不确定性下的 Texas Shootout
The Texas Shootout under Uncertainty
AI总结：
本文研究私人估值下 Texas Shootout 机制的公司归属配置，用围绕参考分布的分布带建模奈特式不确定性，推导厌恶模糊性分割者的最优报价，并刻画效率、角色偏好及抑制过早解散的条件。
摘要：
我们研究在私人估值情形下，利用 Texas Shootout 机制将一家共同拥有的公司配置给单一所有者。我们将关于对方分布的 Knightian Uncertainty（奈特式不确定性）识别为该机制能够阻止过早解散的一个原因。通过围绕参考分布 F 的分布带来刻画不确定性，我们推导了厌恶模糊性的分割者的最优价格公告。当估值接近 F 的中位数时，分割者会对不确定性进行对冲；而在高估值和低估值时，则会榨取预期剩余。对于中位数附近的估值，该机制的结果是有效率的。风险中性的共同所有者更愿意担任选择者；在不确定性水平较低时，对任意估值都严格偏好如此，而在不确定性水平较高时，对极端估值严格偏好如此。如果各方认为估值彼此接近，则只需较少的不确定性，就能使该机制始终有效率并减少过早解散。
英文摘要：
We investigate the allocation of a co-owned company to a single owner using the Texas Shoot-Out mechanism with private valuations. We identify Knightian Uncertainty about the peer's distribution as a reason for its deterrent effect of a premature dissolving. Modeling uncertainty by a distribution band around a reference distribution $F$, we derive the optimal price announcement for an ambiguity averse divider. The divider hedges against uncertainty for valuations close to the median of $F$, while extracting expected surplus for high and low valuations. The outcome of the mechanism is efficient for valuations around the median. A risk neutral co-owner prefers to be the chooser, even strictly so for any valuation under low levels of uncertainty and for extreme valuations under high levels of uncertainty. If valuations are believed to be close, less uncertainty is required for the mechanism to always be efficient and reduce premature dissolvements.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
