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Risk and Uncertainty on Technology and Science Under Bayes and Popper's Statements View
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Author(s): Vicente Gonzalez-Prida (University of Seville, Spain & Universidad Nacional de Educación a Distancia, Spain), Jesus Zamora Bonilla (Universidad Nacional de Educación a Distancia, Spain), Christopher Nikulin Chandia (University Federico Santa María, Chile)and Antonio Guillén (University of Seville, Spain)
Copyright: 2021
Pages: 14
Source title:
Advanced Models and Tools for Effective Decision Making Under Uncertainty and Risk Contexts
Source Author(s)/Editor(s): Vicente González-Prida (University of Seville, Spain & National University of Distance Education, Spain)and María Carmen Carnero (University of Castilla-La Mancha, Spain)
DOI: 10.4018/978-1-7998-3246-1.ch007
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Abstract
The overall perspective of this study is related to the concept of risk and uncertainty in the world today. In this sense, it considers Popper's contributions together with the deductive method, contrasted Bayes' contributions with the inductive method. On one hand, induction allows to generate results considered probabilistically true. This is basically the method used by supervised predictive methods of machine learning, where a general rule is inferred from particular examples in which solutions are known, inducing consequently to possible results for new inputs. On the other hand, deduction is a process in which general hypotheses are proposed, and from them, particular statements are obtained. These particular statements can obviously generate the rejection of those initial hypotheses. Under these considerations, Bayes' and Popper's postulates should not be understood as opposed methods. With this, the specific objectives of this chapter states on an overview about technology and its relationship with science, being analyzed from the Popperian and Bayesian perspective.
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