Abstract In order to avoid the worsening of wealth inequality, it is necessary to explore the influencing factors of wealth distribution and discuss measures to reduce wealth inequality. We investigate the wealth distribution in the goods exchange market by using the kinetic theory of rarefied gas. The trading objects are two kinds of commodities (commodities A and B) and the trading subjects are agents of two groups (dealers and speculators). We deduce the interaction rules according to the principle of utility maximization and consider the transfer of agents in the Boltzmann equation. The steady solution of the Fokker-Planck equation for a special case is obtained and the effects of trading strategy and transfer frequency on the steady distribution are analyzed in numerical experiments. The conclusions illustrate that the transfer of agents is conducive to reducing the inequality of wealth distribution.
Corresponding Authors:
Rongmei Sun
E-mail: sunrongmei@smail.swufe.edu.cn
Cite this article:
Rongmei Sun(孙溶镁) and Daixin Wang(汪代薪) A kinetic description of the goods exchange market allowing transfer of agents 2025 Chin. Phys. B 34 030502
[1] Chancel L, Piketty T, Saez E and Zucman G 2022 World Inequality Report 2022 [2] Cordier S, Pareschi L and Toscani G 2005 J. Stat. Phys. 120 253 [3] Cordier S, Pareschi L and Piatecki C 2009 J. Stat. Phys. 134 161 [4] Zhong Y, Lai S Y and Hu C H 2021 Appl. Math. Comput. 404 126231 [5] Maldarella D and Pareschi L 2012 Physica A 391 715 [6] Brugna C and Toscani G 2015 Phys. Rev. E 92 052818 [7] Bertotti M L 2010 Appl. Math. Comput. 217 752 [8] Meng J, Zhou X and Lai S Y 2024 Chin. Phys. B 33 070501 [9] ZhaoMF, Kong L T, LiuMand Lai S Y 2024 Chin. Phys. B 33 090502 [10] Pareschi L and Toscani G 2014 Interacting multiagent systems: Kinetic equations and Monte Carlo methods (OUP Oxford) [11] Toscani G 2006 Commun. Math. Sci. 4 481 [12] Dimarco G, Toscani G and Zanella M 2022 J. Econ. Interact. Coor. 19 493 [13] Boudin L, Monaco R and Salvarani F 2010 Phys. Rev. E 81 036109 [14] Düring B, Franceschi J, Wolfram M T and Zanella M 2024 J. Nonlinear. Sci. 34 79 [15] Furioli G, Pulvirenti A, Terraneo E and Toscani G 2017 Math. Mod. Meth. Appl. Sci. 27 115 [16] Wang L L, Lai S Y and Sun R M 2022 Appl. Math. Comput. 417 126772 [17] Hu C H and Chen H J 2023 Chin. Phys. B 32 088901 [18] Paul S, Mukherjee S, and Joseph A C B K 2022 Philos. Trans. R. Soc. A 380 163 [19] Cao F and Motsch S 2023 Kinet. Relat. Mod. 16 764 [20] Toscani G, Brugna C and Demichelis S 2013 J. Stat. Phys. 151 549 [21] Brugna C and Toscani G 2018 Physica A 499 362 [22] Hu C H, Lai S Y and Lai C 2020 Physica A 549 123938 [23] Düring B and Toscani G 2008 Commun. Math. Sci. 6 1043 [24] Giovangigli F 2012 Sci. China Math. 55 285 [25] Desvillettes L, Monaco R and Salvarani F 2005 Eur. J. Mech. B 24 219 [26] Rossani A and Spiga G 1999 Physica A 272 563 [27] Dimarco G, Pareschi L, Toscani G and Zanella M 2020 Phys. Rev. E 102 022303 [28] Bernardi E, Pareschi L, Toscani G and Zanella M 2022 Entropy 24 233 [29] Bisi M 2022 Philos. Trans. R. Soc. A 380 156 [30] Bisi M and Loy N 2024 Physica D 457 133967 [31] Polyanin A D 2001 Handbook of linear partial differential equations for engineers and scientists (Chapman and hall/crc)
Altmetric calculates a score based on the online attention an article receives. Each coloured thread in the circle represents a different type of online attention. The number in the centre is the Altmetric score. Social media and mainstream news media are the main sources that calculate the score. Reference managers such as Mendeley are also tracked but do not contribute to the score. Older articles often score higher because they have had more time to get noticed. To account for this, Altmetric has included the context data for other articles of a similar age.
