打开战略潘多拉魔盒：条件交易机制

中文摘要 AI 辅助

条件交易引擎（CTE）为离线用户执行条件指令。本文形式化了每次引擎调用中的机制设计问题。条件交易机制（CTM）决定首先评估哪些待处理条件，因为每次评估都会延迟最终的写入。我们将此问题建模为战略潘多拉（Strategic Pandora），即具有独立伯努利盒子的潘多拉盒子模型的折扣变体。智能体报告私下评估的成功概率，在完整模型中，还报告写入动作的价值。我们提出了报告-魏茨曼机制（RW）和报告价值第二价格机制（RWSP）。为了在没有共同先验的情况下比较收入，我们引入了动态无投注收入（dynamic No-Betting Revenue）。在所述竞争和均衡条件下，RW或RWSP的每个合格纯均衡都能获得其相应动态NBR基准的恒定比例收入。

英文摘要

Conditional transaction engines (CTEs) execute conditional instructions for offline users. This paper formalizes the mechanism-design problem within each engine invocation. A conditional transaction mechanism (CTM) decides which pending conditions to evaluate first because each evaluation delays the eventual write. We model this problem as Strategic Pandora, a discounted variant of the Pandora's box model with independent Bernoulli boxes. Agents report privately assessed success probabilities and, in the full model, values for the write action. We propose the reported-Weitzman mechanism (RW) and the reported-values second-price mechanism (RWSP). To compare revenue without a common prior, we introduce dynamic No-Betting Revenue. Under the stated competition and equilibrium conditions, every qualifying pure equilibrium of RW or RWSP earns a constant fraction of its corresponding dynamic NBR benchmark.

发表机构

• Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University（哈佛工程学院与应用科学学院，哈佛大学）
• Blavatnik School of Computer Science and AI, Tel Aviv University（布拉瓦特尼克计算机科学与人工智能学院，特拉维夫大学）
• Department of Computer Science, Rutgers University（计算机科学系，罗格斯大学）

机构由 AI 辅助整理，请以论文原文为准。