In many social-choice mechanisms the resulting choice is not the most
preferred one for some of the participants, thus the need for methods to
justify the choice made in a way that improves the acceptance and satisfaction
of said participants. One natural method for providing such explanations is to
ask people to provide them, e.g., through crowdsourcing, and choosing the most
convincing arguments among those received. In this paper we propose the use of
an alternative approach, one that automatically generates explanations based on
desirable mechanism features found in theoretical mechanism design literature.
We test the effectiveness of both of the methods through a series of extensive
experiments conducted with over 600 participants in ranked voting, a classic
social choice mechanism. The analysis of the results reveals that explanations
indeed affect both average satisfaction from and acceptance of the outcome in
such settings. In particular, explanations are shown to have a positive effect
on satisfaction and acceptance when the outcome (the winning candidate in our
case) is the least desirable choice for the participant. A comparative analysis
reveals that the automatically generated explanations result in similar levels
of satisfaction from and acceptance of an outcome as with the more costly
alternative of crowdsourced explanations, hence eliminating the need to keep
humans in the loop. Furthermore, the automatically generated explanations
significantly reduce participants' belief that a different winner should have
been elected compared to crowdsourced explanations.

在社交选择机制中，因为选择结果不一定是参与者的最优选择，所以需要提供一种方式来使用说明方式加强结果的接受度和满意度。本文提出一种基于理论机制设计文献中的理想机制特征自动生成说明的方法，并通过在排名投票中进行的一系列大规模实验测试了两种方法的有效性。实验结果表明，说明确实影响了参与者的满意度和接受度，尤其当结果（在我们的案例中为胜出的候选人）是参与者最不理想的选择时。自动生成说明与众包说明相比，产生了类似的满意度和结果接受度，从而消除了人为介入的需要，并且显著减少了参与者认为应该选择其他候选人的信念。

为提高参与者满意度而证明社会选择机制结果

Justifying Social-Choice Mechanism Outcome for Improving Participant Satisfaction

The wisdom of the crowd has long become the de facto approach for eliciting
information from individuals or experts in order to predict the ground truth.
However, classical democratic approaches for aggregating individual
\emph{votes} only work when the opinion of the majority of the crowd is
relatively accurate. A clever recent approach, \emph{surprisingly popular
voting}, elicits additional information from the individuals, namely their
\emph{prediction} of other individuals' votes, and provably recovers the ground
truth even when experts are in minority. This approach works well when the goal
is to pick the correct option from a small list, but when the goal is to
recover a true ranking of the alternatives, a direct application of the
approach requires eliciting too much information. We explore practical
techniques for extending the surprisingly popular algorithm to ranked voting by
partial votes and predictions and designing robust aggregation rules. We
experimentally demonstrate that even a little prediction information helps
surprisingly popular voting outperform classical approaches.

使用实用技巧将其扩展到被排名的选票，设计强大的聚合规则，证明了即使少量预测信息也有助于让惊人受欢迎的投票方法胜过传统方法。