图形协调博弈中的风险-安全权衡
A risk-security tradeoff in graphical coordination games
AI总结：
本文研究图形协调博弈中系统运营商在对抗广泛与聚焦攻击时的安全-性能权衡，发现随机选择决策过程可显著改善该权衡，并揭示网络化分布式系统韧性设计的关键挑战。
摘要：
依赖决策者集体行为的系统可能容易受到各种对抗性攻击。系统运营商在面对这些风险时能在多大程度上保护性能？我们在图形协调博弈的背景下提出这一问题，其中网络中的智能体在两种约定之间进行选择，并从协调邻居中获得收益，系统性能以智能体的福利来衡量。在本文中，我们评估了运营商缓解两类对抗性攻击的能力——1) 广泛攻击，即对手激励网络中的所有智能体；2) 聚焦攻击，即对手可以强制选定的智能体子集承诺遵循规定的约定。作为缓解策略，系统运营商可以实施一类分布式算法来管理智能体的决策过程。我们的主要贡献是刻画了运营商在针对最坏情况广泛攻击的安全性与针对聚焦攻击的脆弱性之间的基本权衡。我们表明，当运营商随机选择决策过程时，这种权衡会显著改善。我们的工作强调了系统运营商在维护网络化分布式系统韧性时所面临的设计挑战。
英文摘要：
A system relying on the collective behavior of decision-makers can be vulnerable to a variety of adversarial attacks. How well can a system operator protect performance in the face of these risks? We frame this question in the context of graphical coordination games, where the agents in a network choose among two conventions and derive benefits from coordinating neighbors, and system performance is measured in terms of the agents' welfare. In this paper, we assess an operator's ability to mitigate two types of adversarial attacks - 1) broad attacks, where the adversary incentivizes all agents in the network and 2) focused attacks, where the adversary can force a selected subset of the agents to commit to a prescribed convention. As a mitigation strategy, the system operator can implement a class of distributed algorithms that govern the agents' decision-making process. Our main contribution characterizes the operator's fundamental trade-off between security against worst-case broad attacks and vulnerability from focused attacks. We show that this tradeoff significantly improves when the operator selects a decision-making process at random. Our work highlights the design challenges a system operator faces in maintaining resilience of networked distributed systems.
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。
