Justifying Social-Choice Mechanism Outcome for Improving Participant Satisfaction
Justifying Social-Choice Mechanism Outcome for Improving Participant Satisfaction
复制标题
证明社会选择机制成果的合理性以提高参与者满意度
DOI：
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复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Bar
中科院分区：
文献类型：
--
作者：
Sharadhi Alape Suryanarayana;Bar
在许多社会选择机制中，所产生的选择对于一些参与者来说不是最优选的，因此需要以提高所述参与者的接受度和满意度的方式来证明所做出的选择的方法。提供这种解释的一种自然方法是要求人们提供它们，例如，通过众包，并选择最有说服力的论点中收到的。在本文中，我们提出了一种替代方法的使用，一个自动生成的理论机制设计文献中发现理想的机制功能的基础上的解释。我们通过一系列广泛的实验进行了600多名参与者的排名投票，一个经典的社会选择机制的有效性测试的方法。对结果的分析表明，在这种情况下，解释确实会影响对结果的平均满意度和接受度。特别是，当结果（在我们的情况下获胜的候选人）是参与者最不希望的选择时，解释对满意度和接受度有积极的影响。一项比较分析表明，自动生成的解释与成本更高的众包解释相比，其结果的满意度和接受程度相似，因此无需让人类参与其中。此外，与众包的解释相比，自动生成的解释大大降低了参与者认为应该选出不同的赢家的信念。
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.
预算公平租金司
DOI：
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发表时间：
2018
期刊：
AAAI 2018
影响因子：
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作者：
Procaccia, A. D.;Velez, R. A.;Yu, D.
通讯作者：
Yu, D.
没有责任就没有可解释性：交互式机器学习中解释和反馈的实证研究
DOI：
10.1145/3313831.3376624
发表时间：
2020
期刊：
CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
影响因子：
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作者：
Smith-Renner, Alison;Fan, Ron;Birchfield, Melissa;Wu, Tongshuang;Boyd-Graber, Jordan;Weld, Daniel S.;Findlater, Leah
通讯作者：
Findlater, Leah
可解释的投票
DOI：
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发表时间：
2020
期刊：
NeurIPS
影响因子：
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作者：
Peters, Dominik;Procaccia, Ariel D.
通讯作者：
Procaccia, Ariel D.