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When agents self-determine options from which they choose, for instance, their resource consumption and production, while these choices have a collective systemwide impact, optimal decision-making turns into a combinatorial optimization problem known as NP-hard. In such challenging computational problems, centrally managed (deep) learning systems often require personal data with implications on privacy and citizens\u2019 autonomy. This article envisions an alternative unsupervised and decentralized collective learning approach that preserves privacy, autonomy, and participation of multi-agent systems self-organized into a hierarchical tree structure. Remote interactions orchestrate a highly efficient process for\n            <jats:italic>decentralized collective learning<\/jats:italic>\n            . This disruptive concept is realized by I-EPOS, the\n            <jats:italic>Iterative Economic Planning and Optimized Selections<\/jats:italic>\n            , accompanied by a paradigmatic software artifact. Strikingly, I-EPOS outperforms related algorithms that involve non-local brute-force operations or exchange full information. This article contributes new experimental findings about the influence of network topology and planning on learning efficiency as well as findings on techno-socio-economic tradeoffs and global optimality. Experimental evaluation with real-world data from energy and bike sharing pilots demonstrates the grand potential of collective learning to design ethically and socially responsible participatory sharing economies.\n          <\/jats:p>","DOI":"10.1145\/3277668","type":"journal-article","created":{"date-parts":[[2018,11,27]],"date-time":"2018-11-27T13:18:59Z","timestamp":1543324739000},"page":"1-33","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":55,"title":["Decentralized Collective Learning for Self-managed Sharing Economies"],"prefix":"10.1145","volume":"13","author":[{"given":"Evangelos","family":"Pournaras","sequence":"first","affiliation":[{"name":"Professorship of Computational Social Science ETH Zurich, Zurich, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peter","family":"Pilgerstorfer","sequence":"additional","affiliation":[{"name":"Professorship of Computational Social Science ETH Zurich, Zurich, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas","family":"Asikis","sequence":"additional","affiliation":[{"name":"Professorship of Computational Social Science ETH Zurich, Zurich, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2018,11,26]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-25873-2_25"},{"key":"e_1_2_1_2_1","volume-title":"Proceedings of the International Conference on Machine Learning. 699--707","author":"Allen-Zhu Zeyuan","year":"2016"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.5555\/645503.656271"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.5555\/3193939.3193942"},{"key":"e_1_2_1_5_1","unstructured":"Arthur Earl Bryson and Yu-Chi Ho. 1969. 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