Gini estimation under infinite variance

Fontanari, Andrea; Taleb, Nassim Nicholas; Cirillo, Pasquale

doi:10.1016/j.physa.2018.02.102

Cited by 27 publications

(11 citation statements)

References 23 publications

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“…where i and j are indices to run through the entire population A of agents who have S index surplus resources. Though there are many problems with describing whole distributions with a single number, by computing the Gini Coefficient over the entire population rather than sampling the population, by not assuming a particular distribution, and by only using Gini Coefficients as relative measures for comparing populations of the same size, these inaccuracies can be minimized (Fontanari et. al., 2018).…”

Section: Inequality Sensitivities To Agent Characteristicsmentioning

confidence: 99%

Population and Inequality Dynamics in Simple Economies

Stevenson¹

2021

Preprint

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While the use of spatial agent-based and individual-based models has flourished across many scientific disciplines, the complexities these models generate are often difficult to manage and quantify. This research reduces population-driven, spatial modeling of individuals to the simplest configurations and parameters: an equal resource opportunity landscape with equally capable individuals; and asks the question, "Will valid complex population and inequality dynamics emerge from this simple economic model?" Two foraging economies are modeled: subsistence and surplus. The resulting, emergent population dynamics are characterized by their sensitivities to agent and landscape parameters. The various steady and oscillating regimes of single-species population dynamics are generated by appropriate selection of model growth parameters. These emergent dynamics are shown to be consistent with the equation-based, continuum modeling of single-species populations in biology and ecology. The intrinsic growth rates, carry capacities, and delay parameters of these models are implied for these simple economies. Aggregate measures of individual distributions are used to understand the sensitivities to model parameters. New local measures are defined to describe complex behaviors driven by spatial effects, especially extinctions. Inequality measures are defined and applied to surplus economies. Sensitivities of inequality dynamics to model parameters are explored. Individual wealth trajectories are examined for insight into the different dynamics of inequality distributions. This simple economic model is shown to generate significantly complex population and inequality dynamics. Model parameters generating the intrinsic growth rate have strong effects on these dynamics, including large variations in inequality. Significant inequality effects are shown to be caused by birth costs above and beyond their contribution to the intrinsic growth rate. The highest levels of inequality are found during the initial non-equilibrium period and are driven by factors different than those driving steady state inequality.

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Section: Inequality Sensitivities To Agent Characteristicsmentioning

confidence: 99%

Population and Inequality Dynamics in Simple Economies

Stevenson¹

2021

Preprint

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“…Also, as mean age decreases to and below 10, the effects of chaotic population trajectories appear. 2 Comparisons of inequality distributions measured with a single ratio has both mathematical [8,6] and practical difficulties [21,18]. Equally sized populations are fully sampled in comparisons for the former and total wealth is also considered for the latter.…”

Section: Wealth Inequality Dynamicsmentioning

confidence: 99%

Dynamics of Wealth Inequality in Simple Artificial Societies

Stevenson¹

2021

Preprint

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A simple generative model of a foraging society generates significant wealth inequalities from identical agents on an equal opportunity landscape. These inequalities arise in both equilibrium and nonequilibrium regimes with some societies essentially never reaching equilibrium. Reproduction costs mitigate inequality beyond their affect on intrinsic growth rate. The highest levels of inequality are found during non-equilibrium regimes. Inequality in dynamic regimes is driven by factors different than those driving steady state inequality.

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“…x, which, under fat-tailed distributions, as many socio-economic variables tend to be, may be undefined. In such cases, as shown in [52], the Gini coefficient cannot be reliably estimated with non-parametric methods and will result in a downward bias emerging under fat tails.…”

Section: Spreading Indexmentioning

confidence: 99%

Ranking places in attributed temporal urban mobility networks

et al. 2020

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Drawing on the recent advances in complex network theory, urban mobility flow patterns, typically encoded as origin-destination (OD) matrices, can be represented as weighted directed graphs, with nodes denoting city locations and weighted edges the number of trips between them. Such a graph can further be augmented by node attributes denoting the various socioeconomic characteristics at a particular location in the city. In this paper, we study the spatio-temporal characteristics of "hotspots" of different types of socioeconomic activities as characterized by recently developed attribute-augmented network centrality measures within the urban OD network. The workflow of the proposed paper comprises the construction of temporal OD networks using two custom data sets on urban mobility in Rome and London, the addition of socioeconomic activity attributes to the OD network nodes, the computation of network centrality measures, the identification of "hotspots" and, finally, the visualization and analysis of measures of their spatio-temporal heterogeneity. Our results show structural similarities and distinctions between the spatial patterns of different types of human activity in the two cities. Our approach produces simple indicators thus opening up opportunities for practitioners to develop tools for real-time monitoring and visualization of interactions between mobility and economic activity in cities.

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Gini estimation under infinite variance

Cited by 27 publications

References 23 publications

Population and Inequality Dynamics in Simple Economies

Population and Inequality Dynamics in Simple Economies

Dynamics of Wealth Inequality in Simple Artificial Societies

Ranking places in attributed temporal urban mobility networks

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