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Teranga Go!: Carpooling Collaborative Consumption Community with multi-criteria hesitant fuzzy linguistic term set opinions to build confidence and trust

Rosana Montes, Ana M. Sanchez, Pedro Villar, Francisco Herrera

TL;DR

An intelligent decision support system in the platform based on computing with words is implemented to help creating values of confidence, trust and safety among the members of the Teranga Go! community.

Abstract

Classic Delphi and Fuzzy Delphi methods are used to test content validity of a data collection tools such as questionnaires. Fuzzy Delphi takes the opinion issued by judges from a linguistic perspective reducing ambiguity in opinions by using fuzzy numbers. We propose an extension named 2-Tuple Fuzzy Linguistic Delphi method to deal with scenarios in which judges show different expertise degrees by using fuzzy multigranular semantics of the linguistic terms and to obtain intermediate and final results expressed by 2-tuple linguistic values. The key idea of our proposal is to validate the full questionnaire by means of the evaluation of its parts, defining the validity of each item as a Decision Making problem. Taking the opinion of experts, we measure the degree of consensus, the degree of consistency, and the linguistic score of each item, in order to detect those items that affect, positively or negatively, the quality of the instrument. Considering the real need to evaluate a b-learning educational experience with a consensual questionnaire, we present a Decision Making model for questionnaire validation that solve it. Additionally, we contribute to this consensus reaching problem by developing an online tool under GPL v3 license. The software visualizes the collective valuations for each iteration and assists to determine which parts of the questionnaire should be modified to reach a consensual solution.

Teranga Go!: Carpooling Collaborative Consumption Community with multi-criteria hesitant fuzzy linguistic term set opinions to build confidence and trust

TL;DR

An intelligent decision support system in the platform based on computing with words is implemented to help creating values of confidence, trust and safety among the members of the Teranga Go! community.

Abstract

Classic Delphi and Fuzzy Delphi methods are used to test content validity of a data collection tools such as questionnaires. Fuzzy Delphi takes the opinion issued by judges from a linguistic perspective reducing ambiguity in opinions by using fuzzy numbers. We propose an extension named 2-Tuple Fuzzy Linguistic Delphi method to deal with scenarios in which judges show different expertise degrees by using fuzzy multigranular semantics of the linguistic terms and to obtain intermediate and final results expressed by 2-tuple linguistic values. The key idea of our proposal is to validate the full questionnaire by means of the evaluation of its parts, defining the validity of each item as a Decision Making problem. Taking the opinion of experts, we measure the degree of consensus, the degree of consistency, and the linguistic score of each item, in order to detect those items that affect, positively or negatively, the quality of the instrument. Considering the real need to evaluate a b-learning educational experience with a consensual questionnaire, we present a Decision Making model for questionnaire validation that solve it. Additionally, we contribute to this consensus reaching problem by developing an online tool under GPL v3 license. The software visualizes the collective valuations for each iteration and assists to determine which parts of the questionnaire should be modified to reach a consensual solution.
Paper Structure (24 sections, 2 theorems, 17 equations, 6 figures, 10 tables)

This paper contains 24 sections, 2 theorems, 17 equations, 6 figures, 10 tables.

Table of Contents

  1. Introduction
  2. The design of a quesionnaire for b-learning
  3. Properties of a valid questionnaire
  4. Methodology and research question
  5. Experience description
  6. Questionnaire definition
  7. A 2-Tuple Fuzzy Linguistic Delphi method
  8. The Delphi method
  9. The linguistic representation model
  10. The 2-Tuple linguistic computational model
  11. Multi-granular linguistic information
  12. Stages of application of the 2-Tuple Fuzzy Linguistic Delphi method
  13. The MultiExpert-MultiCriteria Linguistic DM problem
  14. Validation by Consensus
  15. A web tool to apply the 2-Tuple Fuzzy Linguistic Delphi method
  16. ...and 9 more sections

Key Result

Proposition 1

Let $(s_i,\alpha)$ be a 2-tuple linguistic value. There is a function $\varDelta$ that translates a 2-tuple into a number $\beta \in [0,g]$:

Figures (6)

  • Figure 1: The value of $\alpha$ represents the translation of the membership function to the nearest term.
  • Figure 2: Unification step translates linguistic values in $S^3$, $S^5$ or $S^7$ to level $t^*$, which in this case is $S^{13}$.
  • Figure 3: The proposed 2TFLD method solves several MEMCLDM problems that are repeated trough iterations till a consensus level is reached for each item.
  • Figure 4: The solution of a MEMCLDM through successive phases is the qualification of a item of the questionnaire.
  • Figure 5: We can import separately the description of the questionnaire and the assessments of the expert panel for each round.
  • ...and 1 more figures

Theorems & Definitions (7)

  • Definition 1
  • Proposition 1
  • Proposition 2
  • Example 1
  • Definition 2
  • Definition 3
  • Example 2