DEX (Decision EXpert): A Qualitative Hierarchical Multi-criteria Method

Bohanec, Marko

doi:10.1007/978-981-16-7414-3_3

Cited by 23 publications

(21 citation statements)

References 66 publications

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“…The DEX method is a qualitative multi-criteria modeling method [62,63]. Its main feature is that it uses symbolic attributes instead of numeric ones.…”

Section: Dex: a Qualitative Mcdm Methodsmentioning

confidence: 99%

Integrating Multi-Criteria Decision Models in Smart Urban Planning: A Case Study of Architectural and Urban Design Competitions

Berčič,

Bohanec,

Ažman Momirski

2024

Smart Cities

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The focus of this study is to integrate the DEX (Decision EXpert) decision-modeling method in architectural and urban design (A & UD) competitions. This study aims to assess the effectiveness of integrating the DEX (Decision EXpert) decision-modeling method into the evaluation process of A & UD competitions to enhance decision-making transparency, objectivity, and efficiency. By using symbolic values in decision models, the approach offers a more user-friendly alternative to the conventional jury decision-making process. The practical application of the DEX method is demonstrated in the Rhinoceros 3D environment to show its effectiveness in evaluating A & UD competition project solutions related to the development of the smart city. The results indicate that the DEX method, with its hierarchical and symbolic values, significantly improves the simplicity of the evaluation process in A & UD competitions, aligning it with the objectives of the smart cities. This method provides an efficient, accessible, and viable alternative to other multi-criteria decision-making approaches. This study importantly contributes to the field of architectural decision making by merging qualitative multi-criteria decision models into the CAD environment, thus supporting more informed, objective, and transparent decision-making processes in the planning and development of smart cities.

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“…The DEX method is a qualitative multi-criteria modeling method [62,63]. Its main feature is that it uses symbolic attributes instead of numeric ones.…”

Section: Dex: a Qualitative Mcdm Methodsmentioning

confidence: 99%

Integrating Multi-Criteria Decision Models in Smart Urban Planning: A Case Study of Architectural and Urban Design Competitions

Berčič,

Bohanec,

Ažman Momirski

2024

Smart Cities

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“…DEX is a qualitative, hierarchical, and rule‐based multicriteria decision‐making (MCDM) method (Bohanec, 2022). MCDM provides a general approach and a multitude of methods that allow the decision‐maker to formulate their objectives in terms of multiple, possibly conflicting, criteria and represent them in the form of a multicriteria model.…”

Section: Methodsmentioning

confidence: 99%

“…Such models can help rescue service to identify skiers with risky behavior and recommend a set of actions to prevent accidents. There are also interesting applications of data mining and machine learning for ski injury prediction and ski data analysis in , Delibašić et al (2018a, 2020, 2022), and Kubany et al (2020. In the aforementioned papers, one can find that even simple machine learning algorithms are able to perform better compared to the human decision-maker.…”

Section: Related Workmentioning

confidence: 99%

Extracting decision models for ski injury prediction from data

Radovanović

Bohanec

Delibašić

2023

Int Trans Operational Res

Self Cite

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Creating decision models for risk assessment of ski injuries is a challenging task. Ski injuries are rare events, but they carry a high cost, that is, can cause working or movement disabilities. Usually, ski risk assessment is performed on small‐scale, case‐controlled studies where the effect of a single factor is evaluated. Recently, data mining and machine learning algorithms are being employed for ski risk assessment and injury prediction. However, these models do not generally satisfy the need for interpretation of the decision model, do not provide explanations for the predictions, and in general do not ensure the completeness and consistency of decision rules. To make data mining and machine learning models useful, one needs to implement the aforementioned properties. Decision support systems are expected to have these properties; however, the process of building such decision support systems is still tedious: it has to consider human biases, assumptions, and subjective values, as well as focus on the decision problem being solved. We propose a method for extraction of decision models from data at hand. Our method DIDEX, Data Induced DEcision eXpert, builds models that have desirable properties for inclusion in decision support systems. The proposed method is used to build a decision model for ski injury prediction based on data from Mt. Kopaonik ski resort, Serbia. The results show that DIDEX generates up to a five times simpler model compared to the existing domain expert DEX models while having a 6% better predictive accuracy. Additionally, its predictive accuracy is comparable to similar machine learning algorithms, such as decision tree classifiers, random forest, and logistic regression.

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“…DEX (Bohanec, 2023; Bohanec & Rajkovič, 1990; https://repo.ijs.si/markobohanec/dexilibrary) is an MCDA method that primarily uses totally ordered qualitative criteria, and requires no numerical judgements; Bohanec draws comparison to VDA without describing DEX as a form of VDA. DEXi, the latest implementation, is free software, but is written in Oxygene, a proprietary dialect of Pascal.…”

Section: Design and Implementationmentioning

confidence: 99%

Artiruno: A free‐software tool for multi‐criteria decision‐making with verbal decision analysis

Arfer

2024

Multi Criteria Decision Anal

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Verbal decision analysis (VDA) is a family of methods for multi‐criteria decision analysis that require no numerical judgements from the agent. Although many such methods have been developed, they share the potential issue of asking the agent many more questions than necessary, particularly under multilevel approaches. Furthermore, whether VDA improves decisions, compared to no intervention, has yet to be investigated empirically. I introduce a new VDA method, Artiruno, with a freely licensed implementation in Python. Artiruno makes inferences mid‐interview so as to require minimal input from the agent, while using a multilevel scheme that allows it to ask complex questions when necessary. Inferences are facilitated by an axiom allowing comparisons to be partitioned across groups of criteria. Artiruno's performance in a variety of simple and complex scenarios can be verified with automated software tests. For an empirical test, I conducted an experiment in which 107 people from an Internet subject pool considered an important decision they faced in their own lives, and were randomly assigned to use Artiruno or to receive no intervention. These subjects proved mostly able to use Artiruno, and they found it helpful, but Artiruno seemed to have little influence on their decisions or outcomes.

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DEX (Decision EXpert): A Qualitative Hierarchical Multi-criteria Method

Cited by 23 publications

References 66 publications

Integrating Multi-Criteria Decision Models in Smart Urban Planning: A Case Study of Architectural and Urban Design Competitions

Integrating Multi-Criteria Decision Models in Smart Urban Planning: A Case Study of Architectural and Urban Design Competitions

Extracting decision models for ski injury prediction from data

Artiruno: A free‐software tool for multi‐criteria decision‐making with verbal decision analysis

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