In many applications, ads are displayed together with the prices, so as to
provide a direct comparison among similar products or services. The
price-displaying feature not only influences the consumers' decisions, but also
affects the advertisers' bidding behaviors. In this paper, we study ad auctions
with display prices from the perspective of mechanism design, in which
advertisers are asked to submit both the costs and prices of their products. We
provide a characterization for all incentive compatible auctions with display
prices, and use it to design auctions under two scenarios. In the former
scenario, the display prices are assumed to be exogenously determined. For this
setting, we derive the welfare-maximizing and revenue-maximizing auctions for
any realization of the price profile. In the latter, advertisers are allowed to
strategize display prices in their own interests. We investigate two families
of allocation policies within the scenario and identify the equilibrium prices
accordingly. Our results reveal that the display prices do affect the design of
ad auctions and the platform can leverage such information to optimize the
performance of ad delivery.

研究了广告拍卖中的显示价格对机制设计的影响，提出了所有激励兼容的拍卖策略，并使用它来设计两种情景下的拍卖，结果表明显示价格确实影响拍卖的设计并且平台可以利用这些信息来优化广告投放的表现。

广告展示价格的机制设计

Mechanism Design for Ad Auctions with Display Prices

Recommendation systems often face exploration-exploitation tradeoffs: the
system can only learn about the desirability of new options by recommending
them to some user. Such systems can thus be modeled as multi-armed bandit
settings; however, users are self-interested and cannot be made to follow
recommendations. We ask whether exploration can nevertheless be performed in a
way that scrupulously respects agents' interests---i.e., by a system that acts
as a fiduciary. More formally, we introduce a model in which a recommendation
system faces an exploration-exploitation tradeoff under the constraint that it
can never recommend any action that it knows yields lower reward in expectation
than an agent would achieve if it acted alone. Our main contribution is a
positive result: an asymptotically optimal, incentive compatible, and ex-ante
individually rational recommendation algorithm.

本文介绍了一种新的推荐系统模型，其面临探索 - 开发折衷，并可以被建模为多臂老虎机设置，由于用户具有自利性质，因此不能被强制性遵循推荐，作者提出了一种算法以探索为基础，同时考虑到代理人的利益，是一个渐进最优、刺激兼容、先验个体理性的推荐算法。

受托机构强盗

Fiduciary Bandits

We consider the problem of binary prediction with expert advice in settings
where experts have agency and seek to maximize their credibility. This paper
makes three main contributions. First, it defines a model to reason formally
about settings with selfish experts, and demonstrates that "incentive
compatible" (IC) algorithms are closely related to the design of proper scoring
rules. Designing a good IC algorithm is easy if the designer's loss function is
quadratic, but for other loss functions, novel techniques are required. Second,
we design IC algorithms with good performance guarantees for the absolute loss
function. Third, we give a formal separation between the power of online
prediction with selfish experts and online prediction with honest experts by
proving lower bounds for both IC and non-IC algorithms. In particular, with
selfish experts and the absolute loss function, there is no (randomized)
algorithm for online prediction-IC or otherwise-with asymptotically vanishing
regret.

本文研究带有机构的二元预测问题，提出了一个模型并采用激励兼容算法设计方法，证明了对于绝对损失函数，IC 算法具有很好的性能保证。同时，通过证明 IC 和非 IC 算法的下界，明确了自私专家在线预测性能与诚实专家在线预测性能之间的差异。