Search arXiv⌕ Search

arXiv · 2610.08143

A New Model for the Income Distribution

Abstract

In this study, we propose a kinetic trap--diffusion model to describe the emergence of Pareto distributions in money-exchange systems. Using kinetic Monte Carlo simulations, we show that the Pareto exponent depends explicitly on temperature and takes values in the range $0.5 \leq ν(T) \leq 1.5$. In the present framework, the temperature $T$ acts as a control parameter that regulates the exchange dynamics through thermally activated diffusion. Unlike conventional kinetic exchange models, where the Pareto exponent is fixed by microscopic rules, the proposed model generates a range of Pareto exponents as a function of $T$. The temperature-dependent Pareto exponent constitutes the central novelty of the proposed kinetic trap--diffusion framework. This feature provides a natural explanation for the empirically observed variations in Pareto exponents across different countries and economic conditions. In addition, the fraction of zero-wealth agents and the Gini index exhibit a non-monotonic dependence on temperature, revealing distinct dynamical regimes arising from the competition between trapping and money mobility. The persistence of the Pareto-like stationary distribution under strongly nonuniform initial conditions further supports the robustness of the proposed mechanism. These results show that different Pareto-tail and inequality regimes can emerge from the same trap--diffusion dynamics through changes in a single control parameter

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ekrem Aydiner. 2026-10-06. A New Model for the Income Distribution. https://doi.org/10.1016/j.physa.2026.132084

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Who Waits the Longest? Unequal Paths to Legal Stability among Migrants and Refugees

Legal systems shape not only the recognition of migrants and refugees but also the pace and stability of their integration. Refugees often shift between multiple legal classifications, a process we refer to as the "legal journey". This journey is frequently prolonged and uncertain. Using a network-based approach, we analyze legal transitions for over 350,000 migrants in Austria (2022-2024). EU nationals such as Germans reach stable residence in under four months; refugees from conflict-affected regions face far longer and more uncertain journeys, ranging from two months for Ukrainians to nine months for Syrians and 20 months for Afghans. Refugee women, especially from Syria and Afghanistan, are more likely to gain protection. Afghan women reach stability in 14 months on average, less than half the 30 months Afghan men wait. We also find that those who cross the border without going through official border controls face higher exit rates and lower chances of securing a stable status. We show that legal integration is not a uniform process, but one structured by institutional design, entry mode, and unequal timelines.

physics.soc-ph↗

Higher-order interactions reveal synergistic backbones of cycling infrastructure networks

Infrastructure networks essentially underlie human mobility and transport. Improving the quality of single links increases network performance locally. However, efficient transport requires high-quality connected corridors across multi-link paths that do not emerge from independent single-link upgrades. Here, we introduce a framework for evaluating the impact of jointly upgrading multiple links as inherently higher-order interactions, enabling us to quantify link synergies in complex transport networks. Two links are synergistic if an upgrade of one increases the benefit of upgrading the other, promoting upgrades of topologically complementary links along the same path while discouraging upgrades of redundant parallel links. By expressing these synergies as second-order derivatives of overall network performance, we develop an efficient computational framework to identify synergistic links that form a connected network backbone. We apply our theoretical framework by combining empirical street network and cycling demand data for Hamburg, Germany, with a perturbed utility route choice model for urban bicycle traffic. Our results reveal synergies from higher-order interactions, thereby enabling strategic infrastructure planning that goes beyond local link importance in complex transport and flow networks.

physics.soc-ph↗

Probabilistic neighbors' selection competes with confirmation bias in a bounded confidence model

In this work, we investigate three modified versions of the classic Hegselmann-Krause opinion dynamics model, incorporating features that are typical of many real-world systems, such as uncertainty in the selection of interacting agents and a weighted evaluation of the relevance of their opinions in the influence function to enhance confirmation bias. Through extensive simulations across different network topologies, ranging from stylized network models (Barabási-Albert, Erdős-Rényi, and Watts-Strogatz networks) to empirical networks with community structure, we identify the influence of each modification on opinion evolution and convergence. Our findings reveal that they exert opposite effects on the bounded confidence threshold required for consensus. We further explore an extension based on a data-driven opinion initialization on the empirical networks, where initial opinions are drawn from Gaussian distributions specific to each detected community. While the qualitative effects of the three modified models remain consistent, this new initialization strategy reveals distinct dynamics within the network communities. These insights provide a new and comprehensive perspective on how realistic variations of the Hegselmann-Krause model, in terms of both interaction rules and initial opinion distributions, affect opinion dynamics and shed light on the mechanisms of consensus formation within structured communities.

physics.soc-ph↗