AI 中文总结
针对存在无组合约束的商品束市场，构建组合买方出价双向拍卖，证明其继承大市场约束特性，模拟显示无组合是有限市场关键摩擦。
AI 中文摘要
我们研究商品以束形式具有价值的市场中的双向拍卖，例如数据、模型权重和微调AI资产等市场。此类市场的一个关键摩擦是“无组合（No Assembly）”：平台可能因法律或技术原因，无法将不同卖家提供的组件组合成单个束。我们在该约束下构建了组合买方出价双向拍卖。在明确的稳定性和价格影响条件下（该条件在一般情况下成立，且对于两种商品可从局部价格接受性和反馈界推导得出），每个束子市场继承了单商品双向拍卖的大市场约束：出价谎报（bid shading）消失，清算价格集中在竞争水平并跟踪共同价值（即价格发现）。关键的激励步骤是，对束出价不会产生超出单商品逻辑的一阶策略性扭曲，该结论针对两种商品已被证明；对于更大的商品集合，该结论仍为维持性条件。多智能体强化学习模拟对福利损失进行了分解，结果表明，“无组合（No Assembly）”而非策略性谎报是有限市场的关键摩擦，在中等厚度的市场中两种损失均较小，且随商品间互补性增强而下降。
英文摘要
We study double auctions for markets in which goods are valuable in bundles, such as data, model weights, and fine-tuned AI assets. A key friction in such markets is No Assembly: a platform may be unable, for legal or technical reasons, to combine components supplied by different sellers into a single bundle. We formulate a combinatorial buyer's-bid double auction under this constraint. Under explicit stability and price-influence conditions (maintained in general, and for two goods derived from local price-taking and a feedback bound), each bundle submarket inherits the large-market discipline of single-good double auctions: bid shading vanishes, and clearing prices concentrate on competitive levels and track the common value (price discovery). The key incentive step, that bidding on a bundle creates no first-order strategic distortion beyond the single-good logic, is proved for two goods; for larger item sets it remains a maintained condition. Multi-agent reinforcement-learning simulations decompose the welfare loss and indicate that No Assembly, not strategic shading, is the binding finite-market friction, with both losses small in moderately thick markets and declining with complementarity amongst goods.
Comments67 pages, 8 figures, 6 tables
