Robust convention emergence in social networks through self-reinforcing structures dissolution

IF 2.2 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Daniel Villatoro, J. Sabater-Mir, S. Sen
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引用次数: 20

Abstract

Convention emergence solves the problem of choosing, in a decentralized way and among all equally beneficial conventions, the same convention for the entire population in the system for their own benefit. Our previous work has shown that reaching 100% agreement is not as straighforward as assumed by previous researchers, that, in order to save computational resources fixed the convergence rate to 90% (measuring the time it takes for 90% of the population to coordinate on the same action). In this article we present the notion of social instruments as a set of mechanisms that facilitate and accelerate the emergence of norms from repeated interactions between members of a society, only accessing local and public information and thus ensuring agents' privacy and anonymity. Specifically, we focus on two social instruments: rewiring and observation. Our main goal is to provide agents with tools that allow them to leverage their social network of interactions while effectively addressing coordination and learning problems, paying special attention to dissolving metastable subconventions. The first experimental results show that even with the usage of the proposed instruments, convergence is not accelerated or even obtained in irregular networks. This result leads us to perform an exhaustive analysis of irregular networks discovering what we have defined as Self-Reinforcing Structures (SRS). The SRS are topological configurations of nodes that promote the establishment and persistence of subconventions by producing a continuous reinforcing effect on the frontier agents. Finally, we propose a more sophisticated composed social instrument (observation + rewiring) for robust resolution of subconventions, which works by the dissolution of the stable frontiers caused by the Self-Reinforcing Substructures (SRS) within the social network.
通过自我强化的结构解体,社会网络中出现了稳健的惯例
公约涌现解决了选择的问题,以一种分散的方式,在所有同样有益的公约中,为了自己的利益,为系统中的全体人口选择相同的公约。我们之前的工作表明,达到100%的一致并不像以前的研究人员假设的那样简单,即为了节省计算资源,将收敛率固定为90%(测量90%的人口在同一行动上协调所需的时间)。在这篇文章中,我们提出了社会工具的概念,作为一套机制,促进和加速规范的出现,从一个社会成员之间的反复互动中,只访问本地和公共信息,从而确保代理人的隐私和匿名。具体来说,我们关注两种社会工具:重新布线和观察。我们的主要目标是为智能体提供工具,使它们能够在有效地解决协调和学习问题的同时,利用它们的社会互动网络,特别注意分解亚稳态子约定。第一个实验结果表明,即使使用所提出的工具,在不规则网络中收敛速度也没有加快,甚至没有得到收敛。这一结果使我们对不规则网络进行了详尽的分析,发现了我们所定义的自我强化结构(SRS)。SRS是节点的拓扑结构,通过对边界代理产生持续的强化效应来促进子约定的建立和持久。最后,我们提出了一种更复杂的组合社会工具(观察+重新布线),用于子约定的稳健解决,该工具通过解散社会网络中由自我强化子结构(SRS)引起的稳定边界来工作。
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来源期刊
ACM Transactions on Autonomous and Adaptive Systems
ACM Transactions on Autonomous and Adaptive Systems 工程技术-计算机:理论方法
CiteScore
4.80
自引率
7.40%
发文量
9
审稿时长
>12 weeks
期刊介绍: TAAS addresses research on autonomous and adaptive systems being undertaken by an increasingly interdisciplinary research community -- and provides a common platform under which this work can be published and disseminated. TAAS encourages contributions aimed at supporting the understanding, development, and control of such systems and of their behaviors. TAAS addresses research on autonomous and adaptive systems being undertaken by an increasingly interdisciplinary research community - and provides a common platform under which this work can be published and disseminated. TAAS encourages contributions aimed at supporting the understanding, development, and control of such systems and of their behaviors. Contributions are expected to be based on sound and innovative theoretical models, algorithms, engineering and programming techniques, infrastructures and systems, or technological and application experiences.
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