带预测的无偏选择
Impartial Selection with Predictions
AI总结：
该研究针对相互提名场景下的无偏智能体选择问题，引入最高提名组预测信息，给出不同场景下机制的一致性与鲁棒性界，证明可在几乎不损失鲁棒性的前提下实现渐近最优一致性。
摘要：
我们研究基于相互提名的智能体选择问题，这是一个在委员会选拔、AI对齐等领域有诸多应用的理论问题。由于智能体既是选择者也是被选择者，他们可能有动机谎报对他人资格的真实看法，以影响自身被选中的概率。无偏机制通过保证智能体的选中结果与其自身提交的提名无关，从而规避了这一问题。已有研究对无偏机制的性能建立了严格界，性能以其对获提名最多智能体的提名数的近似能力来衡量。我们研究了当无偏机制获得一组获最高提名数智能体的预测时，其性能可在多大程度上得到提升。具体而言，我们给出了这类机制的一致性和鲁棒性界，其中一致性衡量预测准确时机制的性能，鲁棒性衡量预测不准确时机制的性能。对于最多选择$k$个智能体且每个智能体可提名任意数量其他智能体的一般场景，我们提出了一种一致性为$1-O\big(\frac{1}{k}\big)$、鲁棒性为$1-\frac{1}{e}-O\big(\frac{1}{k}\big)$的机制。对于每个智能体仅投一票、仅选出单个智能体的特殊情况，我们证明可实现1-一致性，同时保证$\frac{1}{2}$-鲁棒性。与已有结果的细致对比表明，（渐近）最优的一致性可以在几乎不牺牲鲁棒性的前提下实现。
英文摘要：
We study the selection of agents based on mutual nominations, a theoretical problem with many applications from committee selection to AI alignment. As agents both select and are selected, they may be incentivized to misrepresent their true opinion about the eligibility of others to influence their own chances of selection. Impartial mechanisms circumvent this issue by guaranteeing that the selection of an agent is independent of the nominations cast by that agent. Previous research has established strong bounds on the performance of impartial mechanisms, measured by their ability to approximate the number of nominations for the most highly nominated agents. We study to what extent the performance of impartial mechanisms can be improved if they are given a prediction of a set of agents receiving a maximum number of nominations. Specifically, we provide bounds on the consistency and robustness of such mechanisms, where consistency measures the performance of the mechanisms when the prediction is accurate and robustness its performance when the prediction is inaccurate. For the general setting where up to $k$ agents are to be selected and agents nominate any number of other agents, we give a mechanism with consistency $1-O\big(\frac{1}{k}\big)$ and robustness $1-\frac{1}{e}-O\big(\frac{1}{k}\big)$. For the special case of selecting a single agent based on a single nomination per agent, we prove that $1$-consistency can be achieved while guaranteeing $\frac{1}{2}$-robustness. A close comparison with previous results shows that (asymptotically) optimal consistency can be achieved with little to no sacrifice in terms of robustness.
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。
