Human behavior and lognormal distribution. A kinetic description

In recent years, it has been increasing evidence that lognormal distributions are widespread in physical and biological sciences, as well as in various phenomena of economics and social sciences. In social sciences, the appearance of lognormal distribution has been noticed, among others, when looking at body weight, and at women’s age at first marriage. Likewise, in economics, lognormal distribution appears when looking at consumption in a western society, at call-center service times, and others. The common feature of these situations, which describe the distribution of a certain people’s hallmark, is the presence of a desired target to be reached by repeated choices. In this paper, we discuss a possible explanation of lognormal distribution forming in human activities by resorting to classical methods of statistical mechanics of multi-agent systems. The microscopic variation of the hallmark around its target value, leading to a linear Fokker–Planck-type equation with lognormal equilibrium density, is built up introducing as main criterion for decision a suitable value function in the spirit of the prospect theory of Kahneman and Twersky.

[1]  Giuseppe Toscani,et al.  Kinetic Models for the Trading of Goods , 2013 .

[2]  K. Pesz A class of Fokker-Planck equations with logarithmic factors in diffusion and drift terms , 2002 .

[3]  Xiaogang Jin,et al.  Departure headways at signalized intersections: A log-normal distribution model approach , 2009 .

[4]  Victor M. Yakovenko,et al.  Statistical mechanics of money , 2000 .

[5]  Parongama Sen Phase transitions in a two-parameter model of opinion dynamics with random kinetic exchanges. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[6]  Kristian Giesen,et al.  The size distribution across all cities - Double Pareto lognormal strikes , 2010 .

[7]  C. Cercignani The Boltzmann equation and its applications , 1988 .

[8]  Lorenzo Pareschi,et al.  On a Kinetic Model for a Simple Market Economy , 2004, math/0412429.

[9]  Giuseppe Toscani,et al.  Kinetic and mean field description of Gibrat's law , 2016, 1606.04796.

[10]  S. Fortunato,et al.  Statistical physics of social dynamics , 2007, 0710.3256.

[11]  E. Ben-Naim Opinion dynamics: Rise and fall of political parties , 2004, cond-mat/0411427.

[12]  Moshe Levy,et al.  Microscopic Simulation of Financial Markets: From Investor Behavior to Market Phenomena , 2000 .

[13]  Lorenzo Pareschi,et al.  Reviews , 2014 .

[14]  Lorenzo Pareschi,et al.  Mesoscopic Modelling of Financial Markets , 2009, 1009.2743.

[15]  Gerard J. P. van Breukelen Parallel information processing models compatible with lognormally distributed response times , 1995 .

[16]  Francesco Salvarani,et al.  Conciliatory and contradictory dynamics in opinion formation , 2012 .

[17]  L. Ahrens The lognormal distribution of the elements (A fundamental law of geochemistry and its subsidiary) , 1954 .

[18]  S. Galam,et al.  Towards a theory of collective phenomena: Consensus and attitude changes in groups , 1991 .

[19]  Giuseppe Toscani,et al.  Entropy production and the rate of convergence to equilibrium for the Fokker-Planck equation , 1999 .

[20]  Distribution of metal values in ore deposits , 1940 .

[21]  Jean-Daniel Zucker,et al.  From Individual Choice to Group Decision Making , 2000 .

[22]  X. Gabaix Zipf's Law for Cities: An Explanation , 1999 .

[23]  Avishai Mandelbaum,et al.  Statistical Analysis of a Telephone Call Center , 2005 .

[24]  Juan Soler,et al.  From a systems theory of sociology to modeling the onset and evolution of criminality , 2014, Networks Heterog. Media.

[25]  María Vera-Cabello,et al.  Size Distributions for All Cities: Which One is Best? , 2015 .

[26]  Lorenzo Pareschi,et al.  Kinetic models for socio-economic dynamics of speculative markets , 2010, 1009.5499.

[27]  S. Kullback,et al.  A lower bound for discrimination information in terms of variation (Corresp.) , 1967, IEEE Trans. Inf. Theory.

[28]  Giuseppe Toscani,et al.  Sur l'inégalité logarithmique de Sobolev , 1997 .

[29]  S. Chandrasekhar Stochastic problems in Physics and Astronomy , 1943 .

[30]  M. Marsili,et al.  Interacting Individuals Leading to Zipf's Law , 1998, cond-mat/9801289.

[31]  Giuseppe Toscani,et al.  Fokker–Planck equations in the modeling of socio-economic phenomena , 2017 .

[32]  P. Markowich,et al.  Boltzmann and Fokker–Planck equations modelling opinion formation in the presence of strong leaders , 2009, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[33]  F. W. Preston PSEUDO-LOGNORMAL DISTRIBUTIONS' , 1981 .

[34]  Arturo Ramos Are the log-growth rates of city sizes distributed normally? Empirical evidence for the USA , 2017 .

[35]  C. Villani,et al.  Generalization of an Inequality by Talagrand and Links with the Logarithmic Sobolev Inequality , 2000 .

[36]  D. Burmaster,et al.  Bivariate distributions for height and weight of men and women in the United States. , 1992, Risk analysis : an official publication of the Society for Risk Analysis.

[37]  Soumyajyoti Biswas,et al.  Disorder induced phase transition in kinetic models of opinion dynamics , 2011, 1102.0902.

[38]  M. Marchesi,et al.  VOLATILITY CLUSTERING IN FINANCIAL MARKETS: A MICROSIMULATION OF INTERACTING AGENTS , 1998 .

[39]  S. Galam,et al.  Sociophysics: A new approach of sociological collective behaviour. I. mean‐behaviour description of a strike , 1982, 2211.07041.

[40]  J. Carrillo,et al.  Rényi entropy and improved equilibration rates to self-similarity for nonlinear diffusion equations , 2014, 1403.3128.

[41]  M. Marchesi,et al.  Scaling and criticality in a stochastic multi-agent model of a financial market , 1999, Nature.

[42]  D E Burmaster,et al.  Lognormal Distributions for Body Weight as a Function of Age for Males and Females in the United States, 1976–1980 , 1997, Risk analysis : an official publication of the Society for Risk Analysis.

[43]  C. Lo Dynamics of Fokker–Planck Equation with Logarithmic Coefficients and Its Application in Econophysics , 2010 .

[44]  Nicola Bellomo,et al.  On the dynamics of social conflicts: looking for the Black Swan , 2012, ArXiv.

[45]  E. Ben-Naim,et al.  Bifurcations and patterns in compromise processes , 2002, cond-mat/0212313.

[46]  Solomon Kullback,et al.  Correction to A Lower Bound for Discrimination Information in Terms of Variation , 1970, IEEE Trans. Inf. Theory.

[47]  Stephen M Roberts,et al.  Body Weight Distributions for Risk Assessment , 2007, Risk analysis : an official publication of the Society for Risk Analysis.

[48]  J. Gillis,et al.  Probability and Related Topics in Physical Sciences , 1960 .

[49]  Juan Soler,et al.  ON THE DIFFICULT INTERPLAY BETWEEN LIFE, "COMPLEXITY", AND MATHEMATICAL SCIENCES , 2013 .

[50]  The log‐normal distribution , 2017 .

[51]  R. Blundell,et al.  Why Is Consumption More Log Normal than Income? Gibrat’s Law Revisited , 2009, Journal of Political Economy.

[52]  Katarzyna Sznajd-Weron,et al.  Opinion evolution in closed community , 2000, cond-mat/0101130.

[53]  Jeff Miller,et al.  Information processing models generating lognormally distributed reaction times , 1993 .

[54]  P. Beaudry,et al.  Spatial Equilibrium with Unemployment and Wage Bargaining: Theory and Estimation , 2013 .

[55]  L. Theodore Log-Normal Distribution , 2015 .

[56]  M. Marchesi,et al.  VOLATILITY CLUSTERING IN FINANCIAL MARKETS: A MICROSIMULATION OF INTERACTING AGENTS , 2000 .

[57]  P E SARTWELL,et al.  The distribution of incubation periods of infectious disease. , 1950, American journal of hygiene.

[58]  Arnab Chatterjee,et al.  Master equation for a kinetic model of a trading market and its analytic solution. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[59]  S. Galam Rational group decision making: A random field Ising model at T = 0 , 1997, cond-mat/9702163.

[60]  P E Sartwell,et al.  The incubation period and the dynamics of infectious disease. , 1966, American journal of epidemiology.

[61]  Giuseppe Toscani,et al.  Pareto tails in socio-economic phenomena: a kinetic description , 2018 .

[62]  J. Eeckhout Gibrat's Law for (All) Cities , 2004 .

[63]  G. Toscani,et al.  Size distribution of cities: A kinetic explanation , 2018, Physica A: Statistical Mechanics and its Applications.

[64]  Miguel Puente-Ajovín,et al.  On the parametric description of the French, German, Italian and Spanish city size distributions , 2015 .

[65]  S. Redner,et al.  Unity and discord in opinion dynamics , 2003 .

[66]  Trevor V. Suslow,et al.  Lognormal distribution of bacterial populations in the rhizosphere , 1984 .

[67]  David R. Hunter,et al.  mixtools: An R Package for Analyzing Mixture Models , 2009 .

[68]  Bikas K. Chakrabarti,et al.  Pareto Law in a Kinetic Model of Market with Random Saving Propensity , 2004 .

[69]  Giuseppe Toscani,et al.  Kinetic equations modelling wealth redistribution: a comparison of approaches. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[70]  Zipf's law in city size from a resource utilization model. , 2014, Physical review. E, Statistical, nonlinear, and soft matter physics.

[71]  W. Stahel,et al.  Log-normal Distributions across the Sciences: Keys and Clues , 2001 .

[72]  E. Scalas,et al.  Statistical equilibrium in simple exchange games II. The redistribution game , 2007 .

[73]  E. Scalas,et al.  Statistical equilibrium in simple exchange games I , 2006 .

[74]  D. Hunter,et al.  mixtools: An R Package for Analyzing Mixture Models , 2009 .

[75]  Marcello Delitala,et al.  On a discrete generalized kinetic approach for modelling persuader's influence in opinion formation processes , 2008, Math. Comput. Model..

[76]  A. Tversky,et al.  Choices, Values, and Frames , 2000 .

[77]  Giuseppe Toscani,et al.  Call center service times are lognormal: A Fokker–Planck description , 2018, Mathematical Models and Methods in Applied Sciences.

[78]  Alexis Akira Toda A NOTE ON THE SIZE DISTRIBUTION OF CONSUMPTION: MORE DOUBLE PARETO THAN LOGNORMAL , 2015, Macroeconomic Dynamics.

[79]  S. S. Hirano,et al.  Lognormal Distribution of Epiphytic Bacterial Populations on Leaf Surfaces , 1982, Applied and environmental microbiology.

[80]  S. Solomon,et al.  A microscopic model of the stock market: Cycles, booms, and crashes , 1994 .

[81]  Bikas K. Chakrabarti,et al.  Statistical mechanics of money: how saving propensity affects its distribution , 2000, cond-mat/0004256.

[82]  Lorenzo Pareschi,et al.  Mathematical Modeling of Collective Behavior in Socio-Economic and Life Sciences , 2010 .

[83]  A. Tversky,et al.  Prospect theory: analysis of decision under risk , 1979 .

[84]  Zeynep Akşin,et al.  The Modern Call Center: A Multi‐Disciplinary Perspective on Operations Management Research , 2007 .

[85]  Francesco Salvarani,et al.  A KINETIC APPROACH TO THE STUDY OF OPINION FORMATION , 2009 .

[86]  Massimo Riccaboni,et al.  The size distribution of US cities: Not Pareto, even in the tail , 2013 .

[87]  Giuseppe Toscani,et al.  A Boltzmann-like equation for choice formation , 2009 .

[88]  G. Toscani,et al.  Kinetic models of opinion formation , 2006 .

[89]  A. Malanca,et al.  Distribution of 226Ra, 232Th, and 40K in soils of Rio Grande do Norte (Brazil) , 1996 .

[90]  Cédric Villani,et al.  Contribution à l'étude mathématique des équations de Boltzmann et de Landau en théorie cinétique des gaz et des plasmas , 1998 .

[91]  Francesco Salvarani,et al.  The quasi-invariant limit for a kinetic model of sociological collective behavior , 2009 .

[92]  Vladimir A. Bolotin,et al.  Telephone Circuit Holding Time Distributions , 1994 .

[93]  Anirban Chakraborti,et al.  Opinion formation in kinetic exchange models: spontaneous symmetry-breaking transition. , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.