Threshold Resource Redistribution in Spatially-Structured Kinship Networks

TL;DR

This paper models a threshold-based resource redistribution process in spatial kinship networks, revealing how such dynamics influence population survival, network structure, and local clustering, especially under varying resource densities and network configurations.

Contribution

It introduces a novel spatially-explicit model of resource redistribution based on kinship and examines its effects on population persistence and network heterogeneity.

Findings

Lower resource densities can still sustain populations due to redistribution.

Network heterogeneity and local clustering increase with resource redistribution.

Spatial structure and kinship influence resource sharing and population stability.

Abstract

We present a model for a threshold-based resource redistribution process in a spatially-explicit population, characterizing the relation between kinship network structure, local interactions and persistence. We find that population survival becomes possible for lower resource densities, but leads to increased network heterogeneity and locally centralized clusters. We interpret this in relation to a feedback between the kinship network structure and reproduction ability. Agents receive stochastic resources and solicit additional resources from connected individuals when below a minimum, with each agent contributing a fraction of their excess based on relatedness. We first analyze a fully-connected population with uniform redistribution fraction and discuss mean field expectations as well as finite size corrections. We extend this model to a hub-and-spoke network, exploring the impact of network asymmetry or centrality on resource distribution. We then develop a spatially-limited population model with diffusion, local pairing, reproduction and mortality. Redistribution is introduced as a function of relatedness (generational distance through most-recent common ancestor) and distance. Redistribution-dependent populations exhibit a higher level of relational closeness with increased clustering for agents of highest node strength. These results highlight the interaction of resource density, cooperation and kinship in a spatially-limited regime.