{
  "abstract": "Interactive decision support systems often require repeated preference judgments, although identifying the best alternative may not justify eliciting all \\(n(n-1)/2\\) pairwise comparisons. We propose ALEC (Adaptive Lexicographic Elicitation of Comparisons), which re-ranks the alternatives after each judgment and asks the first unobserved pair in the lexicographic order of the current ranking, using the Analytic Hierarchy Process as the concrete setting. Under a log-normal error model, directly comparing a contrast maximises the one-step reduction in its effective resistance. Posterior bounds on the probability of a wrong choice or of exceeding a relative-loss tolerance provide stopping rules valid under adaptive questioning, while a consistency-and-margin condition gives a deterministic certificate of agreement with the full-matrix winner. Simulations show that re-ranking is most useful in larger problems with limited budgets; ALEC outperforms Harker's original rule in most scenarios with \\(n\\ge11\\), while under a common stopping rule it achieves higher hit-rate point estimates than the stronger Harker-log variant at a modest additional questioning cost. With a 5\\% loss tolerance and a cap of \\(2n\\) additional comparisons, the stopping rule saves \\res{prot-s06-saveadd-min}--\\res{prot-s06-saveadd-max}\\% of them, while among runs stopped by the bound the observed rate of losses above tolerance remains at most \\res{s06-errearly-max}\\%, against a nominal 10\\%. ALEC therefore provides an adaptive elicitation module for decision support systems that balances decision precision against user effort.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.08123",
  "author_total": 2,
  "authors": [
    {
      "name": "Luis Ángel Calvo"
    },
    {
      "name": "Jiri Mazurek"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/avtdn6ahz4ptn.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.08123",
  "fulltext_url": null,
  "id": "avtdn6ahz4ptn",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.08123",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "math.OC"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.08123",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-05",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/avtdn6ahz4ptn.ris",
  "sources": [
    {
      "first_seen": "2026-11-06T18:21:34Z",
      "native_id": "2611.08123",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.08123"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.OC",
      "subfield": "optimisation-control"
    }
  ],
  "title": "Adaptive pairwise preference elicitation for decision support: identifying the best alternative with fewer judgments",
  "title_text": "Adaptive pairwise preference elicitation for decision support: identifying the best alternative with fewer judgments",
  "type": "preprint",
  "updated_at": "2026-11-09T00:20:48Z",
  "url": "https://research.draconitic.com/paper/avtdn6ahz4ptn",
  "venue": "arXiv",
  "versions": []
}