A. Chakraborti, and A. Chatterjee, Econophysics and Sociophysics. Trends and Per sepctives, (Viley-VCH Verlag GmbH & Co KGaA, Veinheim 2006).
[70] Cyberemotions. Collective Emotions in Cyberspace, J.A. Hołyst (Ed.), Springer Complexity (Springer International Publishing Switzerland 2017).
[71] K. Sznajd-Weron and J. Sznajd, Opinion evolution in closed community, Int. J. Mod. Phys. C 11, 1157 (2000).
[72] D. Stauffer, Sociophysics: the Sznajd model and its applications, Comp. Phys. Comm. 146(1), 93 (2002).
[73] E. Bonabeau, G. G. Theraulaz, J. L. Deneubourg, Phase diagram of a model of selforganizinghierarchies, Physica A 217, 373 (1995).
[74] D. Pumain, Hierarchy in Natural and Social Sciences, (Springer-Verlag, 2006).
[75] R. Paluch, K. Suchecki, and J.A. Hołyst, Models of random graph hierarchies, Eur. Phys. J. B 88: 216 (2015).
[76] A. Nowak, J. Szamrej, B. Latané, From Private Attitude to Public Opinion: A Dynamic Theory of Social Impact, Psychological Review 97(3), 362 (1990).
[77] E.W. Montroll, Social dynamics and the quantifying of social forces, Proc. Nat. Acad. Sci. USA 75, 4633 (1978).
[78] M. Ausloos, Another Analytic View about Quantifying Social Forces, Advances in Complex Systems 16, 1250088 (2013).
[79] P. Sobkowicz and A. Sobkowicz, Two-Year Study of Emotion and Communication Patterns in a Highly Polarized Political Discussion Forum, Social Science Computer Review May 6 (2012).
[80] P. Sobkowicz, Quantitative Agent Based Model of Opinion Dynamics: Polish Elections of 2015, Plos One May 12 (2016).
[81] Why Society is a Complex Matter. Meeting Twenty- first Century Challenges with a New Kind of Science. With a contribution of Dirk Helbing, P. Ball (Ed.) (Springer-Verlag, Berlin 2012).
[82] D. Helbing, New Ways to Promote Sustainability and Social Well-Beingin a Complex, Strongly Interdependent World: The FuturICT Approach in Why Society is a Complex Matter. Meeting Twenty-first Century Challenges with a New Kind of Science, (Springer-Verlag, Berlin 2012) p. 55.
[83] D. Helbing, I. Farkas, and T. Vicsek, Simulating dynamical features of escape panic, Naturew 407, 487 (2000).
[84] C. Castellano, S. Fortunato, and V. Loreto, Statistical Physics of Social dynamics, Rev. Mod. Phys. 81, 591 (2009)
[85] Th. Gross and B. Blasius, Adaptive coevolutionary networks: a review, J. Royal Soc. Interface 5, 259 (2008).
[86] M. Perc, J.J. Jordan, D. Rand, Zhen Wang, S. Boccaletti, and A. Szolnoki, Statistical physics of human cooperation, Phys. Rep. 687, 1 (2017).
[87] V. Loreto, A. Baronchelli, A. Mukherjee, A. Puglisi, and F. Tria, Statistical physics of language dynamics, J. Stat. Mech.: Theory and Experiment 2011, P04006 (2011).
[88] Sch. Christian and D. Stauffer, Recent development in computer simulations of language competition, Computing in Science & Engineering 8, 60 (2006).
[89] R. Axelrod, The dissemination of culture: A model with local convergence and global polarization, J. Conflict Res. 41, 203 (1997).
[90] C. Castellano, M. Marsili, and A. Vespignani, Nonequilibrium phase transition in a model for social influence, Phys. Rev. Lett. 85, 3536 (2000).
[91] K. Klemm, V.M. Eguiluz, R.Toral, and M. San Miguel, Nonequilibrium transitions in complex networks: A model of social interaction, Phys. Rev. E 67, 026120 (2003).
[92] T. Raducha and T. Gubiec, Coevolving complex networks in the model of social interactions, Physica A 471, 427 (2017).
[93] M.A.L. Chavira and R. Marcelin-Jiménez, Distributed rewiring model for complex networking: The effect of local rewiring rules on final structural properties, Plos One 12(11), e0187538 (2017).
[94] M. Ausloos and F. Petroni, Statistical dynamics of religions and adherents, Europhys. Lett. 77(3), 38002 (2007).
[95] V.M. Yakovenko and J.B. Rosser, Colloquium: Statistical mechanics of money, wealth, and income, Rev. Mod. Phys. 81, 1707 (2009).
[96] M. Jagielski and R. Kutner, Modelling of income distribution in the European Union with the Fokker Planck equation, Physica A 392(9), 2130 (2013).
[97] J.-P. Bouchaud and M. Mezard, Wealth Condensation in a simple model of economy, Physica A 282, 536 (2000).
[98] Z. Burda, D. Johnston, J. Jurkiewicz, M. Kaminski, M.A. Nowak, G. Papp, and I. Zahed, Wealth condensation in Pareto macroeconomies, Phys. Rev. E 65, 026102 (2002).
[99] C. Hertellu, P. Richmond, and B.M. Roehner, Deciphering the fluctuations of high frequency birth rates, Physica A 509, 1046 (2018).
[100] T. Aste and T. Di Matteo, Introduction to Complex and Econophysics Systems: A Navigation map, In Complex Physical, Biophysical and Econophysical SDystems in World Scientific Lecture Notes in Complex Systems, edited by Robert L. Dewar & Frank Detering (World Scientific, Singapore 2010), Vol. 9, Chap. 1, pp. 1-35.
[101] R. J. Buonocore, N. Musmeci, T. Aste, and T. Di Matteo, Two different flavours of complexity in financial data, Eur. Phys. J. Special Topics 225, 3105 (2016).
[102] N. Musmeci, V. Nicosia, T. Aste, T. Di Matteo, and V. Latora, The Multiplex Dependency Structure of Financial Markets, Complexity, vol. 2017, Article ID 9586064, 13 pages, 2017, doi:10.1155/2017/9586064 (arXiv:1606.04872).
[103] F. Jovanovic, Ch. Schinckus, Econophysics and Financial Economics. An Emerging Dialogue, (Oxford Univ. Press, Oxford, 2017).
[104] F. Black, M.S. Scholes, and R.C. Merton, The Pricing of Options and Corporate Liabilities, Journal of Political Economy 81, 637 (1973).
[105] J.-Ph. Bouchaud and M. Potters, Theory of Financial Risks. From Statistical Physics to Risk Management, (Cambridge Univ. Press, Cambridge, 2001).
[106] Y. Malevergne and D. Sornette, Extreme Financial Risks. From Dependence to Risk Management, (Springer-Verlag, Heidelberg, 2006).

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-d89a0f26-df09-44cf-a74d-358d4c91df1d