Lexicographic Preferences Similarity for Coalition Formation in Complex Markets: Introducing PLPSim, HRECS, ContractLex, PriceLex, F@LeX, and PLPGen

Lexicographic Preference Trees (LP-Trees) offer a compact and expressive framework for modeling complex decision-making scenarios. However, efficiently 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—HRECS1, HRECS2, and HRECS3—that leverage PLPSim to group agents with similar preferences. We further propose ContractLex and PriceLex protocols (comprising five lexicographic protocols CLF, CFB, CFW, CFA, CFP), along with a new evaluation metric, F@LeX, 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 consumer PLP-Trees are matched with supplier tariffs to optimize coalition outcomes. Experimental results, evaluated using Normalized Discounted Cumulative Gain (nDCG), Davies–Bouldin dispersion, and F@LeX, show that PLPSim-based coalitions outperform baseline approaches. Notably, the combination HRECS3 + CFP yields the highest consumer satisfaction, while HRECS3 + CFB achieves balanced satisfaction for both consumers and suppliers. Although electricity tariffs and renewable energy contracts—both static and dynamic—serve as the motivating example, the proposed framework generalizes to broader multiagent systems, offering a foundation for preference-driven coalition formation, adaptive policy design, and sustainable market optimization.