Linguistic Trust Research Articles

Distribution Linguistic Trust Propagation and Aggregation Based on Numerical Scale and Archimedean t−norm

Distribution Linguistic Trust Propagation and Aggregation Based on Numerical Scale and Archimedean t−norm

Social network trust relationship environment based advanced ovarian cancer treatment decision-making model: An approach based on linguistic information with experts’ multiple confidence levels

Advanced ovarian cancer is the deadliest gynecologic cancer in World. The survival rate is not significant, and it depends on how well cancer responds to treatment. Generally, the treatment decision depends on Gynecologic oncology experts, which treatment is best for the patient. However, a Gynecologic oncology expert’s limited rationality and personality traits strongly impact a patient’s treatment decision. In addition, according to the American Cancer Society, a patient’s treatment decision may change if Gynecologic oncology experts change. To solve this kind of medical problem, we design a group decision-making model (GDM) based on linguistic preference relations (LPRs) with multiple confidence levels like confidence and doubting levels of experts to analyse experts’ treatment decision opinions digitally for improving the survival rate of patients. In a GDM, consistency analysis and obtaining expert weights are two keys. Hence, this paper design an LPRs with confidence and doubting levels (LPRs-CDLs) based GDM model under a unique consistency improvement algorithm and Pythagorean linguistic trust relations social network analysis driven expert weight determination. First, we define the additive consistency of LPRs-CDLs based on minimum confidence and maximum doubting levels. Second, a consistency reaching process (CRP) algorithm with convergence theorem is offered to improve the consistency of experts’ opinion decisions. Third, develop a trust propagation model based on Pythagorean linguistic trust relations under a social network environment for expert’s weights determination. Then, a social network trust relationship-based GDM model with LPRs-CDLs information is presented to the advanced ovarian cancer patient’s treatment final decision. Finally, we show the rationality of this study with some comparative analysis.

Optimal investment decision of agrivoltaic coupling energy storage project based on distributed linguistic trust and hybrid evaluation method

Photovoltaic (PV) power generation is becoming a good solution to meet energy demand. However, centralized PV production sites may pose a threat to industrial land or agricultural land. To solve the energy-environment-land conflict, agrivoltaic coupling energy storage (AVCES) projects become a feasible new land use model. The investment decision of AVCES project is a multi-criteria decision problem (MCDM). Therefore, this paper firstly constructs an evaluation index system containing four criteria: resources, economy, social, environment and ecology. Secondly, the subjective weights of each indicator are determined based on the distributed linguistic trust model and the cloud model, and the objective weights are determined based on the cloud model and the integrated determination of objective criteria weights (IDOCRIW) model. Subjective and objective weights are combined based on game theory. Finally, the TODIM model is used to get the optimal choice among multiple schemes. After the case study, the applicability and stability of the proposed framework are validated, and the research results will provide good reference values for the investment decision of AVCES projects.

Minimum conflict consensus models for group decision-making based on social network analysis considering non-cooperative behaviors

Decision makers (DMs) may exhibit non-cooperative behaviors such as deviating from recommendations or making only minor modifications in consensus-based group decision-making (GDM) problems. These non-cooperative behaviors will lead to high intra-group conflicts and low consensus efficiency. This paper designs a budget-constrained consensus framework with minimum group conflict based on social network analysis to address non-cooperative behaviors. First, the conflict degrees among DMs are quantified by opinion discrepancies and asymmetric 2-tuple linguistic trust values. Then, DMs’ weights are derived by considering degree centrality and node strength indices in the constructed conflict network. To obtain a feasible budget constraint for the consensus, the non-linear optimization models considering non-cooperative behaviors are proposed. Furthermore, two kinds of minimum conflict consensus models are established to deal with general and completely non-cooperative DMs, respectively. The existence of the optimal solutions of these consensus models and the convergence of the consensus reaching process are proved. Finally, the proposed consensus models are illustrated by an example of the negotiation of the sewage discharge standards. Compared with the consensus models based on minimum cost or adjustment, the proposed consensus models could achieve higher consensus and lower group conflict.

A Minimum Adjustment Cost Consensus Framework Considering Harmony Degrees and Trust Propagation for Social Network Group Decision Making

In consensus-based social network group decision-making (SN-GDM) problems, the low degree of harmony caused by opinion discrepancies and weak trust relationships may lead to high consensus costs and conflict among experts. To address these problems, we design a minimum adjustment cost consensus framework considering harmony degrees in SN-GDM with incomplete 2-tuple linguistic trust. First, considering the decay of trust through its propagation process, a discount-factor-based 2-tuple linguistic trust propagation operator is proposed to estimate the unknown trust relationships among experts. Then, the harmony degrees are measured based on opinion discrepancies and 2-tuple linguistic trust values, and experts’ weights could be obtained. To improve the consensus levels of the inconsistent experts, a minimum adjustment cost consensus model considering harmony degrees is explored to generate interval recommendations. The existence of optimal solutions and the convergence of the proposed consensus model are also proved. Finally, the validity of the proposed consensus framework is verified by the case of base station selection. With the higher overall harmony degree and individual consensus level, we extend the classical minimum cost consensus model and incorporate the harmony degrees among experts into the consensus reaching process.

Реконструкция интерпретационного уровня в структуре языковой личности как участника виртуальной диалогической коммуникации

The authors reconstructed the interpretive level in the model of a linguistic personality as a participant in a virtual dialogue. The authors proceeded from the understanding that the model of a linguistic personality includes the following levels: 1) superficial understanding of the speech product to create a response text, 2) interpretation, 3) modus level, 4) intertextual level. The interpretational level includes interpretation of the perceived initial cue. Interpretation in this case is a mental-linguistic activity, during which the meaning of the original message is appropriated by its addressee and verbalized in the response message. The following interpretative strategies serve here as description units: author- and text-oriented, holistic and elementary, linguistic trust and linguistic skepticism, copyial and creative. The research featured responses of native speakers published on the portal of the Bankfax news agency. The analysis of dialogic utterances was followed by a reconstruction of the interpretive level in the structure of a linguistic personality as a participant in virtual dialogue. The author of the response proved to be an active interpreter with various interpretation strategies.

Social network analysis and consensus reaching process-driven group decision making method with distributed linguistic information

In group decision making with social network analysis (SNA), determining the weights of experts and constructing the consensus-reaching process (CRP) are hot topics. With respect to the generation of weights of experts, this paper firstly develops a distributed linguistic trust propagation operator and a path order weighted averaging (POWA) operator to explore the trust propagation and aggregation between indirectly connected experts, and the weights of experts can be derived by using relative node in-degree centrality in a complete distributed linguistic trust relationship matrix. Then, three levels of consensus are proposed, in which the most inconsistent evaluation information in distributed linguistic trust decision-making matrices can be pinpointed. Subsequently, the distance between experts’ evaluation information and collective evaluation information is designed to be applied as the adjustment cost in CRP. Finally, a novel feedback mechanism supported by the minimum adjustment cost is activated until the group consensus degree reaches the predefined threshold. The novelties of this paper are as follows: (1) the proposed POWA considers the trust value as well as the propagation efficiency of trust path when aggregating the trust relationship in SNA; (2) the consensus reaching mechanism can gradually improve the value of group consensus degree by continuously adjusting the most inconsistent evaluation information.

Consistency and consensus reaching process for group decision making based on complete interval distributed preference relations under social network analysis

Group decision-making (GDM) problems often consist of many indeterminacy factors in realistic situation. How to cope with consistency and consensus under uncertain circumstance are two critical issues in pairwise comparison based GDM problems. In this paper, we firstly propose the model of complete interval distributed preference relation (CIDPR) based on the concept of linguistic distribution with interval symbolic proportions, distribution linguistic preference relation (DLPR) and IDPR. Secondly, the additive consistency index of CIDPR is defined to measure the consistency level of expert's judgment, and an adjustment algorithm is proposed for converting inconsistent CIDPR to an acceptable consistent level. Thirdly, since trust relation is a critical factor in the generation of experts’ weights and the adjustment of experts’ opinions, consensus reaching process (CRP) is designed to take into account distributed linguistic trust relations under social network analysis (SNA). In the proposed adjustment mechanism, non-consensus individual should modify opinion towards his/her trusted and highly weighted expert. The advantage of the proposed inconsistent CIDPR adjustment model can maximally retain the information in the original distribution, while the CRP has a relatively fast convergent speed and good practicality. An illustrative example of strategic new product selection is conducted to demonstrate the applicability of the proposed method and its potential in supporting realistic GDM problems.

An adaptive Grey-Markov model based on parameters Self-optimization with application to passenger flow volume prediction

It has been demonstrated that local prediction approaches show better prediction performance compared with global ones. The paper proposes a novel local prediction model named as the adaptive grey-Markov prediction model for incomplete and complex dynamic systems. This model displays favorable adaptability, flexibility and universality because of four advantages. Firstly, because of the randomness and non-repeatability of time series, the paper proposes adaptive exponential accumulated generating operators to describe different contribution degrees of historical information to future change trends of system characteristics. Secondly, this paper proposes a new paradigm of adaptive grey background value, which can automatically adjust parameters based on historical information to optimize this model and reduce prediction errors. Thirdly, the optimization model based on average absolute percentage error is constructed, and the corresponding adaptive parameters are searched by Ant Lion Optimizer algorithm. Fourthly, this model consists of a quadratic polynomial function describing nonlinearity and a periodic function generated by a Fourier series characterizing noise and period. Thereupon, the adaptive odd-period grey model is proposed. Its residual modified model, named as adaptive grey-Markov modified model, is given based on Markov chain (MC) and virtual linguistic trust degree (VLTD) to further improve prediction accuracy. The modified values of absolute errors are determined by the score functions produced from trust degrees of absolute errors to states on MC calculated by VLTD method. This modified way retains the influence degrees of absolute errors on the modified values and improves the prediction accuracy. Finally, the paper further demonstrates performance and practicability of the proposed prediction model through predicting passenger flow of Chengdu Metro Line1 and comparative analysis.

Vertrauensrelevantes Wissen und Diskurssemantik: eine diskurslinguistische Annäherung an das KonzeptVertrauen

AbstractThis paper investigates the concept of trust at the discourse level. Based on an epistemological discourse approach (Busse 1987), trust is defined as trust-relevant knowledge, which is usually implicit. The paper focuses on the methodology of the analysis of trust at discourse level in the mass media reporting on refugees by identifying implicit indicators of trust constructions (trust dimensions) and their discourse linguistic operationalization through argumentation patterns. The manual annotation of trust dimensions and a combination of hermeneutic and automatic analysis usingMaxqdaplay a central role for the operationalization of the concepttrustfor discourse linguistic analysis. The implementation of the method is demonstrated by analyzing the media construction of the loss and the rebuilding of trust towards refugees (the discursive trust dynamics) in the context of the New Year's Eve sexual assaults in Germany in December 2015/January 2016. In contrast to sociological studies, the present analysis does not show an increased mass media construction of distrust in the face of this discursive event and therefore demonstrates that discourse linguistic trust research contributes to the interdisciplinary research on trust and provides valid findings about the collective knowledge about refugees.

Satisfaction-driven consensus model for social network MCGDM with incomplete information under probabilistic linguistic trust

The advancement of science and technology and the development of network environments have made social network multi-criteria group decision making (SN-MCGDM) an interesting research topic. A satisfaction-driven consensus model that can be applied to incomplete information under probabilistic linguistic trust in SN-MCGDM is presented. First, to model the trust relationships among group experts more flexibly and accurately, the concept of a probabilistic linguistic trust function is defined. Based on this concept, a t-norm-based probabilistic linguistic trust propagation operator and a path-weighted averaging operator are proposed to construct the complete trust relationships among group experts. Then, the incomplete evaluation information in the decision matrix is estimated based on the trust relationships. To identify inconsistent experts, a new consensus measure is provided. To achieve the individual aims as well as to retain the initial opinions of the experts to the greatest extent, identification rules based on satisfaction along with suggestion rules with local modifications are then proposed to help experts reach consensus. Finally, an example followed by comparative analyses is provided to verify the effectiveness of the proposed consensus-reaching model.

A maximum self-esteem degree based feedback mechanism for group consensus reaching with the distributed linguistic trust propagation in social network

This paper focuses on consensus reaching process (CRP) under social network in which the trust relationship expressed by linguistic information. A new feedback mechanism in social network group decision making (SN-GDM) is proposed, which mainly consists of the following two aspects: (1) The propagation of distributed linguistic trust is investigated to study trust relation among experts; (2) A maximum self-esteem degree based feedback mechanism is developed to produce personalized advice for reaching higher group consensus. To do so, a novel linguistic trust propagation method is proposed to obtain the complete trust relationship among group. The self-esteem degree is used to define the extent that an individual makes concessions. Then, a maximum self-esteem degree based optimal feedback mechanism is built to produce personalized advice to help inconsistent experts make change of their opinion. Its novelty lies in the establishment of an optimization model with the nonlinear group self-esteem degree function as the objective function while group consensus threshold as the restrictions. Therefore, the inconsistent experts will reach a group consensus with the minimum loss of self-esteem degree, and then, it achieves the optimal balance between individual self-esteem and group consensus. Finally, a ranking process is applied to derive the appropriate consensus solution.

Virtual linguistic trust degree-based evidential reasoning approach and its application to emergency response assessment of railway station

In real life, the assessment information is usually expressed by virtual linguistic terms, but the trust degrees of different assessment values to the same reference grade are usually ignored. In addition, the provided information is usually incomplete or uncertain, because the objective things’ characteristics are elusive and the decision maker's knowledge is limited. To solve such problems, in this paper, we first propose the virtual linguistic trust degree. Then, in view of the nonlinear changes of the decision maker's psychological states, we depict the virtual linguistic trust degree by a nonlinear function, and present some relevant aggregation operators. In order to avoid the loss of information, we propose a novel evidential reasoning approach that combines the virtual linguistic trust degree with basic unit-interval monotonic function. In this approach, the normalized basic probability mass can be obtained by the continuous basic unit-interval monotonic function, which satisfies the consensus axiom of original evidential reasoning. Meanwhile, we propose the normalized factor on the basis of the rule that the remaining unassigned probability mass is assigned into any subsets, and then present the whole framework of an extended evidential reasoning algorithm. Furthermore, a numerical example about the emergency response assessment of railway station is conducted to show the usage of the algorithm. Finally, the validity of this algorithm is demonstrated by the comparative analysis.

A trust propagation and collaborative filtering based method for incomplete information in social network group decision making with type-2 linguistic trust

Abstract A theoretical framework to deal with incomplete preference information in social network group decision making (SN-GDM) is put forward. Firstly, the concept of the type-2 linguistic trust function is defined to model direct trust relationship of group experts in SN-GDM. It adopts the bilattice structure with of trust/distrust values that are all described by distributed linguistic function. Secondly, the type-2 uninorm trust propagation operator is investigated to connect the indirect trust relationship between group experts. The path-ordering induced order weighted averaging (P-IOWA) operator is proposed to aggregate to multiple trust propagation paths into a collective one. Then, the completed trust relationship between group experts is constructed to estimate the incomplete preference information in decision matrix. Combining the social network trust with the collaborative filtering, a comprehensive estimation method for incomplete information is proposed. In detail, the estimation method based the social network trust uses the actual reputation between experts as ‘historic’ actions. While the estimation method by the collaborative filtering utilizes the similar degree of decision as ‘current’ information. Then, the proposed comprehensive estimation method resembles the Bayesian approach, and consequently it can enhance the reliability for the evaluation of the incomplete elements in GDM. Hence, a consensus reaching process for GDM with incomplete preference information is proposed. Finally, an example is given to illustrate the use of the proposed method.

A minimum adjustment cost feedback mechanism based consensus model for group decision making under social network with distributed linguistic trust

A theoretical feedback mechanism framework to model consensus in social network group decision making (SN-GDM) is proposed with following two main components: (1) the modelling of trust relationship with linguistic information; and (2) the minimum adjustment cost feedback mechanism. To do so, a distributed linguistic trust decision making space is defined, which includes the novel concepts of distributed linguistic trust functions, expectation degree, uncertainty degrees and ranking method. Then, a social network analysis (SNA) methodology is developed to represent and model trust relationship between a networked group, and the trust in-degree centrality indexes are calculated to assign an importance degree to the associated user. To identify the inconsistent users, three levels of consensus degree with distributed linguistic trust functions are calculated. Then, a novel feedback mechanism is activated to generate recommendation advices for the inconsistent users to increase the group consensus degree. Its novelty is that it produces the boundary feedback parameter based on the minimum adjustment cost optimisation model. Therefore, the inconsistent users are able to reach the threshold value of group consensus incurring a minimum modification of their opinions or adjustment cost, which provides the optimum balance between group consensus and individual independence. Finally, after consensus has been achieved, a ranking order relation for distributed linguistic trust functions is constructed to select the most appropriate alternative of consensus.