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极端不平等的财富分配的动力模型:对它们在随机交换中的稳定性的数值研究
Asim Ghosh1, Suchismita Banerjee2, Sanchari Goswami3
1Department of Physics, Raghunathpur College, Raghunathpur, Purulia 723133, India.
Entropy (Basel, Switzerland)
|July 29, 2023
概括
动力交换模型探索财富集中. 班纳吉模型显示了极端的财富凝聚,但随机交换阻止了这一点,提供了对经济不平等动态的见解.
科学领域:
- 经济建模经济建模
- 统计物理学的统计物理.
- 计算经济学是计算机经济学.
背景情况:
- 全球财富不平等是一个重大问题,少数精英拥有不成比例的财富.
- 动力交换模型用于模拟市场动态和财富分配.
研究的目的:
- 调查动力交换模型是否可以复制极端财富集中.
- 分析了Chakraborti,Bannerjee和Goswami-Sen模型的统计属性. 为了分析Chakraborti,Bannerjee和Goswami-Sen模型的统计属性.
主要方法:
- 探索动力交换模型,包括Chakraborti和Bannerjee模型.
- 使用蒙特卡洛模拟来研究统计特征.
- 分析随机交换概率对财富凝聚的影响.
主要成果:
- Chakraborti模型导致财富在一个人身上完全凝聚起来.
- 班纳吉模型的结果是,大约99.98%的财富由十个代理人持有.
- 引入随机交换或使用像戈斯瓦米-森这样的模型可以防止财富的凝聚.
结论:
- 动力交换模型可以显示极端的财富集中,反映现实世界的不平等.
- 随机交换概率对于减轻财富凝结至关重要.
- 模型动态,例如戈斯瓦米-森模型中的模型,可以本质上防止极端的财富差异.
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