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Data-driven decisions: Artificial intelligence-based experimental validation of ocean ecosystem services scale

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Detalhes bibliográficos
Resumo:Several studies address the main topic of research, ecosystem services. It is also proven that decision-making in organizations generally involves a decision-maker, who assumes internal responsibility for the results. However, when the decision is collective, we need to think about the context of governance. How can we increase the sustainable decisions of ocean ecosystem services governance? When this decision is applied to ocean ecosystem services, in particular, we need a parameter. Therefore, the proposed scale is an initial guide for support key decision-makers decisions on the governance of ocean services ecosystems. The scale proposal with validation through classical linear regression, and supported by an artificial neural network, demonstrates the main variables that influence the decision and contribute to possible risk mitigations in terms of decisions.
Autores principais:Figueiredo, Ronnie
Outros Autores:Cabral, Pedro
Assunto:Artificial neural network Decision makers Ecosystem services Governance Measurement Ocean Scale
Ano:2024
País:Portugal
Tipo de documento:artigo
Tipo de acesso:acesso aberto
Instituição associada:Instituto Politécnico de Leiria
Idioma:inglês
Origem:IC-online
Descrição
Resumo:Several studies address the main topic of research, ecosystem services. It is also proven that decision-making in organizations generally involves a decision-maker, who assumes internal responsibility for the results. However, when the decision is collective, we need to think about the context of governance. How can we increase the sustainable decisions of ocean ecosystem services governance? When this decision is applied to ocean ecosystem services, in particular, we need a parameter. Therefore, the proposed scale is an initial guide for support key decision-makers decisions on the governance of ocean services ecosystems. The scale proposal with validation through classical linear regression, and supported by an artificial neural network, demonstrates the main variables that influence the decision and contribute to possible risk mitigations in terms of decisions.