We study routing games in which travelers optimize over routes that are remembered or surfaced, rather than over a fixed exogenous action set. The paper develops a tractable design theory for endogenous recall and then connects it back to an explicit finite-memory micro model. At the micro level, each traveler carries a finite memory state, receives surfaced alternatives, chooses via a logit rule, and updates memory under a policy such as LRU. This yields a stationary Forgetful Wardrop Equilibrium (FWE); existence is proved under mild regularity, and uniqueness follows in a contraction regime for the reduced fixed-point map. The paper's main design layer is a stationary salience model that summarizes persistent memory and interface effects as route-specific weights. Salience-weighted stochastic user equilibrium is the unique minimizer of a strictly convex potential, which yields a clean optimization and implementability theory. In this layer we characterize governed implementability under ratio budgets and affine tying constraints, and derive constructive algorithms on parallel and series-parallel networks. The bridge between layers is exact for last-choice memory (B=1): the micro model is then equivalent to the salience model, so any interior salience vector can be realized by an appropriate surfacing policy. For larger memories, we develop an explicit LRU-to-TTL-to-salience approximation pipeline and add contraction-based bounds that translate surrogate-map error into fixed-point and welfare error. Finally, we define a Recall Braess Paradox, in which improving recall increases equilibrium delay without changing physical capacity, and show that it can arise on every two-terminal network with at least two distinct s-t paths. Targeted experiments support the approximation regime, governed-design predictions, and the computational advantages of the reduced layer.

翻译：本文研究旅行者基于被记住或呈现的路径而非固定外生行动集进行优化的路径博弈问题。论文构建了可处理的内生回忆设计理论，并将其与显式有限记忆微观模型建立联系。在微观层面，每位旅行者携带有限记忆状态，接收呈现的备选方案，通过logit规则进行选择，并在LRU等策略下更新记忆。这产生了平稳的遗忘型沃德罗普均衡（FWE）；我们在温和正则性条件下证明了其存在性，并在收缩机制下通过简化不动点映射证明了其唯一性。论文的主要设计层面提出了稳态显著性模型，将持久记忆与界面效应总结为路径特定权重。显著性加权随机用户均衡是严格凸势函数的唯一极小值点，由此得到简洁的优化与可实施性理论。在此层面，我们刻画了比率预算与仿射耦合约束下的可控可实施性，并推导了并行与串并联网络上的构造性算法。当记忆容量为B=1时，微观模型与显著性模型等价，任何内点显著性向量均可通过适当的呈现策略实现。针对更大记忆容量，我们开发了显式的LRU-to-TTL-to-显著性近似管道，并增加了基于收缩的界限，将代理映射误差转化为不动点与福利误差。最后，我们定义了"回忆布拉斯悖论"——在物理容量不变的情况下改善回忆反而增加均衡延迟的现象，并证明该现象可在每条具有至少两条s-t路径的双端点网络上出现。针对性实验验证了近似框架、可控设计预测以及简化层面的计算优势。