Robust coordination in adversarial social networks: From human behavior to agent-based modeling
Robust coordination in adversarial social networks: From human behavior to agent-based modeling
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DOI：
10.1017/nws.2021.5
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发表时间：
2021-09-01
期刊：
影响因子：
1.7
通讯作者：
Vorobeychik，Yevgeniy
中科院分区：
文献类型：
--
作者：
Hajaj，Chen;Joveski，Zlatko;Vorobeychik，Yevgeniy
去中心化协调是社会和组织面临的基本挑战之一。尽管从各种角度进行了广泛的探讨，但受到有限关注的一个问题是在敌对代理存在的情况下的人类协调。我们通过将人类受试者定位为网络上的节点，并赋予每个节点一个角色来研究这个问题，可以是常规角色（目标是在所有常规玩家之间达成共识），也可以是对抗性角色（旨在阻止常规玩家之间达成共识）。我们表明，对抗性节点确实在阻止共识方面非常成功。然而，我们证明，拥有网络邻居之间的通信能力可以显着提高协调成功率以及对抗节点的弹性。我们对通信的分析表明，敌对节点试图利用这种能力来达到自己的目的，但这样做的方式有些有限，可能是为了阻止常规节点识别他们的意图。此外，我们表明，可信节点的存在通常价值有限，但当存在许多敌对节点并且玩家可以进行通信时确实有帮助。最后，我们使用实验数据开发人类行为的计算模型，并探索其他参数变化：网络拓扑和密度的特征以及布局，所有这些都使用生成的数据驱动的基于代理（DDAB）模型。
Decentralized coordination is one of the fundamental challenges for societies and organizations. While extensively explored from a variety of perspectives, one issue that has received limited attention is human coordination in the presence of adversarial agents. We study this problem by situating human subjects as nodes on a network, and endowing each with a role, either regular (with the goal of achieving consensus among all regular players), or adversarial (aiming to prevent consensus among regular players). We show that adversarial nodes are, indeed, quite successful in preventing consensus. However, we demonstrate that having the ability to communicate among network neighbors can considerably improve coordination success, as well as resilience to adversarial nodes. Our analysis of communication suggests that adversarial nodes attempt to exploit this capability for their ends, but do so in a somewhat limited way, perhaps to prevent regular nodes from recognizing their intent. In addition, we show that the presence of trusted nodes generally has limited value, but does help when many adversarial nodes are present, and players can communicate. Finally, we use experimental data to develop computational models of human behavior and explore additional parametric variations: features of network topologies and densities, and placement, all using the resulting data-driven agent-based (DDAB) model.