687 research outputs found
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
A Mix Model of Discounted Cash-Flow and OWA Operators for Strategic Valuation
The stock market volatility and the actual stock Exchange activity have increased the need of counting with effective methods on the part of financial analysts to achieve a division in relation to the investment actions, being also growing the demand of methodological instruments that reduce and minimize the risks and uncertainty when valuating financial actives and companies. These systems not only must use quantitative information but the inclusion of qualitative information must also bear heavily on them, as an improvement element in the adjustment of these valuating methods, with the aim of throwing a more well-conceived or less mistaken decision. In this work, the use of Discounted Cash-Flow model is proposed, with quantitative information together with the OWA operators as an inclusion method of ualitative information in the traditional valuating models, with the aim of generating an strategic valuating system which allows to develop more agreed and less mistaken valuations
OWA-based aggregation operations in multi-expert MCDM model
This paper presents an analysis of multi-expert multi-criteria decision making (ME-MCDM) model based on the ordered weighted averaging (OWA) operators. Two methods of modeling the majority opinion are studied as to aggregate the experts' judgments, in which based on the induced OWA operators. Then, an overview of OWA with the inclusion of different degrees of importance is provided for aggregating the criteria. An alternative OWA operator with a new weighting method is proposed which termed as alternative OWAWA (AOWAWA) operator. Some extensions of ME-MCDM model with respect to two-stage aggregation processes are developed based on the classical and alternative schemes. A comparison of results of different decision schemes then is conducted. Moreover, with respect to the alternative scheme, a further comparison is given for different techniques in integrating the degrees of importance. A numerical example in the selection of investment strategy is used as to exemplify the model and for the analysis purpose
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
A Linguistic Multi-Criteria Decision Making Model Applied to the Integration of Education Questionnaires
We present a model made up of linguistic multi-criteria decision making processes to integrate the answers to
heterogeneous questionnaires, based on a five-point Likert scale, into a unique form rooted in the widespread
course experience questionnaire. The main advantage of having the resulting integrated questionnaire is that it can
be incorporated into other course experience questionnaire surveys to make benchmarking among organizations.
This model has been applied to integrate heterogeneous educational questionnaires at the University of Granada.European Union (EU)
TIN2010-17876Andalusian Excellence Projects
TIC-05299
TIC-599
Majority multiplicative ordered weighting geometric operators and their use in the aggregation of multiplicative preference relations
In this paper, we introduced the majority multiplicative ordered weighted
geometric (MM-OWG) operator and its properties. This is a general type of
the aggregate dependent weights which we have applied in geometric environment.
The MM-OWG operator is based on the OWG operators and on the
majority operators. We provide the MM-OWG operators to aggregate in a
multiplicative environment, i.e. when it’s necessary to aggregate information
given on a ratio scale. Therefore, it allows us to incorporate the concept of
majority in problems where the information is provided using a ratio scale.
Its properties are studied and an application for multicriteria decision making
problems with multiplicative preference relations is presented
Linguistic Consensus Models Based on a Fuzzy Ontology
The main purpose of a Group Decision Making model is to reach a consensual solution as quickly as possible by decreasing the gap between the perceptions of different decision makers. The perception of the decision makers depends on the various relations between alternatives and attributes. As a real life example, one can mention the present problem of the euro crisis: before finding a solution for the situation, the different perceptions of each country have to be attuned to have a common ground for negotiations. We have to cope with two different issues when modeling a Group Decision Making problem: (1) the relations describing alternatives and attributes are known only partially in most of the cases and (2) these relations change dynamically. Fuzzy ontologies can provide a solution to handle both issues in an efficient way: we can model incomplete and uncertain information using the well-established theory of fuzzy logic and we can dynamically model the changes in the structure by employing ontologies. Therefore, we propose a new linguistic extension of a consensus model to deal with the psychology of negotiation by using the power of a fuzzy ontology as weapon of influence in order to improve group decision scenarios making them more precise and realistic.European Union (EU)FUZZYLING-II
TIN2010-17876Andalusian Excellence Projects
TIC-05299
TIC-5991Finnish Funding Agency for Technology & Innovation (TEKES)
40039/1
Fudge: Fuzzy ontology building with consensuated fuzzy datatypes
An important problem in Fuzzy OWL 2 ontology building is the definition of fuzzy membership functions for real-valued fuzzy sets (so-called fuzzy datatypes in Fuzzy OWL 2 terminology). In this paper, we present a tool, called Fudge, whose aim is to support the consensual creation of fuzzy datatypes by aggregating the specifications given by a group of experts. Fudge is freeware and currently supports several linguistic aggregation strategies, including the convex combination, linguistic OWA, weighted mean and fuzzy OWA, and easily allows to build others in. We also propose and have implemented two novel linguistic aggregation operators, based on a left recursive form of the convex combination and of the linguistic OWA
Dynamic adaptation of user profiles in recommender systems
In a period of time in which the content available through the Internet
increases exponentially and is more easily accessible every day, techniques
for aiding the selection and extraction of important and personalised
information are of vital importance. Recommender Systems (RS) appear as
a tool to help the user in a decision making process by evaluating a set of
objects or alternatives and aiding the user at choosing which one/s of them
suits better his/her interests or preferences. Those preferences need to be
accurate enough to produce adequate recommendations and should be
updated if the user changes his/her likes or if they are incorrect or
incomplete. In this work an adequate model for managing user preferences
in a multi-attribute (numerical and categorical) environment is presented to
aid at providing recommendations in those kinds of contexts. The
evaluation process of the recommender system designed is supported by a
new aggregation operator (Unbalanced LOWA) that enables the
combination of the information that defines an alternative into a single
value, which then is used to rank the whole set of alternatives. After the
recommendation has been made, learning processes have been designed to
evaluate the user interaction with the system to find out, in a dynamic and
unsupervised way, if the user profile in which the recommendation process
relies on needs to be updated with new preferences. The work detailed in
this document also includes extensive evaluation and testing of all the
elements that take part in the recommendation and learning processes
- …