网络化决策的信息架构理论：新鲜度、局部性与协调性
A Theory of Information Architecture for Networked Decisions: Freshness, Locality, and Coordination
浏览论文内容
中文总结 AI 辅助
本文针对网络化多智能体决策，提出信息架构理论，权衡信息新鲜度与范围，推导闭式阈值及最优邻域半径，揭示性能取决于最优决策的可预测性。
中文摘要 AI 辅助
网络化系统在决策所依据信息的范围与其新鲜度之间面临权衡：对系统更广泛的视角有助于更好的协调，但组装和传递这些信息需要时间，因此信息到达时已更陈旧。我们针对一个由多个智能体组成的团队研究这一权衡，该团队重复选择行动以最小化一个共享的二次成本，该成本由独立演化、不受智能体行动影响的环境驱动。信息架构规定了每个智能体观察什么、从何处观察以及以何种延迟观察；我们通过架构相对于基于完整、当前信息做出的决策所损失的成本来衡量架构。我们首先比较新鲜局部观测与完整但延迟的全局视图。当环境的每个分量以共同的指数速率去相关时，这种比较简化为延迟与相干时间之比的闭式阈值。然后我们研究中间架构，其中每个智能体基于一个时间对齐、因此更陈旧的更广邻域快照行动。最优邻域半径出现在新增范围的边际价值等于失去新鲜度的边际成本之处。在一个典型空间模型中，该半径由空间相关长度、决策相关长度和时间传播长度决定。在整个过程中，架构性能由最优决策的可预测性而非原始状态的可预测性支配。
英文摘要
Networked systems face a tradeoff between the scope of the information behind a decision and its freshness: a broader view of the system supports better coordination, but assembling and communicating it takes time, so it arrives older. We study this tradeoff for a team of agents that repeatedly choose actions to minimize a shared quadratic cost driven by an environment that evolves on its own, unaffected by the agents' actions. An information architecture specifies what each agent observes, from where, and with what delay; we measure an architecture by the cost it loses relative to a decision made with complete, current information. We first compare fresh local observations with a complete but delayed global view. When every component of the environment decorrelates at a common exponential rate, this comparison reduces to a closed-form threshold on the ratio of delay to coherence time. We then study intermediate architectures in which each agent acts on a time-aligned, and therefore older, snapshot of a wider neighborhood. The optimal neighborhood radius occurs where the marginal value of added scope equals the marginal cost of lost freshness. In a canonical spatial model, this radius is set by the spatial-correlation, decision-relevance, and temporal-propagation lengths. Throughout, architecture performance is governed by the predictability of the optimal decision rather than of the raw state.
发表机构
- University of Southern California（南加州大学）
机构由 AI 辅助整理，请以论文原文为准。
