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Principle of least effort vs. maximum efficiency: deriving Zipf-Pareto's laws

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  • Wang, Qiuping A.

Abstract

This paper provides a derivation of Zipf-Pareto laws directly from the principle of least effort. A probabilistic functional of efficiency is introduced as the consequence of an extension of the nonadditivity of the efficiency of thermodynamic engine to a large number of living agents assimilated to engines, all randomly distributed over their output. Application of the maximum calculus to this efficiency yields the Zipf's and Pareto's laws.

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  • Wang, Qiuping A., 2021. "Principle of least effort vs. maximum efficiency: deriving Zipf-Pareto's laws," Chaos, Solitons & Fractals, Elsevier, vol. 153(P1).
    • Handle: RePEc:eee:chsofr:v:153:y:2021:i:p1:s0960077921008432
    DOI: 10.1016/j.chaos.2021.111489
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    References listed on IDEAS

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    1. David Dollar & Tatjana Kleineberg & Aart Kraay, 2015. "Growth, inequality and social welfare: cross-country evidence," Economic Policy, CEPR, CESifo, Sciences Po;CES;MSH, vol. 30(82), pages 335-377.
    2. Li, W. & Wang, Q.A. & Nivanen, L. & Le Méhauté, A., 2006. "How to fit the degree distribution of the air network?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 368(1), pages 262-272.
    3. Deng, Weibing & Li, Wei & Cai, Xu & Wang, Qiuping A., 2011. "The exponential degree distribution in complex networks: Non-equilibrium network theory, numerical simulation and empirical data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(8), pages 1481-1485.
    4. Andrew G. Berg & Jonathan D. Ostry, 2017. "Inequality and Unsustainable Growth: Two Sides of the Same Coin?," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 65(4), pages 792-815, November.
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    1. Agouzal, Abdellatif & Lafouge, Thierry & Bertin, Marc, 2024. "Relationship between the principle of least effort and the average cost of information in a zipfian context," Journal of Informetrics, Elsevier, vol. 18(1).
    2. Wang, Zhenhua & Ren, Ming & Gao, Dong & Li, Zhuang, 2023. "A Zipf's law-based text generation approach for addressing imbalance in entity extraction," Journal of Informetrics, Elsevier, vol. 17(4).

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