“Belief Updating under Uncertainty: Probabilistic Epistemology and Its Limitations”

Probabilistic epistemology, especially the Bayesian model, provides a dynamically updated framework for the construction of knowledge under uncertainty. Starting from the shortcomings of the traditional view of knowledge, this paper analyzes how probabilistic theory can cope with uncertainty by adjusting the strength of beliefs, so as to gradually approach the truth. Bayesian epistemology has shown its unique application potential in the fields of science, medicine and economic forecasting. Especially, Bayesian epistemology can enhance rational belief in dynamic environment through the update mechanism of conditional probability. Although Bayesian epistemology has many advantages, it has obvious limitations in its application to non-empirical fields such as ethics and metaphysics. This paper also explores the importance of Bayesianism in knowledge construction and compares it with other epistemological models such as evidentialism and dependability. This paper argues that although Bayesianism provides flexible instrumental support for knowledge construction, it still has great limitations in dealing with fuzzy and non-quantitative knowledge and ethical decision-making, which limits its application to broader epistemological problems. At the same time, it is pointed out that probabilistic epistemology can be combined with other methods to expand its application scope.