The Tournament Tree Method for preference elicitation in Multi-criteria decision-making

• URL: http://arxiv.org/abs/2510.08197v1
• Date: Thu, 09 Oct 2025 13:24:32 GMT
• Title: The Tournament Tree Method for preference elicitation in Multi-criteria decision-making
• Abstract summary: Tournament Tree Method (TTM) is a novel elicitation and evaluation framework.<n>The proposed approach ensures consistency by design, minimizes cognitive effort, and reduces the dimensionality of preference modeling.<n>It is compatible with the classical Deck of Cards method, and thus it can handle interval and ratio scales.
• Score: 8.06855767139412
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Pairwise comparison methods, such as Fuzzy Preference Relations and Saaty's Multiplicative Preference Relations, are widely used to model expert judgments in multi-criteria decision-making. However, their application is limited by the high cognitive load required to complete $m(m-1)/2$ comparisons, the risk of inconsistency, and the computational complexity of deriving consistent value scales. This paper proposes the Tournament Tree Method (TTM), a novel elicitation and evaluation framework that overcomes these limitations. The TTM requires only $m-1$ pairwise comparisons to obtain a complete, reciprocal, and consistent comparison matrix. The method consists of three phases: (i) elicitation of expert judgments using a reduced set of targeted comparisons, (ii) construction of the consistent pairwise comparison matrix, and (iii) derivation of a global value scale from the resulting matrix. The proposed approach ensures consistency by design, minimizes cognitive effort, and reduces the dimensionality of preference modeling from $m(m-1)/2$ to $m$ parameters. Furthermore, it is compatible with the classical Deck of Cards method, and thus it can handle interval and ratio scales. We have also developed a web-based tool that demonstrates its practical applicability in real decision-making scenarios.

Related papers

• Multiple-Prediction-Powered Inference [57.30769275944024]
  This work provides theoretical guarantees about the minimax optimality, finite-sample performance, and normality of the MultiPPI estimator.<n>We show that MultiPPI consistently achieves lower estimation error than existing baselines.
  arXiv  Detail & Related papers  (2026-03-28T21:24:00Z)
• Principled Algorithms for Optimizing Generalized Metrics in Binary Classification [53.604375124674796]
  We introduce principled algorithms for optimizing generalized metrics, supported by $H$-consistency and finite-sample generalization bounds.<n>Our approach reformulates metric optimization as a generalized cost-sensitive learning problem.<n>We develop new algorithms, METRO, with strong theoretical performance guarantees.
  arXiv  Detail & Related papers  (2025-12-29T01:33:42Z)
• CoLSE: A Lightweight and Robust Hybrid Learned Model for Single-Table Cardinality Estimation using Joint CDF [7.945011337356916]
  Cardinality estimation is a critical component of query optimization.<n>We propose CoLSE, a hybrid learned approach for single-table cardinality estimation.<n> Experimental results show that CoLSE achieves a favorable trade-off among accuracy, training time, latency, and model size, outperforming existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-12-14T10:08:20Z)
• LLM Routing with Dueling Feedback [49.67815163970033]
  We study the problem of selecting the best model for each query while balancing user satisfaction, model expertise, and inference cost.<n>We formulate routing as contextual dueling bandits, learning from pairwise preference feedback rather than absolute scores.<n>We introduce Category-Calibrated Fine-Tuning (CCFT), a representation-learning method that derives model embeddings from offline data using contrastive fine-tuning with categorical weighting.
  arXiv  Detail & Related papers  (2025-10-01T12:52:25Z)
• Access Paths for Efficient Ordering with Large Language Models [7.826046892571884]
  We present the LLM ORDER BY operator as a logical abstraction and study its physical implementations within a unified evaluation framework.<n>We introduce three new designs: an agreement-based batch-size policy, a majority voting mechanism for pairwise sorting, and a two-way external merge sort adapted for LLMs.
  arXiv  Detail & Related papers  (2025-08-30T01:44:36Z)
• On Monotonicity in AI Alignment [10.244128221542228]
  This paper investigates the root causes of (non) monotonicity, for a general comparison-based preference learning framework.<n>Under mild assumptions, we prove that such methods still satisfy what we call local pairwise monotonicity.<n>We also provide a bouquet of formalizations of monotonicity, and identify sufficient conditions for their guarantee, thereby providing a toolbox to evaluate how prone learning models are to monotonicity violations.
  arXiv  Detail & Related papers  (2025-06-10T17:17:48Z)
• Achieving $\widetilde{\mathcal{O}}(\sqrt{T})$ Regret in Average-Reward POMDPs with Known Observation Models [69.1820058966619]
  We tackle average-reward infinite-horizon POMDPs with an unknown transition model.<n>We present a novel and simple estimator that overcomes this barrier.
  arXiv  Detail & Related papers  (2025-01-30T22:29:41Z)
• Regret Minimization and Statistical Inference in Online Decision Making with High-dimensional Covariates [7.21848268647674]
  We integrate the $varepsilon$-greedy bandit algorithm for decision-making with a hard thresholding algorithm for estimating sparse bandit parameters.<n>Under a margin condition, our method achieves either $O(T1/2)$ regret or classical $O(T1/2)$-consistent inference.
  arXiv  Detail & Related papers  (2024-11-10T01:47:11Z)
• LLaMA-Berry: Pairwise Optimization for O1-like Olympiad-Level Mathematical Reasoning [56.273799410256075]
  The framework combines Monte Carlo Tree Search (MCTS) with iterative Self-Refine to optimize the reasoning path. The framework has been tested on general and advanced benchmarks, showing superior performance in terms of search efficiency and problem-solving capability.
  arXiv  Detail & Related papers  (2024-10-03T18:12:29Z)
• An incremental preference elicitation-based approach to learning potentially non-monotonic preferences in multi-criteria sorting [53.36437745983783]
  We first construct a max-margin optimization-based model to model potentially non-monotonic preferences. We devise information amount measurement methods and question selection strategies to pinpoint the most informative alternative in each iteration. Two incremental preference elicitation-based algorithms are developed to learn potentially non-monotonic preferences.
  arXiv  Detail & Related papers  (2024-09-04T14:36:20Z)
• MAP: Low-compute Model Merging with Amortized Pareto Fronts via Quadratic Approximation [80.47072100963017]
  We introduce a novel and low-compute algorithm, Model Merging with Amortized Pareto Front (MAP)<n>MAP efficiently identifies a set of scaling coefficients for merging multiple models, reflecting the trade-offs involved.<n>We also introduce Bayesian MAP for scenarios with a relatively low number of tasks and Nested MAP for situations with a high number of tasks, further reducing the computational cost of evaluation.
  arXiv  Detail & Related papers  (2024-06-11T17:55:25Z)
• Variance-Aware Regret Bounds for Stochastic Contextual Dueling Bandits [53.281230333364505]
  This paper studies the problem of contextual dueling bandits, where the binary comparison of dueling arms is generated from a generalized linear model (GLM) We propose a new SupLinUCB-type algorithm that enjoys computational efficiency and a variance-aware regret bound $tilde Obig(dsqrtsum_t=1Tsigma_t2 + dbig)$. Our regret bound naturally aligns with the intuitive expectation in scenarios where the comparison is deterministic, the algorithm only suffers from an $tilde O(d)$ regret.
  arXiv  Detail & Related papers  (2023-10-02T08:15:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.