随机共识动力学用于去中心化决策系统
Stochastic consensus dynamics for decentralized decision systems
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中文总结 AI 辅助
本研究通过随机网络上的随机共识模型，发现初始多数被放大、连通性加速一致且规模提升可靠性，为去中心化决策系统的共识形成提供了统一框架。
中文摘要 AI 辅助
社会、技术和经济系统中集体现象的一个基本机制是去中心化决策，其中个体代理的行为在没有中央协调的情况下由局部交互决定。共识形成是此类系统的核心特征，与区块链网络、分布式人工智能、自主多代理系统、分布式控制和集体决策网络相关。我们使用随机网络上的随机共识模型研究共识形成，并考察初始条件、网络连通性和系统规模如何塑造一致状态的出现。我们表明，随机动力学强烈放大小的初始多数，逐步抑制竞争状态，并将系统推向可预测的集体结果。网络连通性主要控制这一过程的效率：增加连通性加速局部一致的传播，并降低波动逆转初始主导状态的可能性，尽管这些收益在高度连通的网络中逐渐饱和。系统规模产生互补效应。更大的网络需要更多的个体更新才能达到一致，但它们在选择初始多数所偏好的状态方面也越来越可靠。有限规模分析表明，与不确定结果相关的初始条件范围随着网络增长而逐渐变窄，大约以系统规模的平方根倒数递减。这些结果揭示了一种集体放大机制，通过该机制，弱的初始不对称性在大型去中心化网络中变得越来越具有决定性，并为理解分布式决策系统中共识形成的效率、可预测性和可靠性提供了一个简单框架。
英文摘要
A fundamental mechanism underlying collective phenomena in social, technological, and economic systems is decentralized decision-making, in which the behavior of individual agents follows from local interactions in the absence of central coordination. Consensus formation is a central feature of such systems, with relevance to blockchain networks, distributed artificial intelligence, autonomous multi-agent systems, distributed control, and collective decision networks. We investigate consensus formation using a stochastic consensus model on random networks and examine how initial conditions, network connectivity, and system size shape the emergence of unanimous states. We show that the stochastic dynamics strongly amplify small initial majorities, progressively suppress the competing state, and drive the system toward a predictable collective outcome. Network connectivity primarily controls the efficiency of this process: increasing connectivity accelerates the propagation of local agreement and reduces the likelihood that fluctuations reverse the initially dominant state, although these gains gradually saturate in highly connected networks. System size produces a complementary effect. Larger networks require more individual updates to reach unanimity, but they are also increasingly reliable in selecting the state favored by the initial majority. Finite-size analysis shows that the range of initial conditions associated with uncertain outcomes becomes progressively narrower as the network grows, decreasing approximately with the inverse square root of the system size. These results reveal a collective amplification mechanism by which weak initial asymmetries become increasingly decisive in large decentralized networks, and they provide a simple framework for understanding the efficiency, predictability, and reliability of consensus formation in distributed decision systems.
发表机构
- Universidade de Pernambuco（伯南布哥联邦大学）
- Universidade Federal de Pernambuco（伯南布哥联邦大学）
- Photrek, Inc.（Photrek公司）
- SUNY Polytechnic Institute（纽约州立大学理工学院）
- Dartmouth（达特茅斯学院）
- Jiangsu University（江苏大学）
机构由 AI 辅助整理，请以论文原文为准。
