An optimal feedback model to prevent manipulation behaviours in consensus under social network group decision making

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.A novel framework to prevent manipulation behaviour in consensus reaching process under social network group decision making is proposed, which is based on a theoretically sound optimal feedback model. The manipulation behaviour classification is twofold: (1) ‘individual manipulation’ where each expert manipulates his/her own behaviour to achieve higher importance degree (weight); and (2) ‘group manipulation’ where a group of experts force inconsistent experts to adopt specific recommendation advices obtained via the use of fixed feedback parameter. To counteract ‘individual manipulation’, a behavioural weights assignment method modelling sequential attitude ranging from ‘dictatorship’ to ‘democracy’ is developed, and then a reasonable policy for group minimum adjustment cost is established to assign appropriate weights to experts. To prevent ‘group manipulation’, an optimal feedback model with objective function the individual adjustments cost and constraints related to the threshold of group consensus is investigated. This approach allows the inconsistent experts to balance group consensus and adjustment cost, which enhances their willingness to adopt the recommendation advices and consequently the group reaching consensus on the decision making problem at hand. A numerical example is presented to illustrate and verify the proposed optimal feedback model

GIS-Based Local Ordered Weighted Averaging: A Case Study in London, Ontario

GIS-based multicriteria analysis is a procedure for combining a set of criterion maps and associated criterion weights to obtain overall value for each spatial unit (location) in the study area. Ordered Weighted Averaging (OWA) is a generic algorithm of the multicriteria analysis. It has been integrated into GIS and applied for tackling a wide range of spatial problems. However, the conventional OWA method is based on an assumption of spatial homogeneity of its parameters. Therefore, it is referred to as a global model. This thesis proposes a local form OWA. The local model is based on the range sensitivity principle. A case study of examining spatial patterns of socioeconomic status in London, Ontario is presented. The results show that there are substantial differences between the spatial patterns generated by the global and local OWA methods

A Behavioral Analysis of WOWA and SUOWA Operators

Producción CientíficaWeighted ordered weighted averaging (WOWA) and semiuninorm-based ordered weighted averaging (SUOWA) operators are two families of aggregation functions that simultaneously generalize weighted means and OWA operators. Both families can be obtained by using the Choquet integral with respect to normalized capacities. Therefore, they are continuous, monotonic, idempotent, compensative, and homogeneous of degree 1 functions. Although both families fulfill good properties, there are situations where their behavior is quite different. The aim of this paper is to analyze both families of functions regarding some simple cases of weighting vectors, the capacities from which they are building, the weights affecting the components of each vector, and the values they return.Ministerio de Economía, Industria y Competitividad (ECO2012-32178)Junta de Castilla y León (programa de apoyo a proyectos de investigación – Ref. VA066U13

Trust Based Consensus Model for Social Network in an Incomplete Linguistic Information Context

A theoretical framework to consensus building within a networked social group is put forward. This article investigates a trust based estimation and aggregation methods as part of a visual consensus model for multiple criteria group decision making with incomplete linguistic information. A novel trust propagation method is proposed to derive trust relationship from an incomplete connected trust network and the trust score induced order weighted averaging operator is presented to aggregate the orthopairs of trust/distrust values obtained from different trust paths. Then, the concept of relative trust score is defined, whose use is twofold: (1) to estimate the unknown preference values and (2) as a reliable source to determine experts' weights. A visual feedback process is developed to provide experts with graphical representations of their consensus status within the group as well as to identify the alternatives and preference values that should be reconsidered for changing in the subsequent consensus round. The feedback process also includes a recommendation mechanism to provide advice to those experts that are identified as contributing less to consensus on how to change their identified preference values. It is proved that the implementation of the visual feedback mechanism guarantees the convergence of the consensus reaching process

Construction of interval-valued fuzzy preference relations from ignorance functions and fuzzy preference relations. Application to decision making

The file attached is this record is the authors pre-print. The publishers version of record can be found by following the DOI link

Una combinación basada en operadores OWA para la Clasificación de Género Multi-etiqueta de páginas web

This paper presents a new method for genre identification that combines homogeneous classifiers using OWA (Ordered Weighted Averaging) operators. Our method uses character n-grams extracted from different information sources such as URL, title, headings and anchors. To deal with the complexity of web pages, we applied MLKNN as a multi-label classifier, in which a web page can be affected by more than one genre. Experiments conducted using a known multi-label corpus show that our method achieves good results.En este trabajo se presenta un nuevo método para la identificación de género que combina clasificadores homogéneos utilizando OWA (promedio ponderado) Pedimos operadores. Nuestro método utiliza caracteres n-gramas extraídos de diferentes fuentes de información, tales como URL, título, encabezados y anclajes. Para hacer frente a la complejidad de las páginas web, se aplicó MLKNN como un clasificador multi-etiqueta, en el que una página web puede verse afectada por más de un género. Los experimentos llevados a cabo usando un conocido corpus multi-etiqueta muestran que nuestro método logra buenos resultados

Aggregating sentiment in Europe: the relationship with volatility and returns

This paper presents several proposals for creating an aggregate sentiment index for the European stock market. We achieve this objective by using the OWA and WOWA operators, which have been successful in finance and have a strong financial interpretation. We compute ten different aggregate sentiment indices for the 2007-2021 period and evaluate their ability to provide information about current and future market volatility and returns. We find several results of interest for both investors and policymakers. Sentiment indices have a strong negative relationship with market volatility. Extreme values of sentiment can predict future market returns, with low values indicating positive returns and high values suggesting negative returns. Finally, using stock market capitalisation as an input of the WOWA operator enhances explanatory power of the indices on future market returns compared to the OWA operator

Attitude Quantifier Based Possibility Distribution Generation Method for Hesitant Fuzzy Linguistic Group Decision Making

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The possibility distribution-based approach is one of the powerful tools available to manage hesitant fuzzy linguistic term set (HFLTS) information. However, existing possibility distribution studies have not considered the experts’ satisfied preference for HFLTSs in the process of generating the possibility distribution. This paper aims at filling this research gap. To achieve this goal, a novel possibility distribution generation method based on the concept of linguistic quantifier is proposed. This is accomplished by defining a new attitude linguistic quantifier, which is supported with theoretical results to analyze the relationship between the proposed attitude linguistic quantifier with the original linguistic quantifier, attitude indices and the expected linguistic term. The new possibility distribution generation method is proved to be (1) more general than the two main existing approaches, which are particular cases for specific linguistic quantifiers; and (2) useful to implement the concept of soft majority in the resolution process of the decision making situation. Additionally, a new two stages feedback mechanism of attitude adjustment and assessment adjustment is devised to guarantee the convergence of the consensus reaching process. Finally, a framework of group decision making with HFLTSs information is presented and an illustrative example is conducted to verify the proposed method

Fuzzy Group Decision Making for Influence-Aware Recommendations

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Group Recommender Systems are special kinds of Recommender Systems aimed at suggesting items to groups rather than individuals taking into account, at the same time, the preferences of all (or the majority of) members. Most existing models build recommendations for a group by aggregating the preferences for their members without taking into account social aspects like user personality and interpersonal trust, which are capable of affecting the item selection process during interactions. To consider such important factors, we propose in this paper a novel approach to group recommendations based on fuzzy influence-aware models for Group Decision Making. The proposed model calculates the influence strength between group members from the available information on their interpersonal trust and personality traits (possibly estimated from social networks). The estimated influence network is then used to complete and evolve the preferences of group members, initially calculated with standard recommendation algorithms, toward a shared set of group recommendations, simulating in this way the effects of influence on opinion change during social interactions. The proposed model has been experimented and compared with related works