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arXiv 2602.12359cs.GT
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在随机估值下诚实公平分配

Truthful Fair Division under Stochastic Valuations

Google Research(谷歌研究院);Purdue University(普渡大学);Harvard University(哈佛大学)

Daniel Halpern, Alexandros Psomas, Shirley Zhang

AI总结:

在随机估值下,研究了诚实机制在公平分配和效率上的表现,提出DSIC机制在不同情况下实现福利近似和公平性。

摘要:

我们研究了在随机模型下为具有加性偏好的战略代理分配不可分割物品的无钱机制。在此模型中,物品的价值从底层分布中抽取,机制是根据此抽取进行评估(例如,在期望值或以高概率)。受最坏情况不可能性所启发,这些不可能性表明诚实性严重限制了公平性和效率,我们询问诚实机制是否在随机实例上仍然表现不佳。我们首先专注于占优策略激励相容(DSIC)机制。对于两个代理,我们获得了一个清晰的图景。具体来说,我们证明在某种分布下,没有任何DSIC机制能够实现预期福利近似优于(2+√2)/4≈0.854,并且给出一个DSIC机制,该机制在所有分布上同时达到此界限。我们进一步证明,对于每一个分布,存在一个DSIC机制,以高概率实现公平,并获得相同的福利。关键成分是新的、紧致的连接,将DSIC、无钱机制的福利保证与i.i.d.先知不等式联系起来。这种连接使我们能够推广到n个代理;特别是,我们获得一个DSIC机制,实现约0.745的福利近似,以及另一个DSIC机制,实现1/2的福利近似,且以高概率公平。我们然后转向贝叶斯激励相容(BIC)。在i.i.d.估值下,我们证明BIC几乎不增加成本:我们设计了一个先验独立的BIC机制,实现了(1-ε)的最优福利近似,同时以高概率公平。在独立但非相同先验下,我们获得BIC机制,实现了(1-ε)近似帕累托效率和以高概率公平。

英文摘要:

We study no-money mechanisms for allocating indivisible items to strategic agents with additive preferences under a stochastic model. In this model, items' values are drawn from an underlying distribution and mechanisms are evaluated with respect to this draw (e.g., in expectation, or with high probability). Motivated by worst-case impossibilities which show that truthfulness severely restricts fairness and efficiency, we ask whether truthful mechanisms continue to perform poorly on random instances. We first focus on dominant-strategy incentive compatible (DSIC) mechanisms. For two agents, we obtain a tight picture. Specifically, we show that there exists a distribution under which no DSIC mechanism achieves an expected welfare approximation better than $\frac{2+\sqrt{2}}{4}\approx 0.854$, and we give a DSIC mechanism that matches this bound for all distributions simultaneously. We further show that, for every distribution, there exists a DSIC mechanism that is envy-free with high probability and obtains the same welfare. A key ingredient is a new, tight connection between welfare guarantees of a family of DSIC, no-money mechanisms and i.i.d.\ prophet inequalities. This connection allows us to generalize to $n$ agents; in particular, we obtain a DSIC mechanism that achieves a $\approx 0.745$ approximation to welfare, and another DSIC mechanism achieving a $1/2$-approximation welfare that is envy-free with high probability. We then turn to Bayesian incentive compatibility (BIC). Under i.i.d.\ valuations, we show that BIC comes at essentially no cost: we design a prior-independent BIC mechanism that achieves a $(1-\varepsilon)$-approximation to the optimal welfare, while being envy-free with high probability. Under independent but non-identical priors, we obtain BIC mechanisms that are $(1-\varepsilon)$-approximately Pareto efficient and envy-free with high probability.

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。

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