예측주의에 대한 베이즈주의적 접근과 엄격한 시험
Bayesian Approaches to Predicitivism and Severe Tests
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일반적으로 예측주의에서는 포섭된 증거보다 예측된 증거가 해당 가설을 올바로 지지할 수 있다고 주장한다. 본 논문에서는 주요하게 예측주의를 옹호하는 마허의 베이즈주의적 접근 방식과 워럴의 견해를 비판적으로 검토한다. 이 과정에서 해당 가설의 배후에서 그 상위 이론이 중요한 역할을 함을 드러내고, 그러한 이론들과 관련해 메이요의 엄격한 시험의 관점에서라면 그들의 예측주의에 적절히 대응할 수 있음을 보이고자 한다. 이로써 본 논문의 목표는 (강한) 예측주의에 반대하는 메이요의 견해를 새롭게 강화하는 일이다. 이 과정에서 메이요 자신의 잘못을 드러내고 그를 시정하기도 할 것이다. 논의 전개 중에 정동욱의 견해는 좋은 매개 역할을 하였다.
Abstract
In general, predictivism claims that predicted evidence rather than accommodated one can support a concerned hypothesis correctly. In this paper, mainly, Maher's Bayesian approach to predictivism and Worrall's view on it are critically examined. With the examinations, it'll be shown that higher-level theories play an important role beyond the hypothesis in question. And, regarding such theories, it is argued how adequately Maher's and Worrall's predictivism can be criticized from the perspective of Mayo's severe tests. Thereby, the aim of this paper is to strengthen Mayo's denial of (strong) predictivism in a fresh way. On the way, also, her own faults shall be revealed and put right appropriately. Jung's discussions have been very helpful as a mediation for my goals.
참고문헌 (28)
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