Development and Application of Decision Aid for Tactical Control of Battlefield Operations: Bibliographic Sort of the Decision Aiding Literature

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.

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

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/1071181321651191 ◽

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.

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

Mathematics ◽

10.3390/math7030300 ◽

2019 ◽

Vol 7 (3) ◽

pp. 300 ◽

Cited By ~ 9

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.

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

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-191789 ◽

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.

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