Balancing Optimality and Diversity: Human-Centered Decision Making through Generative Curation

[2409.11535] Balancing Optimality and Diversity: Human-Centered Decision Making through Generative Curation Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Machine Learning arXiv:2409.11535 (cs) [Submitted on 17 Sep 2024 ( v1 ), last revised 14 Aug 2026 (this version, v3)] Title: Balancing Optimality and Diversity: Human-Centered Decision Making through Generative Curation Authors: Michael Lingzhi Li , Shixiang Zhu View a PDF of the paper titled Balancing Optimality and Diversity: Human-Centered Decision Making through Generative Curation, by Michael Lingzhi Li and Shixiang Zhu View PDF HTML (experimental) Abstract: Many decision-support systems recommend actions by optimizing measurable objectives, even when a human decision-maker retains final authority and considers additional criteria that are difficult to specify in advance. We study how an algorithm should curate a small portfolio of quantitatively strong alternatives in such settings. We introduce generative curation, a framework that learns a recommendation policy to maximize the expected desirability of the action ultimately selected by the decision-maker. For policies that generate quantitatively competitive actions, we decompose expected portfolio desirability into quantitative performance and a qualitative curation gain. Under a Gaussian process model of residual desirability, this gain is characterized by the Gaussian width induced by the covariance kernel, yielding a decision-theoretic notion of diversity based on qualitative nonredundancy rather than generic geometric separation. We establish diminishing returns to portfolio size and characterize regimes in which optimal policies are balanced, endpoint-concentrated, or space-filling. We develop neural generative and sequential optimization approaches applicable to continuous and combinatorial action spaces. Controlled synthetic experiments demonstrate substantial regret reductions relative to optimization- and distance-based benchmarks, while an Atlanta police redistricting case study illustrates the framework's applicability to a complex operational planning problem. Subjects: Machine Learning (cs.LG) ; Human-Computer Interaction (cs.HC); Optimization and Control (math.OC) Cite as: arXiv:2409.11535 [cs.LG] (or arXiv:2409.11535v3 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2409.11535 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Shixiang Zhu [ view email ] [v1] Tue, 17 Sep 2024 20:13:32 UTC (6,155 KB) [v2] Tue, 5 Aug 2025 02:50:33 UTC (3,353 KB) [v3] Fri, 14 Aug 2026 20:32:44 UTC (3,413 KB) Full-text links: Access Paper: View a PDF of the paper titled Balancing Optimality and Diversity: Human-Centered Decision Making through Generative Curation, by Michael Lingzhi Li and Shixiang Zhu View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2024-09 Change to browse by: cs cs.HC math math.OC References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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