Multicriteria correlation preference information (MCCPI) with nonadditivity index for decision aiding

2020 ◽  
Vol 39 (3) ◽  
pp. 3441-3452
Author(s):  
Li Huang ◽  
Jian-Zhang Wu ◽  
Gleb Beliakov
Keyword(s):  
Decision Aid ◽  
Pairwise Comparison ◽  
Random Generation ◽  
Decision Criteria ◽  
Preference Information ◽  
Decision Aiding ◽  
Dominance Relationships ◽  
Decision Scheme ◽  
Internal Interaction ◽  
Decision Alternatives

MCCPI (Multiple Criteria Correlation Preference Information) is a kind of 2 dimensional decision preference information obtained by pairwise comparison on the importance and interaction of decision criteria. In this paper, we introduce the nonadditivity index to replace the Shapley simultaneous interaction index and construct an undated MCCPI based decision scheme. We firstly propose a diagram to help decision maker obtain the nonadditivity index type MCCPI, then establish transform equations to normalize them into desired capacity and finally adopt a random generation MCCPI based comprehensive decision aid algorithm to explore the dominance relationships and creditable ranking orders of all decision alternatives. An illustrative example is also given to demonstrate the feasibility and effectiveness of the proposed decision scheme. It’s shown that based on some good properties of nonadditivity index in practice, the updated MCCPI model can deal with the internal interaction among decision criteria with relatively less model construction and calculation effort.

scholarly journals Multicriteria Correlation Preference Information (MCCPI)-Based Ordinary Capacity Identification Method

Mathematics ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 300 ◽  
Author(s):  
Jian-Zhang Wu ◽  
Yi-Ping Zhou ◽  
Li Huang ◽  
Jun-Jie Dong
Keyword(s):  
Decision Making ◽  
Decision Aid ◽  
Higher Order ◽  
Decision Criteria ◽  
Preference Information ◽  
Decision Aiding ◽  
Identification Method ◽  
Explicit Information ◽  
Dominance Relationships ◽  
Decision Alternatives

Multicriteria correlation preference information (MCCPI) refers to a special type of 2-dimensional explicit information: the importance and interaction preferences regarding multiple dependent decision criteria. A few identification models have been established and implemented to transform the MCCPI into the most satisfactory 2-additive capacity. However, as one of the most commonly accepted particular type of capacity, 2-additive capacity only takes into account 2-order interactions and ignores the higher order interactions, which is not always reasonable in a real decision-making environment. In this paper, we generalize those identification models into ordinary capacity cases to freely represent the complicated situations of higher order interactions among multiple decision criteria. Furthermore, a MCCPI-based comprehensive decision aid algorithm is proposed to represent various kinds of dominance relationships of all decision alternatives as well as other useful decision aiding information. An illustrative example is adopted to show the proposed MCCPI-based capacity identification method and decision aid algorithm.

Nonadditivity Index Based Quasi-Random Generation of Capacities and Its Application in Comprehensive Decision Aiding

Mathematics ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 301
Author(s):  
Li Huang ◽  
Jian-Zhang Wu ◽  
Rui-Jie Xi
Keyword(s):  
Generation Process ◽  
Random Generation ◽  
Decision Criteria ◽  
Interaction Index ◽  
Decision Aiding ◽  
Satisfactory Performance ◽  
Complex Constraints ◽  
Interaction Phenomenon

The capacity is a powerful tool with exponential coefficients to represent the interaction phenomenon among decision criteria, but its random generation becomes a tough issue for dealing with the monotonicity with all inclusion subsets as well as the complex constraints of decision preference. In this paper, we adopt a kind of explicit interaction index, the nonadditivity index, to construct two types of quasi-random generation methods of capacity under a given decision interaction preference. Compared to the existing random generation algorithms, the methods have relatively satisfactory performance on the statistics characteristic of generated capacities but need rather less calculation effort on the generation process. We also show the effectiveness of proposed quasi-random generation methods by an illustrative decision example.

scholarly journals An Approach for Generating Weights Using the Pairwise Comparison Matrix

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Zaher Sepehrian ◽  
Sahar Khoshfetrat ◽  
Said Ebadi
Keyword(s):  
Pairwise Comparison ◽  
Decision Criterion ◽  
Data Envelopment ◽  
Decision Criteria ◽  
Analytic Hierarchy ◽  
Pairwise Comparison Matrix ◽  
Proposed Model ◽  
Assurance Region ◽  
Potential Applications ◽  
Hierarchy Process

Data envelopment analysis (DEA) has been used for obtaining weights for the analytic hierarchy process (AHP), an approach known as DEAHP. This method sometimes identifies more than one decision criterion or alternative as DEAHP-efficient. To overcome this problem, this paper proposes a new approach that not only generates appropriate weights for the decision criteria or alternatives, but also differentiates between DEAHP-efficient decision criteria or alternatives. To this end, we propose a DEA model with an assurance region and a cross-weight model that prioritizes decision criteria or alternatives by considering their most unfavorable weights. Two numerical examples are also provided to illustrate the advantages and potential applications of the proposed model.

scholarly journals Towards Agricultural Water Management Decisions in the Context of WELF Nexus

Proceedings ◽  
2018 ◽  
Vol 2 (11) ◽  
pp. 613
Author(s):  
Alexandros Psomas ◽  
Isaak Vryzidis ◽  
Athanasios Spyridakos ◽  
Maria Mimikou
Keyword(s):  
Water Management ◽  
Utility Theory ◽  
Decision Aid ◽  
Decision Problems ◽  
Agricultural Water ◽  
Agricultural Water Management ◽  
Decision Aiding ◽  
Prioritization Method ◽  
Multi Attribute Utility Theory ◽  
Points Of View

A Multi-criteria Decision Aid (MCDA) framework based on the combination of Multi-Attribute Utility Theory (MAVT/MAUT) with the Weights Assessment through Prioritization method (WAP) is proposed for decision problems related to agricultural water management in the context of water-energy-land-food (WELF) nexus. The implementation of the framework supports a Decision Maker (DM) to quantify his/her preferences in a structured and rational way, in order to select the best alternative for agricultural water management. Through the use of the Multicriteria Interactive Intelligence Decision Aiding System (MIIDAS), marginal utilities functions for all the criteria are constructed. The criteria are grouped in points of view, which may refer to individual nexus elements and costs for investments or agricultural inputs. The WAP software assists the DM to assess the relative importance of the criteria and estimate their weights.

Effect of Procedural Elements on Trust and Compliance with anImperfect Decision Aid

2021 ◽  
Vol 65 (1) ◽  
pp. 633-637
Author(s):  
Rebecca L. Pharmer ◽  
Christopher D. Wickens ◽  
Benjamin A. Clegg ◽  
C.A.P Smith
Keyword(s):  
Collision Avoidance ◽  
Decision Aid ◽  
Automated System ◽  
Avoidance Task ◽  
The Road ◽  
Decision Aiding ◽  
The Relationship

We sought to establish to what extent incorporating a dichotomized procedural variable (in this case, maritime ‘rules of the road’) and incentives into a decision aiding algorithm would change a previously found non-compliance bias when the algorithm contradicted the known procedure. We also sought to examine the relationship between trust in and dependence on an automated system. An experiment was conducted using a simple, simulated maritime collision avoidance task featuring an imperfect, but highly reliable (87%), decision aid. Adding the dichotomous procedural variable into the algorithms recommendations increased compliance with the system, even for recommendations that violated learned procedures. Performance was still not perfectly calibrated to the actual reliability of the system (underreliance and under-trust). Results also revealed the dissociation between rated trust in, and behavioral dependence on decision aiding automation.

scholarly journals A new approach for criteria weight elicitation of the ARAS-H method

2021 ◽  
Vol 16 ◽  
pp. 89-109
Author(s):  
Maroua Ghram ◽  
◽  
Hela Moalla Frikha ◽  
Keyword(s):  
Mathematical Programming ◽  
Decision Making Process ◽  
Linear Programs ◽  
Programming Technique ◽  
New Approach ◽  
Preference Information ◽  
Decision Aiding ◽  
Criteria Weights ◽  
Preference Disaggregation ◽  
Elicitation Procedure

Criteria weight inference is a crucial step for most of multi-criteria methods. However, criteria weights are often determined directly by the decision-maker (DM) which makes the results unreliable. Therefore, to overcome the imprecise weighting, we suggest the use of the preference programming technique. Instead of obtaining criteria weights directly from the DM, we infer them in a more objective manner to avoid the subjectivity and the unreliability of the results. Our aim is to elicit the ARAS-H criteria weights at each level of the hierarchy tree via mathematical programming, taking into account the DM’s preferences. To put it differently, starting from preference information provided by the DM, we proceed to model our constraints. The ARAS-H method is an extension of the classical ARAS method for the case of hierarchically structured criteria. We adopt a bottom-up approach in order to elicit ARAS-H criteria weights, that is, we start by determining the elementary criteria weights (i.e. the criteria at the lowest level of the hierarchy tree). The solution of the linear programs is obtained using LINGO software. The main contribution of our criteria weight elicitation procedure is in overcoming imprecise weighting without excluding the DM from the decision making process. Keywords: Multiple Criteria Decision Aiding, preference disaggregation, ARAS-H, criteria weights, mathematical programming.

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