Abstract
We study a setting in which a community wishes to identify a strongly supported proposal from a large space of alternatives, in order to change the status quo. We describe a deliberation process in which agents dynamically form coalitions around proposals that they prefer over the status quo. We formulate conditions on the space of proposals and on the ways in which coalitions are formed that guarantee deliberation to succeed, that is, to terminate by identifying a proposal with the largest possible support. Our results provide theoretical foundations for the analysis of deliberative processes in systems for democratic deliberation support, such as, e.g., LiquidFeedback or Polis.
| Original language | English (US) |
|---|---|
| Title of host publication | 35th AAAI Conference on Artificial Intelligence, AAAI 2021 |
| Publisher | Association for the Advancement of Artificial Intelligence |
| Pages | 5339-5346 |
| Number of pages | 8 |
| ISBN (Electronic) | 9781713835974 |
| DOIs | |
| State | Published - 2021 |
| Event | 35th AAAI Conference on Artificial Intelligence, AAAI 2021 - Virtual, Online Duration: Feb 2 2021 → Feb 9 2021 |
Publication series
| Name | 35th AAAI Conference on Artificial Intelligence, AAAI 2021 |
|---|---|
| Volume | 6B |
Conference
| Conference | 35th AAAI Conference on Artificial Intelligence, AAAI 2021 |
|---|---|
| City | Virtual, Online |
| Period | 2/2/21 → 2/9/21 |
Funding
We thank Schloss Dagstuhl—Leibniz Center for Informatics. The paper develops ideas discussed by the authors and other participants during the Dagstuhl Seminar 19381 (Application-Oriented Computational Social Choice), summer 2019. We thank the generous support of the Braginsky Center for the Interface between Science and the Humanities. Edith Elkind was supported by the ERC Starting Grant ACCORD (GA 639945). Nimrod Talmon was supported by the Israel Science Foundation (ISF; Grant No. 630/19).
ASJC Scopus subject areas
- Artificial Intelligence