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储蓄对具有信贷和债务的类气体模型经济的影响。

Effect of Savings on a Gas-Like Model Economy with Credit and Debt.

作者信息

Chacón-Acosta Guillermo, Ángeles-Sánchez Vanessa

机构信息

Departamento de Matemáticas Aplicadas y Sistemas, Universidad Autónoma Metropolitana Cuajimalpa, Vasco de Quiroga 4871, Ciudad de México 05348, Mexico.

Escuela Superior de Economía, Instituto Politécnico Nacional, Plan de Agua Prieta 66, Ciudad de México 11350, Mexico.

出版信息

Entropy (Basel). 2021 Feb 5;23(2):196. doi: 10.3390/e23020196.

DOI:10.3390/e23020196
PMID:33562772
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7915829/
Abstract

In kinetic exchange models, agents make transactions based on well-established microscopic rules that give rise to macroscopic variables in analogy to statistical physics. These models have been applied to study processes such as income and wealth distribution, economic inequality sources, economic growth, etc., recovering well-known concepts in the economic literature. In this work, we apply ensemble formalism to a geometric agents model to study the effect of saving propensity in a system with money, credit, and debt. We calculate the partition function to obtain the total money of the system, with which we give an interpretation of the economic temperature in terms of the different payment methods available to the agents. We observe an interplay between the fraction of money that agents can save and their maximum debt. The system's entropy increases as a function of the saved proportion, and increases even more when there is debt.

摘要

在动力学交换模型中,主体根据既定的微观规则进行交易,这些规则类似于统计物理学那样产生宏观变量。这些模型已被用于研究诸如收入和财富分配、经济不平等根源、经济增长等过程,重现了经济文献中一些广为人知的概念。在这项工作中,我们将系综形式主义应用于一个几何主体模型,以研究储蓄倾向在一个存在货币、信贷和债务的系统中的影响。我们计算配分函数以获得系统的总货币量,据此我们根据主体可用的不同支付方式对经济温度给出一种解释。我们观察到主体能够储蓄的货币比例与其最大债务之间的相互作用。系统的熵随着储蓄比例的增加而增加,并且在存在债务时增加得更多。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/092a/7915829/316824afcca9/entropy-23-00196-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/092a/7915829/99f5f3a63167/entropy-23-00196-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/092a/7915829/40085087f17c/entropy-23-00196-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/092a/7915829/3f44928041a3/entropy-23-00196-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/092a/7915829/07f417186d7e/entropy-23-00196-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/092a/7915829/cf7190253348/entropy-23-00196-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/092a/7915829/316824afcca9/entropy-23-00196-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/092a/7915829/99f5f3a63167/entropy-23-00196-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/092a/7915829/40085087f17c/entropy-23-00196-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/092a/7915829/3f44928041a3/entropy-23-00196-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/092a/7915829/07f417186d7e/entropy-23-00196-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/092a/7915829/cf7190253348/entropy-23-00196-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/092a/7915829/316824afcca9/entropy-23-00196-g006.jpg

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本文引用的文献

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Entropy (Basel). 2020 Mar 28;22(4):386. doi: 10.3390/e22040386.
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Wealth distribution under the spread of infectious diseases.传染病传播下的财富分配。
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An Approach towards Increasing Prediction Accuracy for the Recovery of Missing IoT Data Based on the GRNN-SGTM Ensemble.
基于 GRNN-SGTM 集成的物联网缺失数据恢复预测精度提升方法。
Sensors (Basel). 2020 May 4;20(9):2625. doi: 10.3390/s20092625.
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Phys Rev Lett. 2018 Mar 30;120(13):138301. doi: 10.1103/PhysRevLett.120.138301.
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