Degree Of Consistency Research Articles

Comparison of SAT-Based and ASP-Based Algorithms for Inconsistency Measurement

We present algorithms based on satisfiability problem (SAT) solving, as well as answer set programming (ASP), for solving the problem of determining inconsistency degrees in propositional knowledge bases. We consider six different inconsistency measures whose respective decision problems lie on the first level of the polynomial hierarchy. Namely, these are the contension, forgetting-based, hitting set, max-distance, sum-distance, and hit-distance inconsistency measures. In an extensive experimental analysis, we compare the SAT-based and ASP-based approaches with each other, as well as with a set of naive baseline algorithms. Our results demonstrate that, overall, both the SAT-based and the ASP-based approaches clearly outperform the naive baseline methods in terms of runtime. The results further show that the proposed ASP-based approaches perform superior to the SAT-based ones with regard to all six inconsistency measures considered in this work. Moreover, we conduct additional experiments to explain the aforementioned results in greater detail.

Resveratrol Bioavailability After Oral Administration: A Meta-Analysis of Clinical Trial Data.

Annually, a growing body of studies substantiates the health advantages of polyphenolic compounds, yet their practical application is constrained by swift metabolism and low bioavailability. Resveratrol, a stilbene derivative showcasing typical polyphenolic traits, is particularly noteworthy. Despite abundant bioavailability data from invitro and animal studies, applying these findings to humans demands nuanced consideration. The objective of this article is to conduct a meta-analysis on clinical trial data, systematically assessing the oral bioavailability of resveratrol and deriving meaningful insights into its efficacy in humans. To achieve this goal, we thoroughly examined publications across five major global databases: PubMed, Cochrane Library, Scopus, Embase, and Science Direct. The study exclusively included clinical trials involving healthy adults, where pharmacokinetic parameters were measured following the oral administration of at least one dose of resveratrol as a single preparation. For the meta-analysis data extraction, the mean score and standard deviation (SD) were included. Heterogeneity, degree of inconsistency between studies, and meta-regression were assessed. From these searches, we scrutinized data from 84 oral administrations encompassing nine resveratrol doses ranging from 25 to 5000 mg. Our findings indicate a linear increase in the amount of free resveratrol entering the bloodstream with the administered dose, while Tmax values remain unaffected. The mean maximum plasma concentration of resveratrol (31.07 ng/mL) closely mirrors the mean Cmax observed in the group administered a medium resveratrol dose ranging from 100 to 500 mg (33.59 ng/mL). This similarity implies the appropriateness of employing these specific doses of resveratrol, taking into consideration both its bioavailability and very low risk of potential side effects. However, the analysis of available human oral bioavailability data is constrained by methodological inconsistencies prevalent in existing studies. The meta-analysis underscores substantial heterogeneity, underscoring the imperative for multiple studies to rectify this prevailing trend.

(De)Noise: Moderating the Inconsistency Between Human Decision-Makers

Prior research in psychology has found that people's decisions are often inconsistent. An individual's decisions vary across time, and decisions vary even more across people. Inconsistencies have been identified not only in subjective matters, like matters of taste, but also in settings one might expect to be more objective, such as sentencing, job performance evaluations, or real estate appraisals. In our study, we explore whether algorithmic decision aids can be used to moderate the degree of inconsistency in human decision-making in the context of real estate appraisal. In a large-scale human-subject experiment, we study how different forms of algorithmic assistance influence the way that people review and update their estimates of real estate prices. We find that both (i) asking respondents to review their estimates in a series of algorithmically chosen pairwise comparisons and (ii) providing respondents with traditional machine advice are effective strategies for influencing human responses. Compared to simply reviewing initial estimates one by one, the aforementioned strategies lead to (i) a higher propensity to update initial estimates, (ii) a higher accuracy of post-review estimates, and (iii) a higher degree of consistency between the post-review estimates of different respondents. While these effects are more pronounced with traditional machine advice, the approach of reviewing algorithmically chosen pairs can be implemented in a wider range of settings, since it does not require access to ground truth data.

Balancing validity and reliability as a function of sampling variability in forensic voice comparison

In forensic comparison sciences, experts are required to compare samples of known and unknown origin to evaluate the strength of the evidence assuming they came from the same- and different-sources. The application of valid (if the method measures what it is intended to) and reliable (if that method produces consistent results) forensic methods is required across many jurisdictions, such as the England & Wales Criminal Practice Directions 19A and UK Crown Prosecution Service and highlighted in the 2009 National Academy of Sciences report and by the President’s Council of Advisors on Science and Technology in 2016. The current study uses simulation to examine the effect of number of speakers and sampling variability and on the evaluation of validity and reliability using different generations of automatic speaker recognition (ASR) systems in forensic voice comparison (FVC). The results show that the state-of-the-art system had better overall validity compared with less advanced systems. However, better validity does not necessarily lead to high reliability, and very often the opposite is true. Better system validity and higher discriminability have the potential of leading to a higher degree of uncertainty and inconsistency in the output (i.e. poorer reliability). This is particularly the case when dealing with small number of speakers, where the observed data does not adequately support density estimation, resulting in extrapolation, as is commonly expected in FVC casework.

“I don't care about cookies!” data disclosure and time-inconsistent users

Time-inconsistent internet users neglect future privacy costs and release too much data to digital firms. We study how regulation that requires user consent for data processing affects firm profits, user surplus, and welfare, depending on the degree of time inconsistency and on firms' business models. If the firm appropriates sufficiently high profits from data, consent mechanisms increase welfare only if their design facilitates consent refusal and time inconsistency is neither too high nor too low. If firms can make it difficult to opt out, it may be better for society to let the former choose the disclosure level. However, consent policies increase user surplus when time inconsistency is high. Voluntary caps on usage can raise profits by making some users disclose more data.

Between faces: childhood adversity is associated with reduced threat-safety discrimination during facial expression processing in adolescence

BackgroundChildhood adversity has been associated with alterations in threat-related information processing, including heightened perceptual sensitivity and attention bias towards threatening facial expressions, as well as hostile attributions of neutral faces, although there is a large degree of variability and inconsistency in reported findings.MethodsHere, we aimed to implicitly measure neural facial expression processing in 120 adolescents between 12 and 16 years old with and without exposure to childhood adversity. Participants were excluded if they had any major medical or neurological disorder or intellectual disability, were pregnant, used psychotropic medication or reported acute suicidality or an ongoing abusive situation. We combined fast periodic visual stimulation with electroencephalography in two separate paradigms to assess the neural sensitivity and responsivity towards neutral and expressive, i.e. happy and angry, faces. Linear mixed effects models were used to assess the impact of childhood adversity on facial expression processing.ResultsSixty-six girls, 53 boys and one adolescent who identified as ‘other’, between 12 and 16 years old (M = 13.93), participated in the current study. Of those, 64 participants were exposed to childhood adversity. In contrast to our hypotheses, adolescents exposed to adversity show lower expression-discrimination responses for angry faces presented in between neutral faces and higher expression-discrimination responses for happy faces presented in between neutral faces than unexposed controls. Moreover, adolescents exposed to adversity, but not unexposed controls, showed lower neural responsivity to both angry and neutral faces that were simultaneously presented.ConclusionsWe therefore conclude that childhood adversity is associated with a hostile attribution of neutral faces, thereby reducing the dissimilarity between neutral and angry faces. This reduced threat-safety discrimination may increase risk for psychopathology in individuals exposed to childhood adversity.

A new data complexity measure for multi-class imbalanced classification tasks

The skewed class distribution and data complexity may severely affect the imbalanced classification results. The cost of classification can be significantly reduced if these data complexity are measured and pre-processed prior to training, particularly when dealing with large-scale and high-dimensional datasets. Although many methods have been proposed to evaluate data complexity, most of them fail to fully consider the interaction among different data characteristics, or the connection between class imbalance and these characteristics, thus posing a serious challenge to effectively evaluate the difficulty of classification. This paper presents a new data complexity measure MFII (multi-factor imbalance index), which measures the combined effects of the skewed class distribution and data characteristics on classification difficulty. In particular, it further comprehensively investigates the impact of overlap size, confusion degree, and sub-cluster structure. VoR (value of resolution) and DoC (degree of consistency) are also proposed to evaluate the resolution and interpretability of complexity measures. The experimental results demonstrate that MFII has lower VoR and a stronger correlation with classification metrics, which indicates that MFII can more accurately evaluate the difficulty of multi-class imbalanced classification tasks.

An extended belief rule-based system with hybrid sampling strategy for imbalanced rule base

Imbalanced dataset is an important focus for classification. As the mainstream of addressing imbalanced dataset, data-level methods are trapped in facing difficultly-determined subjective parameters, and the inconsistency between new minority samples and original minority samples simultaneously. To address it, this paper develops an extended belief-rule-based (EBRB) system with hybrid sampling strategy, which is a white-box classifier. The hybrid sampling strategy is composed of an under-sampling process and an oversampling process, in which subjective parameters are not involved. The under-sampling is to identify and remove overlapping majority rules by iteratively determining an appropriate objective threshold for calculating the inconsistency degree of rule base, and to determine and remove redundant non-overlapping majority rules by using the density of non-overlapping rules in clustered groups. The oversampling is to design a differential evolution based iterative process to generate new minority rules in groups by minimizing the inconsistency of rule base. The distribution of original dataset is maintained extremely by balancing rules in clustered majority and minority groups, respectively. This EBRB system is used for the auxiliary diagnosis of thyroid nodules, and its superior performance is highlighted by the comparison with existing EBRB systems, representative data-level methods, and algorithm-level methods.

Study on the spatial relationship between road network and the diversity of urban public facilities: the case of the central area of Changsha City

In the context of contemporary urbanization, the significance of diversifying urban public facilities has attracted significant attention. This study examines the relationship between road network and the diversity of urban public facilities in Changsha City. These factors are measured through the Shannon-Weiner Index and Space Syntax method. To provide greater specificity, the study employs Random Forest and Geographically Weighted Regression models to analyze the relationship between road networks and the diversity of urban public facilities. The results identify a nonlinear relationship between these variables. In addition, high accessibility exhibits a stronger association with diversity than accessibility alone, and this correlation is reflected in varying degrees of inconsistency across different study areas. Finally, the cartographic depiction of diversity clusters is overlaid on the road network, demonstrating a significant relationship between the configuration of the road network and diversity patterns. In conclusion, this research emphasizes the robust correlation between the roadway network and the diversity of urban public facilities. It prompts local governments to focus beyond mere equality, with a greater commitment to enhancing the quality of life for residents through diversity.

Advancing the discourse: A next-generation value chain-based taxonomy for circular economy key performance indicators

The growth of interest in circular economy (CE) has been accompanied by different approaches to measurement of CE outcomes and impacts, leading to a wide portfolio of indicators with varying degrees of overlap, inconsistency, and convergence. The aim of this paper is to propose a unifying framework for CE indicators, as the next generation of CE taxonomies. We first undertake a scoping review of 59 review papers on CE indicators using manual and computational methods (i.e., topic modelling) to inform the taxonomy structure and content. As a result, we report on 11 clusters of approaches that have been attentive to different dimensions of CE (e.g. horizontal value chain, vertical scale of operation (macro, meso, micro), impact category (economic, biophysical, social), material vs product focus, etc.). Highlighting the strengths and weakness of these approaches, we identify gaps in dimensions related to horizontal and vertical scales of measurement, and propose an agnostic, integrative framework that builds on the scientific foundations of previous research, within a more systemic and comprehensive taxonomy. This taxonomy could be used as a guiding framework or heuristic for regulators, both nationally and internationally, and for practitioners to undertake a comprehensive measurement and assessment of CE related interventions and initiatives at scale.

Optimal Contracts for Time-Inconsistent Consumers with Heterogeneous Beliefs

In many markets (e.g., cell phones, video games), firms offer menus of contracts that include some tariffs charging per-use prices above marginal cost and others below marginal cost. We term this puzzling phenomenon as two-sided deviations from marginal cost pricing and present a potential explanation based on two well-recognized consumer characteristics. The first one is that consumers’ actual consumptions may depart systematically from their initial plans (i.e., time-inconsistent preferences). In addition, consumers can be either sophisticated or naive in their beliefs about their time inconsistency (i.e., heterogenous beliefs). We characterize properties of the optimal contracts for a firm to screen the time-inconsistent consumers with heterogeneous beliefs. We articulate the conditions under which the optimal menu may account for two-sided deviations from marginal cost pricing. We also show that, contrary to intuition, a higher degree of time inconsistency may reduce firm profit and increase social welfare. Meanwhile, reducing consumer naivete may harm the society. Moreover, we confirm that our main results are robust to the presence of time-consistent consumers. This paper was accepted by Dmitri Kuksov, marketing. Funding: This work was supported by the Hunan Provincial Natural Science Foundation of China [Grant 2022JJ40102]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.02653 .

Discrepancies between self- and proxy-rated quality of life in people living with dementia.

The aim of this study was to analyze discrepancies between self- and proxy-rated health-related quality of life (HRQoL), measured with the EuroQol 5 Dimension 5 Level survey (EQ-5D-5L), in people living with dementia (PlwD) and their caregivers on an individual response level. EQ-5D-5L, sociodemographic and clinical data were obtained from baseline data of n = 174 dyads of a cluster-randomized, controlled intervention trial. Self- and proxy-rated EQ-5D-5L health profiles were evaluated in terms of response distribution and agreement (weighted Kappa), and discrepancies in individual dimension level were analyzed using the Paretian Classification of Health Change (PCHC) as well as the presence and degree of inconsistencies between ratings. PlwD had a mean age of 80.1, nearly the half were female and 82.3% were mildly to moderately cognitively impaired. PlwD reported a higher utility index than caregiver proxies (mean 0.75vs. 0.68, 83% of PlwD>0.5). According to the PCHC and inconsistency approach, 95% of PlwD rated their health differently compared to proxies; 66% with divergent responses in at least three EQ-5D-5L dimensions. Nine dyads (5%) showed identical ratings. Discrepancies of one higher or lower EQ-5D-5L response represented the most frequent discrepancy (35.4%). Caregivers were two times more likely to report "moderate problems," representing the middle of the 5-point Likert scale. Usual activities had the lowest agreement between ratings (weighted kappa = 0.23). In PlwD reporting no or some problems in EQ-5D-5L-dimensions, proxies were more likely to report more problems and vice versa, especially in the more observable dimension usual activities and less likely in the less observable domains pain/discomfort and anxiety/depression. The central tendency bias observed in proxy-ratings could be associated with assessment uncertainties, resulting in an underestimation (overestimation) in PlwD reporting better (worse) health. This diverging trend extends the knowledge from previous studies and underlines the need for more methodological research in this area. People living with dementia (PlwD) rate their health differently than proxies.Proxy-ratings over- or underestimate PlwD health when self-ratings are low or high.Proxies indicate a possible central tendency bias.Further research is needed to understand influencing factors.

An Empirical Analysis on Performance Inconsistency among Environmental, Social and Governance Components of ESG Ratings

Purpose - The purpose of this study is to empirically investigate the degree of performance inconsistency among the Environmental, Social and Governance ratings. Design/methodology/approach - This study performs regression analysis using the widely used ESG ratings published by the Korea Institute of Corporate Governance and Sustainability. Findings - The results show that firms often do not show consistent performance across the Environmental, Social and Governance aspects, with excellent performance on one aspect but mediocre or poor performance on another. The paper also finds some degree of firm-level persistence in such performance inconsistency, suggesting that the traits of the firm and the industry the firm belongs to might influence whether a firm shows inconsistent performance across the three aspects. Research implications or Originality - This paper highlights the need for researchers and practitioners to understand the underlying behavior of the individual E, S and G ratings, instead of taking them as given, in order to properly design their analyses.

Methodology for Creating a Risk Management System for Drug-Drug Interactions

Drug-drug interactions (DDIs) are a major cause of hospital admissions, accounting for 16.6% of ADRs and about 1% of all hospital admissions. Polypharmacy is on the rise, with a Swedish population-based study of people aged ;;;75 years showing that the prevalence of polypharmacy increased from 27% in 1988 to 54% in 2001 and to 65% in 2006. The aim of the study was to analyze the databases on DDI and to assess the possibility of their use in managing the risks of pharmacotherapy in the Russian Federation and the EAEU. Results. The main DDI databases are characterized. The degree of inconsistency in data on the number of DDI, severity and clinical recommendations was determined. The basic requirements for the completeness of information in databases are formulated. The necessity to harmonize approaches to providing information about DDI, as well as the need to create local databases for medical organizations were revealed. A computer program has been created to minimize the risks of DDI in clinical practice, for expert and scientific purposes. Conclusions. Databases and programs for DDI have different goals, a different set of data on DDI with varying degrees of evidence, differences in assessment of severity, likelihood of occurrence, clinical recommendations for treatment and correction of DDI. The variability of information is due to the lack of a unified system for assessing the severity and likelihood of potential negative consequences of DDI; different purposes for creation; difference in budgets at creation. This situation leads to warning redundancy and physician fatigue from insignificant or unproven DDIs. Based on the identified requirements for local databases, a computer program for modeling rational pharmacotherapy for lower respiratory tract infections was created and received a certificate of state registration.

A two-objective-optimization-driven group decision making model under the bipolarity of decision information

When building consensus in group decision making (GDM) under uncertainty, an important yet rarely studied issue is to find the Pareto solutions of multi-objective optimization model. This paper reports a two-objective (2Ob) optimization driven consensus model in GDM by describing the bipolarity of judgements through intuitionistic multiplicative preference relations (IMPRs). First, it is realized that the inherent property of IMPRs is the hesitancy degree. A novel inconsistency index of IMPRs is proposed by combining the effects of hesitancy degree and inconsistency of boundary matrices. Second, the compatibility measure between two IMPRs is utilized to quantify the consensus level (CL) of decision makers. The threshold of acceptable group CL is found to decrease with the order of IMPRs for the first time. A 2Ob optimization model is constructed by minimizing group inconsistency degree and group CL, respectively. Third, the method of equipping two flexibility degrees to each expert is proposed for optimizing individual IMPRs. It is interesting to find that the constructed granularity matrix is different from interval-valued IMPRs. A multi-objective particle swarm optimization algorithm is adopted to obtain a set of Pareto solutions to GDM problems. Case studies are carried out to illustrate the proposed consensus reaching model. The results help to identify how to provide flexible decisions in GDM under some complexity and uncertainty of a practical problem.

Optimal investor life cycle decisions with time-inconsistent preferences

We examine the lifetime decisions of an investor with time-inconsistent preferences, obtaining optimal time-consistent strategies for investment, consumption, and life insurance premiums explicitly under a stationary Markov perfect equilibrium framework. We find that the investor increases consumption to seek immediate gratification, and simultaneously increases life insurance purchasing to fulfill a legacy need. However, at a later stage in the life cycle, the investor confronts slower wealth accumulation and reduces consumption and life insurance purchasing accordingly. We also investigate the effects of naivete, degree of time inconsistency, risk preferences, legacy weight, insurance cost, death duty, and labor income on the investor's decision making. Our study reveals the balance of consumption, bequest, and terminal wealth motives in life cycle decisions featuring microfounded bequest utility and stochastic labor income.

Fusing daily snow water equivalent from 1980 to 2020 in China using a spatiotemporal XGBoost model

Accurate snow water equivalent (SWE) data are crucial for estimating the water supply capacity of snow and are of profound importance for the communities of hydrology, climatology, and ecology. However, existing SWE products suffer from varying degrees of uncertainty, incompleteness, and inconsistency. This study proposes a novel spatiotemporal fusion model to estimate SWE by combining multisource SWE products (GlobSnow, ERA-Interim, ERA5-Land, GLDAS, and CSWE) and spatiotemporal covariates (land cover, temperature, snow-related, geospatial, and temporal features) with the extreme gradient boosting (XGBoost) model. Specifically, the proposed spatiotemporal XGBoost model can encapsulate the advantages of multisource SWE products and address the nonlinear relations between SWE and its influencing variables. Furthermore, it embeds the geospatial and temporal features to capture the heterogeneities of SWE. Evaluation results on daily SWE records from 647 meteorological sites in China show that the proposed XGBoost SWE outperforms existing SWE products and other fusion methods (RF, SVM, RR, SA, and MLR), which demonstrates the effectiveness of the model with R values of 0.77, 0.70 and MAE and RMSE values of 7.54, 8.62 mm and 12.29, 13.73 mm based on cross-validation according to year and site, respectively. Using the proposed model, a dataset of daily SWE from 1980 to 2020 with a spatial resolution of 25 km over China is provided, aiming to deliver accurate SWE data for hydrological research and water resource management.

Music as Support for Older Adults’ Wellbeing: A Scoping Review

This scoping review investigates the status quo of the research on the role of music in old people's wellbeing, presenting the recent results and critically analyzing the conceptual clarity within the research fields. A search of peer-reviewed publications from January 2010 to December 2022 was conducted, regarding music's role in the wellbeing of this growing group of aging people. A total of 141 articles were included in an extensive screening. Through the analysis, we identified five main mechanisms, through which music may support wellbeing in this age group. These are (1) cognitive and physical rehabilitation through music, (2) social engagement in musical group activities, (3) individual's engagement with music for emotional support, self-connection, and meaning-making, (4) supporting the wellbeing of social environments around older adults, and (5) enhancing accessibility and age-related adaptation within musical activities. Also, we identified a considerable degree of inconsistency in conceptual clarity, methodology, and theoretical approaches used in researching this rather undefined topic. A compilation of research paradigms and recommendations for future research are presented.

TOWARDS THE PROBLEM OF SYNTHESIS OF DECISIONS UNDER FUZZY CONDITIONS: AGREEMENT OF INDIVIDUAL EXPERT ASSESSMENTS

The essence of the method of expert evaluations is a logical-intuitive analysis of the internal and external environment of the management object, the development of alternatives and a quantitative assessment of their quality. The generalized opinion of experts serves as a basis for choosing an adequate decision option. The complex use of intuitive and logical thinking, as well as the formal processing of experts judgments allows to obtain indicators of the quality of alternatives in a multi-criteria decision-making task. One of the common methods of processing expert information is the method of paired comparisons by T. Saati. Each expert submits conclusions in the form of a square matrix, the elements of which are formed according to the scale of the relative importance of the alternative. The consistency of the matrix of pairwise comparisons means that the relative weight coefficients of the alternatives retain their value, regardless of how the comparison is performed. Given that pairwise comparison matrices are built on the basis of human judgment and assumptions, some degree of inconsistency between its elements is to be expected. Such inconsistency is not critical, if it does not go beyond the defined limits. The article considers the method of harmonizing individual assessments of experts in the problem of synthesis of a decision under unclear conditions.

2-SAT discrete Hopfield neural networks optimization via Crow search and fuzzy dynamical clustering approach

<abstract> <p>Within the swiftly evolving domain of neural networks, the discrete Hopfield-SAT model, endowed with logical rules and the ability to achieve global minima of SAT problems, has emerged as a novel prototype for SAT solvers, capturing significant scientific interest. However, this model shows substantial sensitivity to network size and logical complexity. As the number of neurons and logical complexity increase, the solution space rapidly contracts, leading to a marked decline in the model's problem-solving performance. This paper introduces a novel discrete Hopfield-SAT model, enhanced by Crow search-guided fuzzy clustering hybrid optimization, effectively addressing this challenge and significantly boosting solving speed. The proposed model unveils a significant insight: its uniquely designed cost function for initial assignments introduces a quantification mechanism that measures the degree of inconsistency within its logical rules. Utilizing this for clustering, the model utilizes a Crow search-guided fuzzy clustering hybrid optimization to filter potential solutions from initial assignments, substantially narrowing the search space and enhancing retrieval efficiency. Experiments were conducted with both simulated and real datasets for 2SAT problems. The results indicate that the proposed model significantly surpasses traditional discrete Hopfield-SAT models and those enhanced by genetic-guided fuzzy clustering optimization across key performance metrics: Global minima ratio, Hamming distance, CPU time, retrieval rate of stable state, and retrieval rate of global minima, particularly showing statistically significant improvements in solving speed. These advantages play a pivotal role in advancing the discrete Hopfield-SAT model towards becoming an exemplary SAT solver. Additionally, the model features exceptional parallel computing capabilities and possesses the potential to integrate with other logical rules. In the future, this optimized model holds promise as an effective tool for solving more complex SAT problems.</p> </abstract>