We study the problem of agent selection in causal strategic learning under
multiple decision makers and address two key challenges that come with it.
Firstly, while much of prior work focuses on studying a fixed pool of agents
that remains static regardless of their evaluations, we consider the impact of
selection procedure by which agents are not only evaluated, but also selected.
When each decision maker unilaterally selects agents by maximising their own
utility, we show that the optimal selection rule is a trade-off between
selecting the best agents and providing incentives to maximise the agents'
improvement. Furthermore, this optimal selection rule relies on incorrect
predictions of agents' outcomes. Hence, we study the conditions under which a
decision maker's optimal selection rule will not lead to deterioration of
agents' outcome nor cause unjust reduction in agents' selection chance. To that
end, we provide an analytical form of the optimal selection rule and a
mechanism to retrieve the causal parameters from observational data, under
certain assumptions on agents' behaviour. Secondly, when there are multiple
decision makers, the interference between selection rules introduces another
source of biases in estimating the underlying causal parameters. To address
this problem, we provide a cooperative protocol which all decision makers must
collectively adopt to recover the true causal parameters. Lastly, we complement
our theoretical results with simulation studies. Our results highlight not only
the importance of causal modeling as a strategy to mitigate the effect of
gaming, as suggested by previous work, but also the need of a benevolent
regulator to enable it.

我们研究了多个决策者下因果战略学习中的代理选择问题，并解决了相应的两个关键挑战。首先，我们考虑了代理评估和选择过程的影响，发现最佳选择规则在选择最佳代理和提供激励以促进代理改善之间存在权衡。此外，最佳选择规则依赖于代理结果的错误预测。然后，我们研究了决策者的最佳选择规则不会导致代理结果恶化或不公正减少代理选择机会的条件。其次，当存在多个决策者时，选择规则之间的干扰引入了估计潜在因果参数的另一个偏倚来源。为解决这个问题，我们提供了一个合作协议，所有决策者必须共同采用以恢复真实的因果参数。最后，我们通过模拟研究补充了理论结果。我们的结果不仅凸显了因果建模作为减轻操纵效应的策略的重要性，正如以前的工作所建议的，还凸显了需要一位慈善的监管者来使其成为可能。

具有竞争选择的因果战略学习

Causal Strategic Learning with Competitive Selection

The continuous development of Question Answering (QA) datasets has drawn the
research community's attention toward multi-domain models. A popular approach
is to use multi-dataset models, which are models trained on multiple datasets
to learn their regularities and prevent overfitting to a single dataset.
However, with the proliferation of QA models in online repositories such as
GitHub or Hugging Face, an alternative is becoming viable. Recent works have
demonstrated that combining expert agents can yield large performance gains
over multi-dataset models. To ease research in multi-agent models, we extend
UKP-SQuARE, an online platform for QA research, to support three families of
multi-agent systems: i) agent selection, ii) early-fusion of agents, and iii)
late-fusion of agents. We conduct experiments to evaluate their inference speed
and discuss the performance vs. speed trade-off compared to multi-dataset
models. UKP-SQuARE is open-source and publicly available at
this http URL

研究了多领域 Question Answering 模型，发现除了采用多数据集模型外，组合专家智能体力也可得到较大性能提升，为了便于研究者进行多代理模型的研究，将 UKP-SQuARE 平台扩展支持三种多代理系统，包括代理选择，早期融合和延迟融合，并通过实验评估他们的推理速度及性能与速度之间的权衡。

UKP-SQuARE v3: 一个多智能体问答研究平台

UKP-SQuARE v3: A Platform for Multi-Agent QA Research

We study the problem of selecting a member of a set of agents based on
impartial nominations by agents from that set. The problem was studied
previously by Alon et al. and Holzman and Moulin and has important applications
in situations where representatives are selected from within a group or where
publishing or funding decisions are made based on a process of peer review. Our
main result concerns a randomized mechanism that in expectation awards the
prize to an agent with at least half the maximum number of nominations. Subject
to impartiality, this is best possible.

研究代理人选择问题，提出一种随机机制以公正且高效的选择代表，可应用于同行评议、人员选拔等情况。