{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T18:40:58Z","timestamp":1732041658727},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>Elkind et al. (AAAI'21) introduced a model for deliberative coalition formation, where a community wishes to identify a strongly supported proposal from a space of alternatives, in order to change the status quo. In their model, agents and proposals are points in a metric space, agents' preferences are determined by distances, and agents deliberate by dynamically forming coalitions around proposals that they prefer over the status quo. The deliberation process operates via k-compromise transitions, where agents from k (current) coalitions come together to form a larger coalition in order to support a (perhaps new) proposal, possibly leaving behind some of the dissenting agents from their old coalitions. A deliberation succeeds if it terminates by identifying a proposal with the largest possible support. For deliberation in d dimensions, Elkind et al. consider two variants of their model: in the Euclidean model, proposals and agent locations are points in R^d and the distance is measured according to ||...||_2; and in the hypercube model, proposals and agent locations are vertices of the d-dimensional hypercube and the metric is the Hamming distance. They show that in the Euclidean model 2-compromises are guaranteed to succeed, but in the hypercube model for deliberation to succeed it may be necessary to use k-compromises with k &gt;= d. \nWe complement their analysis by \n (1) proving that in both models it is hard to find a proposal with a high degree of support, and even a 2-compromise transition may be hard to compute;\n (2) showing that a sequence of 2-compromise transitions may be exponentially long;\n (3) strengthening the lower bound on the size of the compromise for the d-hypercube model from d to 2^\u2126(d).<\/jats:p>","DOI":"10.1609\/aaai.v36i5.20428","type":"journal-article","created":{"date-parts":[[2022,7,4]],"date-time":"2022-07-04T09:22:29Z","timestamp":1656926549000},"page":"4975-4982","source":"Crossref","is-referenced-by-count":1,"title":["Complexity of Deliberative Coalition Formation"],"prefix":"10.1609","volume":"36","author":[{"given":"Edith","family":"Elkind","sequence":"first","affiliation":[]},{"given":"Abheek","family":"Ghosh","sequence":"additional","affiliation":[]},{"given":"Paul","family":"Goldberg","sequence":"additional","affiliation":[]}],"member":"9382","published-online":{"date-parts":[[2022,6,28]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/20428\/20187","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/20428\/20187","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,4]],"date-time":"2022-07-04T09:22:29Z","timestamp":1656926549000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/20428"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,28]]},"references-count":0,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2022,6,30]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v36i5.20428","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2022,6,28]]}}}