Neutrosophic multicriteria method for the evaluation of the admissibility of interim measures in arbitration proceedings

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Alex Santiago Páez Maldonado
Klever Israel Llerena Villares
Leonardo Mario Falconi Cardenas
Fernando Daniel Lalaleo Camino

Abstract

Abstract. Arbitration is one of the most effective tools for resolving commercial disputes, but its success depends on the timely execution of arbitration decisions without depending on ordinary judicial procedures, which can lead to violations of rights. Given this need, it is crucial to establish preventive measures that promote justice in both the arbitration and judicial spheres. Although arbitration allows the adoption of precautionary measures to protect rights and guarantee legal certainty, this process presents particularities that require attention in the evaluation of requests for these measures, since they differ from those handled in courts. In this context, the objective of the research is to develop a multicriteria neutrosophic method to evaluate the admissibility of precautionary measures in arbitration proceedings in the Metropolitan District of Quito. This methodology will allow an exhaustive analysis of various situations that may arise in the implementation of precautionary measures, as well as provide guidelines on how to make these requests appropriately. The document underlines the importance of adopting preventive approaches that ensure the effectiveness of arbitration decisions and minimize the need to resort to judicial instances, emphasizing the uniqueness of the arbitration process in the management of precautionary measures.

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How to Cite
Neutrosophic multicriteria method for the evaluation of the admissibility of interim measures in arbitration proceedings. (2025). Neutrosophic Computing and Machine Learning. ISSN 2574-1101, 37, 39-46. https://fs.unm.edu/NCML2/index.php/112/article/view/698
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How to Cite

Neutrosophic multicriteria method for the evaluation of the admissibility of interim measures in arbitration proceedings. (2025). Neutrosophic Computing and Machine Learning. ISSN 2574-1101, 37, 39-46. https://fs.unm.edu/NCML2/index.php/112/article/view/698