Disagreement Measure Research Articles

Dispersion of Beliefs Bounds: Sentimental Recovery

We present a nonparametric method to recover a bound on ex ante dispersion of beliefs (DBB) from asset prices with minimal assumptions. DBB constrains the dispersion among all possible distributions in an economy, consistent with observed prices and subject to a good-deal bound. In model-based economies, DBB effectively tracks belief heterogeneity and serves as a diagnostic tool for evaluating model calibrations. Empirically, DBB relates to common proxies of belief dispersion, offering a real-time, market-implied disagreement measure. Our versatile approach applies to both complete and incomplete markets represented by any asset class. This paper was accepted by Kay Giesecke, finance. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.01587 .

A machine learning approach to color space Euclidization

AbstractIn this work, a machine learning methodology is proposed for the issue of color space Euclidization. Given a color difference formula as reference distance law, the Euclidization task consists in finding an injective transformation from the original color space into a real vector space and the corresponding inverse transformation, such that the Euclidean distances in the embedded color space align with the reference color distances. For this, artificial neural networks are devised as function approximators for the color space transformations being sought. Training these neural networks is accomplished through unsupervised learning, making use of random sampling and gradient descent. As key disagreement measure, either the (symmetric) relative isometric disagreement or the standardized residual sum of squares (STRESS) index is considered at a time and incorporated as part of the optimization criterion into the objective function. Comparative evaluation is carried out on well‐established color distance laws, including the CIELAB‐based DE2000 color difference formula. The evaluation results indicate significant performance advantages of the proposed approach over previous contributions.

How to measure disagreement as a premise for learning from controversy in a social media context

Learning scenarios building on disagreement in a learning group or a whole classroom are well established in modern pedagogy. In the specific tradition of collaborative learning, such approaches have been traced back to theories of socio-cognitive conflict and have been associated with argumentative learning interactions. An important premise for these types of learning scenarios is the identification of disagreement. In the spirit of learning analytics, this calls for analytic tools and mechanisms to detect and measure disagreement in learning groups.Our mathematical analysis of several methods shows that methods of different origin are largely equivalent, only differing in the normalization factors and ensuing scaling properties. We have selected a measure that scales best and applied it to a target scenario in which learners judged types and levels of “toxicity” of social media content using an interactive tagging tool. Due restrictions imposed by the pandemic, we had to replace the originally envisaged classroom scenario by online experiments. We report on two consecutive experiments involving 42 students in the first and 89 subjects in the second instance. The results corroborate the adequacy of the measure in combination with the interactive, game-based approach to collecting judgements. We also saw that a revision of categories after the first study reduced the ambiguity. In addition to applying the disagreement measure to the learner judgements, we also assessed several personality traits, such as authoritarianism and social closeness. Regarding the dependency of the learner judgements on personality traits, we could only observe a weak influence of authoritarianism.

Central bank’s forecasts and lack of transparency: An assessment of the effect on private expectations in a large emerging economy

We analyze whether the Central Bank of Brazil’s Inflation Reports projections influences the private’s inflation expectations. Specifically, we investigate how the central bank’s inflation forecasts affect the private sector’s inflation expectations through a qualitative and quantitative examination of the disagreement measure between them. Furthermore, we appraise if the lack of transparency resulting from the difference between the central bank’s inflation forecasts and the realized inflation affects the private’s inflation expectations. Although the findings confirm the previous studies that point out that the central bank transparency can affect the readjustment of market expectations, the results do not rule out the possibility of the central bank’s forecast and private’s inflation expectations being affected reciprocally.

Intelligent ensembling of auto-ML system outputs for solving classification problems

Automatic Machine Learning (Auto-ML) tools enable the automatic solution of real-world problems through machine learning techniques. These tools tend to be more time consuming than standard machine learning libraries, therefore, exploiting all the available resources to the full is a valuable feature. This paper presents a two-phase optimization system for solving classification problems. The system is designed to produce more robust classifiers by exploiting the different architectures that are generated while solving classification problems with Auto-ML tools, particularly AutoGOAL. In the first phase, the system follows a probabilistic strategy to find the best combination of algorithms and hyperparameters to generate a collection of base models according to certain diversity criteria; and in the second, it follows similar Auto-ML strategies to ensemble those models. The HAHA 2019 challenge corpus and the Adult dataset were used to evaluate the system. The experimental results show that: i) a better solution can be built by ensembling a subset of the already tested models; ii) the performance of ensemble methods depends on the collection of base models used; and, iii) ensuring diversity using the double-fault measure produces better results than the disagreement measure. The source code is available online for the research community.

Dynamic Bargain Game Theory in the Internet of Things for Data Trustworthiness.

Smart home and smart building systems based on the Internet of Things (IoT) in smart cities currently suffer from security issues. In particular, data trustworthiness and efficiency are two major concerns in Internet of Things (IoT)-based Wireless Sensor Networks (WSN). Various approaches, such as routing methods, intrusion detection, and path selection, have been applied to improve the security and efficiency of real-time networks. Path selection and malicious node discovery provide better solutions in terms of security and efficiency. This study proposed the Dynamic Bargaining Game (DBG) method for node selection and data transfer, to increase the data trustworthiness and efficiency. The data trustworthiness and efficiency are considered in the Pareto optimal solution to select the node, and the bargaining method assigns the disagreement measure to the nodes to eliminate the malicious nodes from the node selection. The DBG method performs the search process in a distributed manner that helps to find an effective solution for the dynamic networks. In this study, the data trustworthiness was measured based on the node used for data transmission and throughput was measured to analyze the efficiency. An SF attack was simulated in the network and the packet delivery ratio was measured to test the resilience of the DBG and existing methods. The results of the packet delivery ratio showed that the DBG method has higher resilience than the existing methods in a dynamic network. Moreover, for 100 nodes, the DBG method has higher data trustworthiness of 98% and throughput of 398 Mbps, whereas the existing fuzzy cross entropy method has data trustworthiness of 94% and a throughput of 334 Mbps.

On the Link Between Subjective Score Prediction and Disagreement of Video Quality Metrics

Several video quality metrics (VQMs) have been proposed in many publications to predict how humans perceive video quality. It is common to observe significant disagreements amongst the quality predictions of these VQMs for the same video sequence. Following an extensive literature search, we found no publicised work that has investigated if such disagreements convey useful information on the accuracy of VQMs. Herein, a measure for quantifying the disagreement between VQMs is proposed. A small-scale subjective study is carried out to assess the effectiveness of our proposal. In particular, the proposed disagreement measure is shown to be extremely effective in determining whether the quality of any given processed video sequence (PVS) can be accurately predicted by the VQMs. This type of information is particularly useful for identifying video sequences that are likely to degrade the end-user’s quality of experience (QoE). Our proposal is also useful in selecting the most effective PVSs to be employed in a subjective test. We show that the proposed disagreement measure can be effectively predicted from bitstream features. This establishes a link between the capability to accurately assess the quality of a PVS and the way it is encoded. In addition, an analysis is conducted to compare the performances of some well-known and widely used open-source metrics and two proprietary metrics. The two proprietary metrics are used by a large media company for enhancing its delivery pipeline. The outcome of this comparison highlights the suitability of the open-source VQM, Video Multi-method Assessment Fusion (VMAF), as a good benchmark quality measure for both the industrial and academic environments.

KAPPA as Drift Detector in Data Stream Mining

Abstract Concept Drift is considered a challenging problem that appears in data streaming. The classifier’s error rate and the ensemble are used in most of the previous works to manage classification accuracy as a criterion for judging whether concept drift is happening or not. KAPPA is an effective way to measure the level of agreement, and it may be suitable to detect concept drift in a reliable, fast, and computationally efficient way. In this paper, we propose a new concept drift detector, called KAPPA, which aims at reacting to detect concept drift in a reliable, fast, and computationally efficient way. Contrary the disagreement measure that we have already considered in our preliminary work (DMDDM), KAPPA would measure the level of agreement when different classifiers access data items is suitable to detect concept drifts. The performance of KAPPA has been experimentally compared with DMDDM on synthetic dataset streams, considering different performance measures, e.g., delay detection, true positives and the mean accuracy.

Consensual Group-AHPSort: Applying consensus to GAHPSort in sustainable development and industrial engineering

Multi-Criteria Group Decision Making (MCGDM) deals with the process of taking decisions among a number of decision makers who evaluate different alternatives on several criteria that may be conflicting. In such a type of problems conflicts may are not only related to the criteria but also with the decision makers that could result in unsatisfactory solutions because of disagreements or deadlocks to achieve a decision. To avoid these undesirable results, consensus processes have been applied to group decision. The application of MCGDM has been mainly focused on decision problems for ranking and choice alternatives but, similarly to multi-criteria decision making, other types of problems such as sorting and description can be formulated. Although, a few number of MCGDM-sorting models, comparing with ranking and choice ones, have been proposed their importance and impact is quite significant nowadays. Hence, our aim is to consider the recent proposal on Group-AHPSort that did not consider any disagreement measure across the sorting decision process and study how the use of consensus processes can avoid some misclassifications or disagreements among the group of decision makers taking part in the MCGDM problem. To illustrate our approach, a case study focusing on the sorting of five European Union countries according to their sustainable development performance regarding social issues will show the importance and utility of detecting and smoothing disagreements in Group-AHPSort.

Disagreement About Fundamentals: Measurement and Consequences

We propose a measure of disagreement, which reflects differences of opinion as opposed to information asymmetry, that can be extracted from sequences of analyst forecasts. Using a Bayesian theoretical framework, we prove that, when analysts agree, a regression of an analyst's forecast on the previous forecast issued by another analyst should have a slope coefficient of one. The magnitude of the estimated regression coefficient's deviation from one is then employed as a disagreement measure. We validate the measure using tests tied to predicted relations between disagreement and trading volume and bid-ask spreads. Finally, we employ our measure to test for associations between disagreement and expected returns predicted by antecedent theoretical studies.

A framework for quality control of corpus callosum segmentation in large-scale studies

BackgroundThe corpus callosum (CC) is the largest white matter structure in the brain, responsible for the interconnection of the brain hemispheres. Its segmentation is a required preliminary step for any posterior analysis, such as parcellation, registration, and feature extraction. In this context, the quality control (QC) of CC segmentation allows studies on large datasets with no human interaction, and the proper usage of available automated and semi-automated algorithms. New methodWe propose a framework for QC of CC segmentation based on the shape signature, computed at 49 distinct resolutions. At each resolution, a support vector machine (SVM) classifier was trained, generating 49 individual classifiers. Then, a disagreement metric was used to cluster these individual classifiers. The final ensemble was constructed by selecting one representation from each cluster. ResultsThe proposed framework achieved an area under the curve (AUC) metric of 98.25% on the test set (207 subjects) employing an ensemble composed of 12 components. This ensemble outperformed all individual classifiers. Comparison with existing methodsTo the best of our knowledge, this is the first approach to assess quality of CC segmentations on large datasets without the need for a ground-truth. ConclusionsThe shape descriptor is robust and versatile, describing the segmentation at different resolutions. The selection of classifiers and the disagreement measure lead to an ensemble composed of high-quality and heterogeneous classifiers, ensuring an optimal trade-off between the ensemble size and high AUC.

Diversity measure as a new drift detection method in data streaming

Data stream mining is an important research topic that has received increasing attention due to its use in a wide range of applications, such as sensor networks, banking, and telecommunication. A serious and challenging problem affecting data stream mining is concept drift. This problem occurs when the relation between the input data and the target variable changes over time. Several concept drift detection methods have been proposed, however; they either suffer from a high cost in terms of memory or run time or they are not fast enough in terms of detection speed. In this work, we propose a method, called diversity measure as a new drift detection method (DMDDM), which reacts rapidly to concept drift in less time and with less memory consumption. The proposed method combines one of the diversity measures, disagreement measure, known from static learning in streaming scenarios with the Page-Hinkley test and uses these calculations to detect drifts. The proposed method has been experimentally compared with ten drift detection methods in different drift scenarios using several datasets. The experiment results show that the proposed method is capable of detecting concept drifts faster than most of the compared methods with minimal consumption in terms of memory and run time.

Disagreement beta

When two investors agree to disagree on market prospects and bet against each other, both expect to profit from their trades. Hence, an increase in disagreement leads to higher perceived trading profits and lower marginal utilities for both investors, so disagreement betas can affect cross-sectional asset returns. We construct a disagreement measure using professional forecasts of U.S. macroeconomic fundamentals. Betas with respect to this disagreement factor positively explain cross-sectional returns of stocks, corporate bonds, mortgage-backed securities, and government securities. Further tests using portfolio-based test assets confirm the significant pricing power of the disagreement factor on top of influential benchmark factors.

Biomedical Text Categorization Based on Ensemble Pruning and Optimized Topic Modelling.

Text mining is an important research direction, which involves several fields, such as information retrieval, information extraction, and text categorization. In this paper, we propose an efficient multiple classifier approach to text categorization based on swarm-optimized topic modelling. The Latent Dirichlet allocation (LDA) can overcome the high dimensionality problem of vector space model, but identifying appropriate parameter values is critical to performance of LDA. Swarm-optimized approach estimates the parameters of LDA, including the number of topics and all the other parameters involved in LDA. The hybrid ensemble pruning approach based on combined diversity measures and clustering aims to obtain a multiple classifier system with high predictive performance and better diversity. In this scheme, four different diversity measures (namely, disagreement measure, Q-statistics, the correlation coefficient, and the double fault measure) among classifiers of the ensemble are combined. Based on the combined diversity matrix, a swarm intelligence based clustering algorithm is employed to partition the classifiers into a number of disjoint groups and one classifier (with the highest predictive performance) from each cluster is selected to build the final multiple classifier system. The experimental results based on five biomedical text benchmarks have been conducted. In the swarm-optimized LDA, different metaheuristic algorithms (such as genetic algorithms, particle swarm optimization, firefly algorithm, cuckoo search algorithm, and bat algorithm) are considered. In the ensemble pruning, five metaheuristic clustering algorithms are evaluated. The experimental results on biomedical text benchmarks indicate that swarm-optimized LDA yields better predictive performance compared to the conventional LDA. In addition, the proposed multiple classifier system outperforms the conventional classification algorithms, ensemble learning, and ensemble pruning methods.

WhaleRank: an optimisation based ranking approach for software requirements prioritisation

Requirement prioritisation is one of the major areas in the software product development process. Ranking methods employed for prioritising orders the requirements based on their importance. Ranking the requirements contributes in enhancing the quality of the product using additional features and to attain customer satisfaction. However, effectiveness is the growing concern in requirement prioritisation. This paper proposes a ranking method WhaleRank to rank the requirement and the effectiveness of ranking gets enhanced using the whale optimisation algorithm (WOA). The WhaleRank method uses four ranking functions based on dictionary words, similarity measure, perception of the manager, and the newly updated requirements that are combined to form a linear rank using the ranking constants. WOA determines the optimal weights of the ranking constants that promote to determine the optimal rank for the requirements. Experimentation with the methods like CBRank, average rank, WhaleRank, and GA provide a comparative performance analysis that proves that the proposed WhaleRank outperforms all methods in terms of accuracy and disagreement measure (NDA) and the values of accuracy and NDA is 83.33% and 16.24% respectively.

WhaleRank: an optimisation based ranking approach for software requirements prioritisation

Requirement prioritisation is one of the major areas in the software product development process. Ranking methods employed for prioritising orders the requirements based on their importance. Ranking the requirements contributes in enhancing the quality of the product using additional features and to attain customer satisfaction. However, effectiveness is the growing concern in requirement prioritisation. This paper proposes a ranking method WhaleRank to rank the requirement and the effectiveness of ranking gets enhanced using the whale optimisation algorithm (WOA). The WhaleRank method uses four ranking functions based on dictionary words, similarity measure, perception of the manager, and the newly updated requirements that are combined to form a linear rank using the ranking constants. WOA determines the optimal weights of the ranking constants that promote to determine the optimal rank for the requirements. Experimentation with the methods like CBRank, average rank, WhaleRank, and GA provide a comparative performance analysis that proves that the proposed WhaleRank outperforms all methods in terms of accuracy and disagreement measure (NDA) and the values of accuracy and NDA is 83.33% and 16.24% respectively.

Disagreement, Underreaction, and Stock Returns

We explore analysts’ earnings forecast data to improve on one popular disagreement measure—the analyst forecast dispersion measure—proposed by Diether et al. [Diether KB, Malloy CJ, Scherbina A (2002) Differences of opinion and the cross section of stock returns. J. Finance 57:2113–2141]. Our analysis suggests that changes in the standard deviations of forecasted earnings can work as a complementary disagreement measure that is comparable across stocks and immune from other return-predictive information contained in the normalization scalars of analyst forecast dispersion measures. We also document evidence that the change-based disagreement measure predicts future cross-sectional returns significantly only when changes in the mean forecasts are negative. This finding suggests that the interaction between disagreement and underreaction to earnings news affects asset prices. This paper was accepted by Wei Jiang, finance.

Differences of Opinion and Stock Prices: Evidence from Spin-Offs and Mergers

I use the setting of corporate spin-offs to identify the impact of differences of opinion on stock prices. Based on a formalization of Miller (1977)'s hypothesis, and using data on investor holdings, I construct a novel measure of differences of opinion about the individual values of the components being separated. As predicted, higher disagreement is related to a significantly more positive event return. Importantly, since I focus on ex date returns, these findings are unrelated to the expected business impacts of the transactions, which should be incorporated in prices before the ex date. Placebo tests using only the expected component of my disagreement measure provide additional support that my tests are valid and that other factors that may be correlated with differences of opinion are not driving the results. Additional tests using mergers provide consistent results, and altogether these findings also provide new insight into the attribution of value created in these transactions.

Disagreement Measure Based Ensemble of Extreme Learning Machine for Gene Expression Data Classification

Selective ensemble learning has become a powerful tool for biological information analysis of gene expression data.In order to mining gene expression data better,we use ensemble of Extreme Learning Machine(ELM) to overcome the shortage that a single ELM is unstable in data classification.In this paper,we propose an algorithm,which is the dissimilarity ensemble based on disagreement measure of Extreme Learning Machines(D-D-ELM).First,we judge the dissimilarity of Extreme Learning Machines with disagreement measure.Then we remove the corresponding ELMs based on the average classification accuracy.At last,the rest ELMs are grouped into an ensemble classifier by the strategy of majority voting.This algorithm is applied on the data of gene expression Breast,Leukemia,Colon,Heart.The theoretical analysis and experiment are given and the statistical analysis on the experimental results demonstrates that D-D-ELM can achieve better classification accuracy with less number of ELMs.

Inflation uncertainty revisited: a proposal for robust measurement

Any measure of unobserved inflation uncertainty relies on specific assumptions which are most likely not fulfilled completely. This calls into question whether an individual measure delivers a reliable signal. To reduce idiosyncratic measurement error, we propose using common information contained in different measures derived from survey data, a variety of forecast models, and volatility models. We show that all measures are driven by a common component, which constitutes an indicator for inflation uncertainty. Moreover, our results suggest that using only one individual disagreement measure may be misleading, particularly during turbulent times. Finally, we study the Friedman–Ball hypothesis. Using the indicator, we show that higher inflation is followed by higher uncertainty. By contrast, we obtain contradictory results for the individual measures. We also document that, after an inflationary shock, uncertainty decreases in the first two months, which is traceable to the energy component in CPI inflation.