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.
|Title of host publication||International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2022|
|Publisher||International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)|
|Number of pages||10|
|State||Published - 2022|
|Event||21st International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2022 - Auckland, Virtual, New Zealand|
Duration: 9 May 2022 → 13 May 2022
|Name||Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS|
|Conference||21st International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2022|
|Period||9/05/22 → 13/05/22|
Bibliographical noteFunding Information:
Acknowledgements: This research has been partly supported by the Israeli Ministry of Science & Technology (grants No. 89583), Israel Science Foundation under grant 1958/20, EU Project TAILOR under grant 992215 and by the Data Science Institute at Bar-Ilan University.
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- Mechanism Design
- Social Choice