Statistical dynamics of wealth inequality in stochastic models of growth

Kemp, Jordan T.; Bettencourt, Luís M. A.

doi:10.1016/j.physa.2022.128180

Cited by 7 publications

(8 citation statements)

References 29 publications

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“…Particularly, agents have different initial conditions of knowledge, they experience different environmental stochastic histories, and they may have different models of the world in terms of their likelihood functions. We will now explore these sources of information heterogeneity and show that with a shared statistical signal, a population can reverse the (dominant) effects of heterogeneity on growth and inequality ( 14 ).…”

Section: Population Effects Of Information Dynamicsmentioning

confidence: 99%

“…In much of this work, agents representing individuals or households (often with life cycles) grow or lose wealth through a multiplicative (geometric) stochastic process. This modeling choice is well supported empirically and introduces a number of key parameters as an agent’s resources (or wealth), r , evolve exponentially with mean growth rate (over time), γ , fluctuate with standard deviation (volatility), σ ( 3 , 12 , 13 ), and vary across individuals of a population with standard deviation

( 14 , 15 ). These parameters describe the statistical dynamics of wealth in heterogeneous populations and the emergence of inequality across various timescales.…”

Section: Introductionmentioning

confidence: 99%

“…The statistics of heterogeneous growth rates are particularly important, as they are the predominant drivers of inequality over long times ( 14 ). In such contexts, agents with higher average growth rates amass more relative wealth, reducing social mobility across populations.…”

Section: Introductionmentioning

confidence: 99%

“…We finish by exploring the general consequences of learning a shared environment on the statistics of information and resources, and discuss the consequences for the role of general education and training on population dynamics and its potential to reverse long-term wealth inequality ( 14 ).…”

Section: Introductionmentioning

confidence: 99%

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Learning increases growth and reduces inequality in shared noisy environments

Kemp

Bettencourt

2023

PNAS Nexus

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Stochastic multiplicative dynamics characterize many complex natural phenomena such as selection and mutation in evolving populations, and the generation and distribution of wealth within social systems. Population heterogeneity in stochastic growth rates has been shown to be the critical driver of wealth inequality over long time scales. However, we still lack a general statistical theory that systematically explains the origins of these heterogeneities resulting from the dynamical adaptation of agents to their environment. In this paper, we derive population growth parameters resulting from the general interaction between agents and their environment, conditional on subjective signals each agent perceives. We show that average wealth growth rates converge, under specific conditions, to their maximal value as the mutual information between the agent’s signal and the environment, and that sequential Bayesian inference is the optimal strategy for reaching this maximum. It follows that when all agents access the same statistical environment, the learning process attenuates growth rate disparities, reducing the long-term effects of heterogeneity on inequality. Our approach shows how the formal properties of information underlie general growth dynamics across social and biological phenomena, including cooperation and the effects of education and learning on life history choices.

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Section: Population Effects Of Information Dynamicsmentioning

confidence: 99%

( 14 , 15 ). These parameters describe the statistical dynamics of wealth in heterogeneous populations and the emergence of inequality across various timescales.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Learning increases growth and reduces inequality in shared noisy environments

Kemp

Bettencourt

2023

PNAS Nexus

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“…It should be added that, although the present study used a model based on the kinetic energy exchange analogy, there is another model that uses potential function to compute probability distributions for income and expenditure [53], and a model that uses population dynamics to compute time developments for growth and inequality [54]. Future research could thus include such models that take into account the finiteness of earth resources and the sustainability of economy.…”

Section: Discussionmentioning

confidence: 99%

Wealth Redistribution and Mutual Aid: Comparison Using Equivalent/Non-Equivalent Exchange Models of Econophysics

Kato

2023

Entropy

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Given wealth inequality worldwide, there is an urgent need to identify the mode of wealth exchange through which it arises. To address the research gap regarding models that combine equivalent exchange and redistribution, this study compares an equivalent market exchange with redistribution based on power centers and a non-equivalent exchange with mutual aid using the Polanyi, Graeber, and Karatani modes of exchange. Two new exchange models based on multi-agent interactions are reconstructed following an econophysics-based approach for evaluating the Gini index (inequality) and total exchange (economic flow). Exchange simulations indicate that the evaluation parameter of the total exchange divided by the Gini index can be expressed by the same saturated curvilinear approximate equation using the wealth transfer rate and time period of redistribution, the surplus contribution rate of the wealthy, and the saving rate. However, considering the coercion of taxes and its associated costs and independence based on the morality of mutual aid, a non-equivalent exchange without return obligation is preferred. This is oriented toward Graeber's baseline communism and Karatani's mode of exchange D, with implications for alternatives to the capitalist economy.

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