阈值资源再分配在空间结构的亲属网络中的应用
Threshold Resource Redistribution in Spatially-Structured Kinship Networks
AI总结：
该研究探讨了在空间受限条件下，基于亲属网络结构的资源再分配机制，揭示了资源密度、合作与亲属关系之间的相互作用。
摘要：
我们提出了一种基于阈值的资源再分配过程模型，用于描述在空间显性种群中亲属网络结构、局部相互作用和持续性之间的关系。我们发现，较低的资源密度使得种群生存成为可能，但会导致网络异质性增加和局部集中簇的形成。我们将这一现象与亲属网络结构与繁殖能力之间的反馈联系起来。代理在获得随机资源后，当低于最低阈值时，会向连接个体请求额外资源，每个代理根据相关性贡献其超额部分的分数。我们首先分析了一个完全连接的种群，具有均匀的再分配比例，并讨论了均场期望以及有限大小修正。我们扩展了这一模型，使其适用于枢纽- spokes网络，探讨了网络不对称性或中心性对资源分配的影响。然后，我们开发了一个具有扩散、局部配对、繁殖和死亡的空间有限种群模型。再分配被定义为相关性（世代距离通过最近共同祖先）和距离的函数。具有再分配的种群表现出更高的关系紧密度，对于节点强度最高的代理，集群程度增加。这些结果突显了在空间受限条件下资源密度、合作和亲属之间的相互作用。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
