Pub Date : 2025-12-26DOI: 10.1016/j.ijar.2025.109624
Jana Borzová, Miriam Kleinová, Lukáš Medvec
In order to overcome some limitations of the classical Hirsch index, Boczek et al. (2021) introduced the upper and lower -Sugeno integrals, extending in particular the approach of Mesiar and Gagolewski (2016). In this paper, we concentrate on the upper -Sugeno integral, which plays a central role in the definition of the Hirsch-Sugeno operator, a construction with significant potential in scientometrics. We investigate its theoretical properties and show, building on the results of Chitescu (2022), that although the upper -Sugeno integral constitutes a genuine generalization of the classical Sugeno integral, in some cases the extended construction collapses back to its original form. Moreover, we demonstrate that the computation of the upper -Sugeno integral can be reformulated as the problem of finding a midpoint of a level measure. This interpretation also connects it to the solution of certain nonlinear equations, including those arising in informetrics.
{"title":"Exploring the upper n-Sugeno integral: Theory and applications to scientometric index design","authors":"Jana Borzová, Miriam Kleinová, Lukáš Medvec","doi":"10.1016/j.ijar.2025.109624","DOIUrl":"10.1016/j.ijar.2025.109624","url":null,"abstract":"<div><div>In order to overcome some limitations of the classical Hirsch index, Boczek et al. (2021) introduced the upper and lower <span><math><mstyle><mi>n</mi></mstyle></math></span>-Sugeno integrals, extending in particular the approach of Mesiar and Gagolewski (2016). In this paper, we concentrate on the upper <span><math><mstyle><mi>n</mi></mstyle></math></span>-Sugeno integral, which plays a central role in the definition of the Hirsch-Sugeno operator, a construction with significant potential in scientometrics. We investigate its theoretical properties and show, building on the results of Chitescu (2022), that although the upper <span><math><mstyle><mi>n</mi></mstyle></math></span>-Sugeno integral constitutes a genuine generalization of the classical Sugeno integral, in some cases the extended construction collapses back to its original form. Moreover, we demonstrate that the computation of the upper <span><math><mstyle><mi>n</mi></mstyle></math></span>-Sugeno integral can be reformulated as the problem of finding a midpoint of a level measure. This interpretation also connects it to the solution of certain nonlinear equations, including those arising in informetrics.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"191 ","pages":"Article 109624"},"PeriodicalIF":3.0,"publicationDate":"2025-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145904230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-25DOI: 10.1016/j.ijar.2025.109622
Yasmin F. Cavaliere, Luís G. Esteves, Victor Fossaluza
Testing hypotheses is fundamental to any scientific investigation or data-driven decision-making process. Since Neyman and Pearson systematized hypothesis testing, this statistical procedure has significantly contributed to the development of competing theories of statistical inference. Common approaches to hypothesis testing include significance tests, most powerful tests, likelihood ratio tests, and Bayesian tests. However, practitioners often use evidence measures, such as p-values, the Pereira-Stern e-value and likelihood ratio statistics, in lieu of the reject-or-fail-to-reject approach proposed by Neyman and Pearson, as they provide a more nuanced understanding of statistical hypotheses from data. This study proposes an axiomatic development of belief relations representing the extent to which sample data support a statistical hypothesis, which is consistent with a few logical requirements that capture, in a sense, the Onus Probandi principle in law. It also examines whether the above-mentioned evidence measures are reasonable mathematical representations of such belief relations, that is, of how much a sample supports a hypothesis. It shows that for discrete parameter and sample spaces, the measure of evidence by Pereira and Stern is a fair representation of such a belief relation, especially for Bayesian decision-makers as it formally considers the uncertainty one has about the unknown parameter at the same time it induces a relation that coincides with the belief relation meeting the axioms. This result renders the Pereira-Stern e-value a genuine measure of support for statistical hypotheses in the discrete case, in addition to its recognized importance in the continuous case.
{"title":"An axiomatic development of Pereira-Stern e-value as a measure of support for statistical hypotheses","authors":"Yasmin F. Cavaliere, Luís G. Esteves, Victor Fossaluza","doi":"10.1016/j.ijar.2025.109622","DOIUrl":"10.1016/j.ijar.2025.109622","url":null,"abstract":"<div><div>Testing hypotheses is fundamental to any scientific investigation or data-driven decision-making process. Since Neyman and Pearson systematized hypothesis testing, this statistical procedure has significantly contributed to the development of competing theories of statistical inference. Common approaches to hypothesis testing include significance tests, most powerful tests, likelihood ratio tests, and Bayesian tests. However, practitioners often use evidence measures, such as p-values, the Pereira-Stern e-value and likelihood ratio statistics, in lieu of the reject-or-fail-to-reject approach proposed by Neyman and Pearson, as they provide a more nuanced understanding of statistical hypotheses from data. This study proposes an axiomatic development of belief relations representing the extent to which sample data support a statistical hypothesis, which is consistent with a few logical requirements that capture, in a sense, the Onus Probandi principle in law. It also examines whether the above-mentioned evidence measures are reasonable mathematical representations of such belief relations, that is, of how much a sample supports a hypothesis. It shows that for discrete parameter and sample spaces, the measure of evidence by Pereira and Stern is a fair representation of such a belief relation, especially for Bayesian decision-makers as it formally considers the uncertainty one has about the unknown parameter at the same time it induces a relation that coincides with the belief relation meeting the axioms. This result renders the Pereira-Stern e-value a genuine measure of support for statistical hypotheses in the discrete case, in addition to its recognized importance in the continuous case.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"190 ","pages":"Article 109622"},"PeriodicalIF":3.0,"publicationDate":"2025-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145880983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-22DOI: 10.1016/j.ijar.2025.109620
Yuedong Zheng , Bao Qing Hu , Guitian He
As a pivotal theoretical branch of three-way decision (3WD), the concept of Three-way Decision Space (3WDS) effectively unifies 3WD models within fuzzy lattices and other partially ordered sets, providing researchers with a comprehensive information system for 3WD. While various types of 3WDSs have been extensively studied, decision-makers often benefit from a wider array of options to achieve better outcomes. To address this need and enrich the decision-making toolkit, this paper introduces novel construction of decision evaluation functions (DEFs). At first, based on the core axiomatic definitions in 3WDSs- DEF -this paper introduces automorphisms on bounded posets to construct such functions, deriving numerous novel DEFs. The second, this paper proposes new transformation methods from semi-decision evaluation functions (S-DEFs) (resp., quasi-decision evaluation functions (Q-DEFs)) to DEFs, extending the methodological toolkit. And then, this paper investigates the interplay between automorphisms and involution negations on bounded posets, with a focused analysis of their properties in the context of truth value set .
{"title":"New construction of decision evaluation functions on three-way decision spaces based on automorphisms of hesitant fuzzy truth values","authors":"Yuedong Zheng , Bao Qing Hu , Guitian He","doi":"10.1016/j.ijar.2025.109620","DOIUrl":"10.1016/j.ijar.2025.109620","url":null,"abstract":"<div><div>As a pivotal theoretical branch of three-way decision (3WD), the concept of Three-way Decision Space (3WDS) effectively unifies 3WD models within fuzzy lattices and other partially ordered sets, providing researchers with a comprehensive information system for 3WD. While various types of 3WDSs have been extensively studied, decision-makers often benefit from a wider array of options to achieve better outcomes. To address this need and enrich the decision-making toolkit, this paper introduces novel construction of decision evaluation functions (DEFs). At first, based on the core axiomatic definitions in 3WDSs- DEF -this paper introduces automorphisms on bounded posets to construct such functions, deriving numerous novel DEFs. The second, this paper proposes new transformation methods from semi-decision evaluation functions (S-DEFs) (resp., quasi-decision evaluation functions (Q-DEFs)) to DEFs, extending the methodological toolkit. And then, this paper investigates the interplay between automorphisms and involution negations on bounded posets, with a focused analysis of their properties in the context of truth value set <span><math><mrow><msup><mn>2</mn><mrow><mo>[</mo><mn>0</mn><mo>,</mo><mn>1</mn><mo>]</mo></mrow></msup><mo>−</mo><mrow><mo>{</mo><mi>⌀</mi><mo>}</mo></mrow></mrow></math></span>.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"190 ","pages":"Article 109620"},"PeriodicalIF":3.0,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145880982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-22DOI: 10.1016/j.ijar.2025.109621
Guangming Lang , Haojun Liu , Mengjun Hu
In three-way conflict analysis, a key challenge lies in faithfully capturing agents’ attitudes towards multiple issues within complex conflict situations. Preference-based conflict situations, which characterize comparisons through ordered issue pairs, offer a structured alternative to traditional rating scales by emphasizing relational judgments. However, existing single-level preference frameworks are limited in their ability to capture variations in preference strengths, confidence levels across agents, and refinements that emerge over time. Moreover, they do not reliably support cross-agent comparison of preference relations. Consequently, single-level models exhibit inherent constraints when representing diverse agent viewpoints across different issue pairs. To overcome these limitations, this paper introduces a multi-level preference framework that generalizes single-level preference, converse, and indifference relations by incorporating multiple levels of relational intensity, thereby enabling a more fine-grained characterization of agents’ preference strengths over issue pairs. Within this framework, we define conflict measures for individual issue pairs between two agents, and further extend them to a set of issues, facilitating the exact quantification of conflict degrees between two agents across multiple issues, and enabling a more accurate trisection of agent pairs into alliance, neutrality, and conflict relations. As a concrete instantiation, we develop a two-level preference-based model distinguishing strong and weak relations, and apply it to a case study on development planning in Gansu Province. A comparative analysis demonstrates that the multi-level preference framework not only captures conflicts with greater expressiveness and accuracy than single-level approaches but also yields richer and more actionable insights for conflict resolution, thereby enhancing both the interpretability and the practical value of three-way conflict analysis.
{"title":"Three-way conflict analysis: From single-level to multi-level preferences","authors":"Guangming Lang , Haojun Liu , Mengjun Hu","doi":"10.1016/j.ijar.2025.109621","DOIUrl":"10.1016/j.ijar.2025.109621","url":null,"abstract":"<div><div>In three-way conflict analysis, a key challenge lies in faithfully capturing agents’ attitudes towards multiple issues within complex conflict situations. Preference-based conflict situations, which characterize comparisons through ordered issue pairs, offer a structured alternative to traditional rating scales by emphasizing relational judgments. However, existing single-level preference frameworks are limited in their ability to capture variations in preference strengths, confidence levels across agents, and refinements that emerge over time. Moreover, they do not reliably support cross-agent comparison of preference relations. Consequently, single-level models exhibit inherent constraints when representing diverse agent viewpoints across different issue pairs. To overcome these limitations, this paper introduces a multi-level preference framework that generalizes single-level preference, converse, and indifference relations by incorporating multiple levels of relational intensity, thereby enabling a more fine-grained characterization of agents’ preference strengths over issue pairs. Within this framework, we define conflict measures for individual issue pairs between two agents, and further extend them to a set of issues, facilitating the exact quantification of conflict degrees between two agents across multiple issues, and enabling a more accurate trisection of agent pairs into alliance, neutrality, and conflict relations. As a concrete instantiation, we develop a two-level preference-based model distinguishing strong and weak relations, and apply it to a case study on development planning in Gansu Province. A comparative analysis demonstrates that the multi-level preference framework not only captures conflicts with greater expressiveness and accuracy than single-level approaches but also yields richer and more actionable insights for conflict resolution, thereby enhancing both the interpretability and the practical value of three-way conflict analysis.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"190 ","pages":"Article 109621"},"PeriodicalIF":3.0,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145880984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-20DOI: 10.1016/j.ijar.2025.109618
A.C. Murph , J.P. Williams , J. Hannig
We introduce a novel approach to inference on parameters that take values in a Riemannian manifold embedded in a Euclidean space. Parameter spaces of this form are ubiquitous across many fields, including chemistry, physics, computer graphics, and geology. This new approach uses generalized fiducial inference (GFI) to obtain a posterior-like distribution on the manifold, without needing to know local parameterizations that map to the constrained space from an unconstrained Euclidean space. Using mathematical tools from Riemannian geometry, we construct a constrained generalized fiducial distribution (CGFD). A Bernstein-von Mises-type result for the CGFD, which provides intuition for how the desirable asymptotic qualities of the unconstrained generalized fiducial distribution are inherited by the CGFD, is provided. To illustrate the practical use of the CGFD, we provide a proof-of-concept example in the context of a linear logspline density estimation problem, and demonstrate that CGFD-based confidence sets exhibit desirable coverage properties via simulation. As an application, we fit a CGFD to COVID-19 case count data from North Carolina, USA.
{"title":"Generalized fiducial inference on differentiable manifolds","authors":"A.C. Murph , J.P. Williams , J. Hannig","doi":"10.1016/j.ijar.2025.109618","DOIUrl":"10.1016/j.ijar.2025.109618","url":null,"abstract":"<div><div>We introduce a novel approach to inference on parameters that take values in a Riemannian manifold embedded in a Euclidean space. Parameter spaces of this form are ubiquitous across many fields, including chemistry, physics, computer graphics, and geology. This new approach uses generalized fiducial inference (GFI) to obtain a posterior-like distribution on the manifold, without needing to know local parameterizations that map to the constrained space from an unconstrained Euclidean space. Using mathematical tools from Riemannian geometry, we construct a <em>constrained generalized fiducial distribution</em> (CGFD). A Bernstein-von Mises-type result for the CGFD, which provides intuition for how the desirable asymptotic qualities of the unconstrained generalized fiducial distribution are inherited by the CGFD, is provided. To illustrate the practical use of the CGFD, we provide a proof-of-concept example in the context of a linear logspline density estimation problem, and demonstrate that CGFD-based confidence sets exhibit desirable coverage properties via simulation. As an application, we fit a CGFD to COVID-19 case count data from North Carolina, USA.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"190 ","pages":"Article 109618"},"PeriodicalIF":3.0,"publicationDate":"2025-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145837496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-18DOI: 10.1016/j.ijar.2025.109619
Bo Wang , Xiaoquan Xu
The Nilpotent Minimum logic expanded with the Baaz-Monteiro Δ connective (NMΔ) offers a rich framework for reasoning with vagueness, yet a systematic quantitative and topological analysis of it remains largely unexplored. To bridge this gap, this paper develops a novel theoretical framework based on the formula-induced function method. We first define the Δ truth degree of a formula in NMΔ, investigating its fundamental properties and proving its soundness under key inference rules such as MP, HS, as well as union and intersection inferences. We further derive a computational expression for the truth degree of generalized conjunctive formulae. Secondly, building on this, we introduce the concepts of Δ similarity degree and Δ pseudo-distance, establishing their essential properties. Finally. this construction yields the Δ logic metric space . Within this space, we perform a preliminary topological analysis, proving the continuity of the fundamental logical operators (Δ, ∼ , → , ∧, ∨) with respect to the Δ pseudo-distance and proving that this space contains no isolated points. The significance of this work lies in providing the necessary foundational framework and tools for future exploration of convergence, density, and other topological properties in non-classical logic systems.
{"title":"Towards a Δ-based metric framework for NMΔ: Δ truth degree and Δ logic metric space","authors":"Bo Wang , Xiaoquan Xu","doi":"10.1016/j.ijar.2025.109619","DOIUrl":"10.1016/j.ijar.2025.109619","url":null,"abstract":"<div><div>The Nilpotent Minimum logic expanded with the Baaz-Monteiro Δ connective (NM<sub>Δ</sub>) offers a rich framework for reasoning with vagueness, yet a systematic quantitative and topological analysis of it remains largely unexplored. To bridge this gap, this paper develops a novel theoretical framework based on the formula-induced function method. We first define the Δ truth degree of a formula in NM<sub>Δ</sub>, investigating its fundamental properties and proving its soundness under key inference rules such as MP, HS, as well as union and intersection inferences. We further derive a computational expression for the truth degree of generalized conjunctive formulae. Secondly, building on this, we introduce the concepts of Δ similarity degree and Δ pseudo-distance, establishing their essential properties. Finally. this construction yields the Δ logic metric space <span><math><mrow><mo>(</mo><mi>F</mi><mrow><mo>(</mo><mi>S</mi><mo>)</mo></mrow><mo>,</mo><msubsup><mi>ρ</mi><mrow><mi>n</mi></mrow><mstyle><mi>Δ</mi></mstyle></msubsup><mo>)</mo></mrow></math></span>. Within this space, we perform a preliminary topological analysis, proving the continuity of the fundamental logical operators (Δ, ∼ , → , ∧, ∨) with respect to the Δ pseudo-distance <span><math><msubsup><mi>ρ</mi><mrow><mi>n</mi></mrow><mstyle><mi>Δ</mi></mstyle></msubsup></math></span> and proving that this space contains no isolated points. The significance of this work lies in providing the necessary foundational framework and tools for future exploration of convergence, density, and other topological properties in non-classical logic systems.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"190 ","pages":"Article 109619"},"PeriodicalIF":3.0,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145837495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-18DOI: 10.1016/j.ijar.2025.109623
Lin Sun , Changwu Feng , Xiankun Zhang , Jiucheng Xu
Due to the increasing prevalence of multilabel data, multilabel classification must tackle the challenges of high-dimensional feature spaces, complex label dependencies, and sample sparsity, which restricts the effectiveness of multilabel learning. To address these challenges, this work constructs a weighted feature graph-based multilabel feature selection methodology via multi-metrics with global–local correlation. First, fuzzy similarity relations in feature space and fuzzy decision similarity within label space are calculated. A joint fuzzy similarity relation is then constructed to capture the consistency of samples across both spaces. Next, we derive associativity from the joint fuzzy similarity relation, obtain redundancy from the fuzzy similarity relation, and measure interactivity using the fuzzy dependency degree of feature subsets. These three metrics are combined to define the edge weights of the feature graph, thereby describing the complex correlations among features for multilabel classification. Second, to evaluate the global–local correlation, a ridge regression coefficient matrix is presented. Concurrently, the correlation weight of information energy ratio is calculated via mutual information between features and labels, forming a feature-label correlation matrix. We then use a multi-Criteria Decision-Making (MCDM) framework to balance these global and local correlations. This allows us to develop a weighted feature-label matrix and a relative closeness degree for each alternative, which determines the node weights of the weighted feature graph. Third, to dynamically adjust the intensity of information propagation between features, a feature-driven, attention-based weight allocation strategy is studied. We construct a feature node matrix that represents multi-source information fusion and an attention matrix by combining graph structural features with the MCDM results. These are used to form a feature-aware hybrid adjacency matrix. An improved PageRank scheme then iteratively updates the feature ranking scores on this hybrid structure, to select a discriminative and representative feature subset. Experiments illustrate that our methodology outperforms other comparative approaches across several metrics on high-dimensional multilabel data.
{"title":"Weighted feature graph-based multilabel feature selection via multi-metrics with global–local correlation","authors":"Lin Sun , Changwu Feng , Xiankun Zhang , Jiucheng Xu","doi":"10.1016/j.ijar.2025.109623","DOIUrl":"10.1016/j.ijar.2025.109623","url":null,"abstract":"<div><div>Due to the increasing prevalence of multilabel data, multilabel classification must tackle the challenges of high-dimensional feature spaces, complex label dependencies, and sample sparsity, which restricts the effectiveness of multilabel learning. To address these challenges, this work constructs a weighted feature graph-based multilabel feature selection methodology via multi-metrics with global–local correlation. First, fuzzy similarity relations in feature space and fuzzy decision similarity within label space are calculated. A joint fuzzy similarity relation is then constructed to capture the consistency of samples across both spaces. Next, we derive associativity from the joint fuzzy similarity relation, obtain redundancy from the fuzzy similarity relation, and measure interactivity using the fuzzy dependency degree of feature subsets. These three metrics are combined to define the edge weights of the feature graph, thereby describing the complex correlations among features for multilabel classification. Second, to evaluate the global–local correlation, a ridge regression coefficient matrix is presented. Concurrently, the correlation weight of information energy ratio is calculated via mutual information between features and labels, forming a feature-label correlation matrix. We then use a multi-Criteria Decision-Making (MCDM) framework to balance these global and local correlations. This allows us to develop a weighted feature-label matrix and a relative closeness degree for each alternative, which determines the node weights of the weighted feature graph. Third, to dynamically adjust the intensity of information propagation between features, a feature-driven, attention-based weight allocation strategy is studied. We construct a feature node matrix that represents multi-source information fusion and an attention matrix by combining graph structural features with the MCDM results. These are used to form a feature-aware hybrid adjacency matrix. An improved PageRank scheme then iteratively updates the feature ranking scores on this hybrid structure, to select a discriminative and representative feature subset. Experiments illustrate that our methodology outperforms other comparative approaches across several metrics on high-dimensional multilabel data.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"190 ","pages":"Article 109623"},"PeriodicalIF":3.0,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145837549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-08DOI: 10.1016/j.ijar.2025.109610
Hanzhong Hou , Chao Zhang , Deyu Li , Wentao Li
The continuous trend development of information intelligence has made social network group decision-making (SN-GDM) increasingly important. However, in the context of SN-GDM, there are three key challenges: how to properly handle the decline in the trust propagation efficiency; the single granularity consensus adjustment strategy may not fully consider the impact of groups with high consensus on costs and the different cooperation intention of subgroups; traditional methods often face challenges in determining high-performance classification thresholds. In response to the above issues, the solution involves reconstructing trust relationships and optimizing clustering via the Leiden algorithm (LA) and structural holes (SHs). Moreover, multi-granularity consensus adjustment is implemented using game theory while classification thresholds are refined with game-theoretic rough sets (GTRSs). More specifically, firstly, the discount rate of trust propagation intermediaries and path reliability are considered to conduct indirect trust propagation and multipath fusion, and the cooperation index (CI) is obtained based on trust relationships and similarities. Secondly, LA is used to cluster DMs, with the CI as the edge weight, to ensure that the community reflects social relationships and opinion consensus. The weight of decision-makers (DMs) and subgroups are objectively determined by comprehensively integrating SHs, CI, similarity and in-degree centrality. Thirdly, a multi-granularity consensus adjustment method involving game theory is proposed. This method considers three adjustment scenarios: joint adjustment, cooperative game, and non-cooperative game, to obtain the optimal adjustment strategy while ensuring the individual benefit of participants. Then, the generality and accuracy of classification thresholds are improved via the application of GTRSs. Finally, a case study is conducted on the evaluation of scenic spots via a questionnaire survey, verifying the feasibility and effectiveness of the proposed method.
随着信息智能化趋势的不断发展，社会网络群体决策（social network group decision, SN-GDM）变得越来越重要。然而，在SN-GDM的背景下，有三个关键的挑战：如何妥善处理信任传播效率的下降；单粒度共识调整策略可能没有充分考虑高共识群体对成本的影响以及子群体不同的合作意向；传统方法在确定高性能分类阈值方面经常面临挑战。针对上述问题，通过Leiden算法（LA）和结构洞（SHs）进行信任关系重构和聚类优化。利用博弈论实现多粒度共识调整，利用博弈论粗糙集（GTRSs）细化分类阈值。具体而言，首先考虑信任传播中介的贴现率和路径可靠性，进行间接信任传播和多路径融合，并基于信任关系和相似度得到合作指数（CI）；其次，采用LA对dm进行聚类，以CI作为边缘权重，确保社区反映社会关系和意见共识；通过综合综合SHs、CI、相似度和度中心性，客观确定决策者和子群体的权重。第三，提出了一种涉及博弈论的多粒度共识调整方法。该方法考虑联合调整、合作博弈和非合作博弈三种调整情景，在保证参与者个体利益的前提下，获得最优的调整策略。然后，通过gtrs的应用，提高分类阈值的通用性和准确性。最后，通过问卷调查对景区进行评价，验证了所提方法的可行性和有效性。
{"title":"Game-theoretic multi-granularity consensus adjustment for social network group decision-making","authors":"Hanzhong Hou , Chao Zhang , Deyu Li , Wentao Li","doi":"10.1016/j.ijar.2025.109610","DOIUrl":"10.1016/j.ijar.2025.109610","url":null,"abstract":"<div><div>The continuous trend development of information intelligence has made social network group decision-making (SN-GDM) increasingly important. However, in the context of SN-GDM, there are three key challenges: how to properly handle the decline in the trust propagation efficiency; the single granularity consensus adjustment strategy may not fully consider the impact of groups with high consensus on costs and the different cooperation intention of subgroups; traditional methods often face challenges in determining high-performance classification thresholds. In response to the above issues, the solution involves reconstructing trust relationships and optimizing clustering via the Leiden algorithm (LA) and structural holes (SHs). Moreover, multi-granularity consensus adjustment is implemented using game theory while classification thresholds are refined with game-theoretic rough sets (GTRSs). More specifically, firstly, the discount rate of trust propagation intermediaries and path reliability are considered to conduct indirect trust propagation and multipath fusion, and the cooperation index (CI) is obtained based on trust relationships and similarities. Secondly, LA is used to cluster DMs, with the CI as the edge weight, to ensure that the community reflects social relationships and opinion consensus. The weight of decision-makers (DMs) and subgroups are objectively determined by comprehensively integrating SHs, CI, similarity and in-degree centrality. Thirdly, a multi-granularity consensus adjustment method involving game theory is proposed. This method considers three adjustment scenarios: joint adjustment, cooperative game, and non-cooperative game, to obtain the optimal adjustment strategy while ensuring the individual benefit of participants. Then, the generality and accuracy of classification thresholds are improved via the application of GTRSs. Finally, a case study is conducted on the evaluation of scenic spots via a questionnaire survey, verifying the feasibility and effectiveness of the proposed method.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"190 ","pages":"Article 109610"},"PeriodicalIF":3.0,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145735355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-06DOI: 10.1016/j.ijar.2025.109608
Chiara Manganini, Esther Anna Corsi, Giuseppe Primiero
In the present work, we develop a novel information-theoretic and logic-based approach to data bias in Machine Learning predictions and show its relevance in the specific context of fairness evaluation. We frame predictions made on biased data as Ulam games, which formalise key aspects of data-driven inference, and from which a variation of the rational non-monotonic consequence relation can be defined. We investigate this framework to model how differential levels of noise in input features impact Machine Learning predictions. To the best of our knowledge, this is the first game-theoretic formalisation of ML unfairness.
{"title":"Data speak but sometimes lie: A game-theoretic approach to data bias and algorithmic fairness","authors":"Chiara Manganini, Esther Anna Corsi, Giuseppe Primiero","doi":"10.1016/j.ijar.2025.109608","DOIUrl":"10.1016/j.ijar.2025.109608","url":null,"abstract":"<div><div>In the present work, we develop a novel information-theoretic and logic-based approach to data bias in Machine Learning predictions and show its relevance in the specific context of fairness evaluation. We frame predictions made on biased data as Ulam games, which formalise key aspects of data-driven inference, and from which a variation of the rational non-monotonic consequence relation can be defined. We investigate this framework to model how differential levels of noise in input features impact Machine Learning predictions. To the best of our knowledge, this is the first game-theoretic formalisation of ML unfairness.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"190 ","pages":"Article 109608"},"PeriodicalIF":3.0,"publicationDate":"2025-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145788224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-06DOI: 10.1016/j.ijar.2025.109606
Svante Linusson, Petter Restadh, Liam Solus
The basic problem of causal discovery is concerned with estimating a directed acyclic graph (DAG) representing the dependence relations in multivariate data. Several successful causal discovery algorithms have optimization-based aspects, which operate via a set of rules for searching the space of DAGs. Recent results have revealed that the edge graph of the so-called characteristic imset polytope, , can provide a diverse set of such rules. Characterizing the edge graph of is a generally challenging problem. However, many algorithms first estimate the adjacencies in the causal DAG, in the form of an undirected graph G, prior to orienting the edges. In this regime, knowledge of the subpolytope defined for DAGs with adjacencies specified by G is valuable. In this paper, we characterize the edge graph of when G is an undirected tree, providing the first family of characteristic imset polytopes for which the edge graph is completely understood. These results are applied to give a new causal discovery algorithm that estimates a polytree representing the dependencies in the given multivariate data. Our algorithm is shown to out-perform comparable methods on both real and synthetic data. Our results also reveal connections between characteristic imset polytopes and the well-studied stable set polytopes from combinatorial optimization.
{"title":"On the edges of characteristic imset polytopes","authors":"Svante Linusson, Petter Restadh, Liam Solus","doi":"10.1016/j.ijar.2025.109606","DOIUrl":"10.1016/j.ijar.2025.109606","url":null,"abstract":"<div><div>The basic problem of causal discovery is concerned with estimating a directed acyclic graph (DAG) representing the dependence relations in multivariate data. Several successful causal discovery algorithms have optimization-based aspects, which operate via a set of rules for searching the space of DAGs. Recent results have revealed that the edge graph of the so-called characteristic imset polytope, <span><math><msub><mrow><mrow><mi>C</mi></mrow><mi>I</mi><mi>M</mi></mrow><mi>p</mi></msub></math></span>, can provide a diverse set of such rules. Characterizing the edge graph of <span><math><msub><mrow><mrow><mi>C</mi></mrow><mi>I</mi><mi>M</mi></mrow><mi>p</mi></msub></math></span> is a generally challenging problem. However, many algorithms first estimate the adjacencies in the causal DAG, in the form of an undirected graph <em>G</em>, prior to orienting the edges. In this regime, knowledge of the subpolytope <span><math><msub><mrow><mrow><mi>C</mi></mrow><mi>I</mi><mi>M</mi></mrow><mi>G</mi></msub></math></span> defined for DAGs with adjacencies specified by <em>G</em> is valuable. In this paper, we characterize the edge graph of <span><math><msub><mrow><mrow><mi>C</mi></mrow><mi>I</mi><mi>M</mi></mrow><mi>G</mi></msub></math></span> when <em>G</em> is an undirected tree, providing the first family of characteristic imset polytopes for which the edge graph is completely understood. These results are applied to give a new causal discovery algorithm that estimates a polytree representing the dependencies in the given multivariate data. Our algorithm is shown to out-perform comparable methods on both real and synthetic data. Our results also reveal connections between characteristic imset polytopes and the well-studied stable set polytopes from combinatorial optimization.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"191 ","pages":"Article 109606"},"PeriodicalIF":3.0,"publicationDate":"2025-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145923702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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