Feasible strategies for conflict resolution within intuitionistic fuzzy preference-based conflict situations
- URL: http://arxiv.org/abs/2602.03403v1
- Date: Tue, 03 Feb 2026 11:30:34 GMT
- Title: Feasible strategies for conflict resolution within intuitionistic fuzzy preference-based conflict situations
- Abstract summary: We introduce an intuitionistic fuzzy preference-based conflict situation that captures agents' attitudes towards issue pairs with finer granularity than that afforded by classical preference-based models.<n>We construct three-way conflict analysis models for trisecting the set of agent pairs, the agent set, and the issue set.<n>We present adjustment mechanism-based feasible strategies that simultaneously account for both adjustment magnitudes and conflict degrees.
- Score: 14.046056867216285
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: In three-way conflict analysis, preference-based conflict situations characterize agents' attitudes towards issues by formally modeling their preferences over pairs of issues. However, existing preference-based conflict models rely exclusively on three qualitative relations, namely, preference, converse, and indifference, to describe agents' attitudes towards issue pairs, which significantly limits their capacity in capturing the essence of conflict. To overcome this limitation, we introduce the concept of an intuitionistic fuzzy preference-based conflict situation that captures agents' attitudes towards issue pairs with finer granularity than that afforded by classical preference-based models. Afterwards, we develop intuitionistic fuzzy preference-based conflict measures within this framework, and construct three-way conflict analysis models for trisecting the set of agent pairs, the agent set, and the issue set. Additionally, relative loss functions built on the proposed conflict functions are employed to calculate thresholds for three-way conflict analysis. Finally, we present adjustment mechanism-based feasible strategies that simultaneously account for both adjustment magnitudes and conflict degrees, together with an algorithm for constructing such feasible strategies, and provide an illustrative example to demonstrate the validity and effectiveness of the proposed model.
Related papers
- Large Language Models in Resolving Contextual Knowledge Conflicts [7.7973038442706715]
We introduce a taxonomy of six types of contextual conflicts and contribute a comprehensive dataset ContextConflict.<n>The dataset contains 5,781 samples, covers both reasoning and summarization tasks, and includes both explicit contradictions and implicit conflicts that require multi-step reasoning.<n>We propose a training-free, label-free steering method that steers activations to encourage a more comprehensive incorporation of evidences for better conflict resolution.
arXiv Detail & Related papers (2026-09-02T20:30:15Z) - Conflicts Make Large Reasoning Models Vulnerable to Attacks [17.460309581795595]
This work investigates how LRMs respond to harmful queries when confronted with two categories of conflicts.<n>Internal conflicts pit alignment values against each other and dilemmas impose mutually contradictory choices.<n>Using over 1,300 prompts across five benchmarks, we evaluate three representative LRMs.
arXiv Detail & Related papers (2026-04-10T11:44:57Z) - Diagnosing Knowledge Conflict in Multimodal Long-Chain Reasoning [78.86309644343295]
Multimodal large language models (MLLMs) in long chain-of-thought reasoning often fail when different knowledge sources provide conflicting signals.<n>We formalize these failures under a unified notion of knowledge conflict, distinguishing input-level objective conflict from process-level effective conflict.<n>Our findings provide a mechanism-level view of multimodal reasoning under knowledge conflict and enable principled diagnosis and control of long-CoT failures.
arXiv Detail & Related papers (2026-02-16T07:10:44Z) - Feasible strategies in three-way conflict analysis with three-valued ratings [6.402753182639444]
This paper investigates feasible strategies from two perspectives of consistency and non-consistency.<n>We develop algorithms to identify feasible strategies, $L$-order feasible strategies, and the corresponding optimal ones.
arXiv Detail & Related papers (2025-12-24T20:52:15Z) - Targeting Misalignment: A Conflict-Aware Framework for Reward-Model-based LLM Alignment [5.900494456937422]
Reward-model-based fine-tuning is a central paradigm in aligning Large Language Models with human preferences.<n>This paper investigates a novel framework to identify and mitigate such misalignment by treating the fine-tuning process as a form of knowledge integration.
arXiv Detail & Related papers (2025-12-10T00:52:21Z) - Challenges in Understanding Modality Conflict in Vision-Language Models [6.839552274513621]
This paper highlights the challenge of decomposing conflict detection from conflict resolution in Vision-Language Models (VLMs)<n>We conduct a mechanistic investigation of LLaVA-OV-7B, a state-of-the-art VLM that exhibits diverse resolution behaviors when faced with conflicting multimodal inputs.<n>Our results show that a linearly decodable conflict signal emerges in the model's intermediate layers and that attention patterns associated with conflict detection and resolution diverge at different stages of the network.
arXiv Detail & Related papers (2025-09-02T20:15:49Z) - Conflicts in Texts: Data, Implications and Challenges [58.03478157713084]
Conflicts could reflect the complexity of situations, changes that need to be explained and dealt with, difficulties in data annotation, and mistakes in generated outputs.<n>This survey categorizes these conflicts into three key areas: (1) natural texts on the web, where factual inconsistencies, subjective biases, and multiple perspectives introduce contradictions; (2) human-annotated data, where annotator disagreements, mistakes, and societal biases impact model training; and (3) model interactions, where hallucinations and knowledge conflicts emerge during deployment.<n>We highlight key challenges and future directions for developing conflict-aware NLP systems that can reason over and reconcile conflicting information more effectively
arXiv Detail & Related papers (2025-04-28T04:24:01Z) - Insight Over Sight: Exploring the Vision-Knowledge Conflicts in Multimodal LLMs [55.74117540987519]
This paper explores the problem of commonsense level vision-knowledge conflict in Multimodal Large Language Models (MLLMs)<n>We introduce an automated framework, augmented with human-in-the-loop quality control, to generate inputs designed to simulate and evaluate these conflicts in MLLMs.<n>Using this framework, we have crafted a diagnostic benchmark consisting of 374 original images and 1,122 high-quality question-answer pairs.
arXiv Detail & Related papers (2024-10-10T17:31:17Z) - ECon: On the Detection and Resolution of Evidence Conflicts [56.89209046429291]
The rise of large language models (LLMs) has significantly influenced the quality of information in decision-making systems.
This study introduces a method for generating diverse, validated evidence conflicts to simulate real-world misinformation scenarios.
arXiv Detail & Related papers (2024-10-05T07:41:17Z) - AdaCAD: Adaptively Decoding to Balance Conflicts between Contextual and Parametric Knowledge [57.66282463340297]
Knowledge conflict arises from discrepancies between information in the context of a large language model and the knowledge stored in its parameters.<n>We propose a fine-grained, instance-level approach called AdaCAD, which dynamically infers the weight of adjustment based on the degree of conflict.<n>We show that ADACAD consistently outperforms other decoding baselines with average QA accuracy gains of 14.21% (absolute) over a static contrastive baseline, and improves the factuality of summaries by 6.19 (AlignScore)
arXiv Detail & Related papers (2024-09-11T16:35:18Z) - Structural Interventions and the Dynamics of Inequality [0.0]
We show that technical solutions must be paired with external, context-aware interventions to enact social change.
This research highlights the ways that structural inequality can be perpetuated by seemingly unbiased decision mechanisms.
arXiv Detail & Related papers (2024-06-03T13:44:38Z) - On the Complexity of Adversarial Decision Making [101.14158787665252]
We show that the Decision-Estimation Coefficient is necessary and sufficient to obtain low regret for adversarial decision making.
We provide new structural results that connect the Decision-Estimation Coefficient to variants of other well-known complexity measures.
arXiv Detail & Related papers (2022-06-27T06:20:37Z) - End-to-End Learning and Intervention in Games [60.41921763076017]
We provide a unified framework for learning and intervention in games.
We propose two approaches, respectively based on explicit and implicit differentiation.
The analytical results are validated using several real-world problems.
arXiv Detail & Related papers (2020-10-26T18:39:32Z) - Combining Experts' Causal Judgments [15.169168801732505]
We consider how experts' causal judgments can be combined in order to determine the most effective intervention.
We define a notion of two causal models being emphcompatible, and show how compatible causal models can be merged.
We illustrate our approach on a number of real-life examples.
arXiv Detail & Related papers (2020-05-20T16:41:07Z)
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.