多路网络中的策略性意见操纵

中文摘要 AI 辅助

网络上的意见动态模型提供了一个框架，用于研究智能体网络如何将分散的意见聚合为共识。然而，现有模型假设智能体如实报告其意见，并未考虑平台跨多个网络聚合智能体报告、且智能体可在每个网络上策略性地报告不同意见的环境。在本文中，我们提出了一种多路网络上的策略性意见操纵模型，其中平台以注意力权重合并L个网络层，每个持有私有意见的智能体在每层选择（可能不同的）报告，并承担特定于层的误报成本，以将合并后的共识拉向自己的意见。我们证明该博弈具有闭式形式的唯一纳什均衡，所得共识是在向每个智能体的可操纵性指数倾斜的中心性下的真实共识，且所得失真为智能体可操纵性与意见的（中心性加权）协方差。我们进一步证明，智能体在每层上的报告比其意见更极端，因此平台观察到的极化高估了真实极化。值得注意的是，我们强调合并层是一把双刃剑：由于可操纵性取决于智能体在每层影响力的平方，将注意力分散到各层会稀释操纵，而跨层异质的误报成本和智能体中心性的变化则可能放大操纵。最后，我们刻画了平台对注意力权重的最优选择，当各层共享平稳分布时以闭式形式给出，否则通过精确梯度和边际检验给出。总之，我们的发现揭示了何时跨网络聚合意见对策略性操纵具有（不具有）鲁棒性，并指出了缓解操纵的潜在干预措施。

英文摘要

Models of opinion dynamics on networks, provide a framework to study how a network of agents aggregates dispersed opinions into a consensus. However, existing models assume that agents truthfully report their opinions, and do not account for environments in which a platform aggregates agents' reports across multiple networks, and agents can strategically report different opinions on each. In this paper, we propose a model of strategic opinion manipulation on multiplex networks, in which a platform merges L network layers with attention weights, and each agent, holding a private opinion, chooses (potentially different) reports on each layer, at a layer-specific misreporting cost, so as to pull the merged consensus toward their own opinion. We show that this game has a unique Nash equilibrium in closed form, that the resulting consensus is the truthful consensus under a centrality tilted toward a manipulability index of each agent, and that the resulting distortion is the (centrality-weighted) covariance of agents' manipulability and opinions. We further show that agents' reports on every layer are more extreme than their opinions, so that the polarization observed by the platform overestimates the true polarization. Notably, we highlight that merging layers is a double-edged sword: as manipulability depends on the square of an agent's influence on each layer, spreading attention across layers dilutes manipulation, while heterogeneous misreporting costs across layers and shifts in agents' centralities can amplify it. Finally, we characterize the platform's optimal choice of attention weights, in closed form when the layers share a stationary distribution, and through an exact gradient and a marginal test otherwise. Together, our findings shed light on when aggregating opinions across networks is (not) robust to strategic manipulation, and point out potential interventions to alleviate it.

发表机构

• University of California, San Diego（加州大学圣地亚哥分校）

机构由 AI 辅助整理，请以论文原文为准。