AI 中文总结
本文提出“相互投标困境”博弈论模型，分析陌生人共谋形成机制，证明互惠非理性，需执行机制维持，并模拟显示检测困难、危害分布性，随机分配为有效防御。
AI 中文摘要
论文投标是大型计算机科学会议审稿人分配的第一步：审稿人声明兴趣，由优化器结合自动亲和度分数进行分配。从未谋面的审稿人在线招募彼此，交换标识符，对彼此的论文进行投标，并以虚高的评分作为回报。现有的共谋模型假设已经信任的同事；公开招募消除了这一假设以及使此类安排得以运作的机制。我们首次提出了同行评审中共谋形成的博弈论模型，即“相互投标困境”：一个四阶段博弈，涵盖招募、在被举报风险下交换标识符、不可验证的投标以及互惠评审。该模型预测这种安排无法形成：一旦被分配了合作伙伴的论文，撰写虚高评审纯粹是成本，因为收益取决于合作伙伴自身的决定。对于任何收益，互惠都不是个体理性的，因此这种安排会瓦解。弥补这一差距的是执行机制，而非激励：作者能看到自己的评审，截止日期每隔几个月重复一次，群体记得谁进行了互惠。我们推导了虚高可持续的条件、超过该检测率则任何合作关系都无法存续的阈值，并表明努力程度同时影响两者。在标定的端到端会议模拟中，我们测试的所有检测器针对一个伪装投标、将投标散布在环中并操纵亲和度的攻击者，其F1分数均不超过0.322；两人合作安排价值相当于3.2个百分点的录用概率；且危害是分布性的而非总体性的：70篇诚实论文被挤掉，而平均质量仅变动0.002，因此没有任何汇总统计量能揭示这一点。随机分配是唯一能触及执行机制本身的防御手段，它使得从未投标的合作伙伴与投标但失败的合作伙伴无法区分。
英文摘要
Paper bidding is the entry point to reviewer assignment at large CS conferences: reviewers declare interest, combined by an optimizer with automated affinity scores. Reviewers who have never met recruit each other online, exchange identifiers, bid on each other's papers, and reciprocate with inflated scores. Existing collusion models assume already-trusted colleagues; open recruitment removes that assumption and the mechanism that made such arrangements work. We give the first game-theoretic model of collusion \emph{formation} in peer review, the \emph{Mutual Bidding Dilemma}: a four-stage game covering recruitment, exchange of identifiers under risk of being reported, unverifiable bidding, and reciprocal reviewing. The model predicts the arrangement cannot form: once assigned a partner's paper, writing the inflated review is pure cost, since the benefit depends on the partner's own decision. Reciprocation is never individually rational, for any payoffs, and the arrangement unwinds. What closes the gap is enforcement, not incentives: authors see their own reviews, deadlines recur every few months, and the group remembers who reciprocated. We derive the condition under which inflation is sustainable, the detection rate above which no partnership survives, and show effort enters both. On a calibrated end-to-end conference simulation, no detector we test exceeds $F_1 = 0.322$ against an attacker who camouflages bids, spreads them around a ring, and manipulates affinity; a two-person arrangement is worth $3.2$ points of acceptance probability; and the harm is distributional, not aggregate: $70$ honest papers are displaced while mean quality moves by only $0.002$, so no summary statistic reveals it. Randomized assignment is the one defense reaching enforcement itself, making a partner who never bid indistinguishable from one who bid and lost.
