{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T19:10:36Z","timestamp":1767985836365,"version":"3.49.0"},"reference-count":36,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T00:00:00Z","timestamp":1767916800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Lexicographic preference trees (LP-Trees) provide a compact and expressive representation for modeling complex decision-making scenarios, yet measuring similarity between complete or partial structures remains a challenge. This study introduces PLPSim, a novel metric for quantifying alignment between partial lexicographic preference trees (PLP-Trees) and develops three coalition formation algorithms\u2014HRECS1, HRECS2, and HRECS3\u2014that leverage PLPSim to group agents with similar preferences. We further propose ContractLex and PriceLex protocols (comprising CLF, CFB, CFW, CFA, CFP) for coalition-based contract and pricing strategies, along with a new evaluation metric, F@Lex, which is designed to assess satisfaction under lexicographic preferences. To illustrate the framework, we generate a synthetic dataset (PLPGen) contextualized in a hybrid renewable energy market, where consumers\u2019 PLP-Trees are aggregated and matched with suppliers\u2019 tariff contracts. Experiments across 162 market scenarios, evaluated using Normalized Discounted Cumulative Gain (nDCG), Davies\u2013Bouldin dispersion, and F@Lex, demonstrate that PLPSim-based coalitions outperform baseline approaches. The combination HRECS3 + CFP yields the highest consumer satisfaction, while HRECS3 + CFB achieves balanced satisfaction for both consumers and suppliers. While electricity tariffs and renewable energy contracts\u2014static and dynamic\u2014serve as the motivating example, the proposed framework generalizes to diverse multi-agent systems, offering a foundation for preference-driven coalition formation, adaptive policy design, and sustainable market optimization.<\/jats:p>","DOI":"10.3390\/info17010062","type":"journal-article","created":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T14:15:50Z","timestamp":1767968150000},"page":"62","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Lexicographic Preferences Similarity for Coalition Formation in Complex Markets: Introducing PLPSim, HRECS, ContractLex, PriceLex, F@Lex, and PLPGen"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5147-9136","authenticated-orcid":false,"given":"Faria","family":"Nassiri-Mofakham","sequence":"first","affiliation":[{"name":"Faculty of Computer Engineering, University of Isfahan, Isfahan 81746-73441, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shadi","family":"Farid","sequence":"additional","affiliation":[{"name":"Faculty of Computer Engineering, University of Isfahan, Isfahan 81746-73441, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7867-4281","authenticated-orcid":false,"given":"Katsuhide","family":"Fujita","sequence":"additional","affiliation":[{"name":"Institute of Global Innovation Research, Tokyo University of Agriculture and Technology, Tokyo 184-8588, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,9]]},"reference":[{"key":"ref_1","unstructured":"Booth, R., Chevaleyre, Y., Lang, J., Mengin, J., and Sombattheera, C. 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