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Risk assessment modeling for knowledge based and startup projects based on feasibility studies: A Bayesian network approach

Akhavan, Mina ; Sebt, Mohammad Vahid ; Ameli, Mariam

Knowledge-based systems, 2021-06, Vol.222, p.106992, Article 106992 [Periódico revisado por pares]

Amsterdam: Elsevier B.V

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  • Título:
    Risk assessment modeling for knowledge based and startup projects based on feasibility studies: A Bayesian network approach
  • Autor: Akhavan, Mina ; Sebt, Mohammad Vahid ; Ameli, Mariam
  • Assuntos: Bayesian analysis ; Bayesian network ; Decision making ; Feasibility studies ; Feasibility Study ; Knowledge based Projects ; Modelling ; Project feasibility ; Risk analysis ; Risk assessment ; Startup ; Uncertainty
  • É parte de: Knowledge-based systems, 2021-06, Vol.222, p.106992, Article 106992
  • Descrição: The start of any business requires investment. The risks involved in this pathway are one of the biggest barriers in each investment. Feasibility studies are one of the most common methods in analyzing an investment plan, but this method does not respond to the risk value of any plan. Therefore the present study aims to calculate the risk of a project through a feasibility study. To this end, Bayesian networks have attracted much attention as a powerful method for modeling decision making under uncertainty conditions in different domains. This paper presents a Bayesian network modeling framework that obtains the project risk by calculating uncertainty in net present value of projects. This model provides a powerful method for analyzing risk scenarios and their impact on the project success. This model can be used as a basis for assessing the risks of innovative projects whose feasibility study has been performed.
  • Editor: Amsterdam: Elsevier B.V
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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