Foundationalism

Foundationalism concerns philosophical theories of knowledge resting upon non-inferential justified belief, or some secure foundation of certainty such as a conclusion inferred from a basis of sound premises. The main rival of the foundationalist theory of justification is the coherence theory of justification, whereby a body of knowledge, not requiring a secure foundation, can be established by the interlocking strength of its components, like a puzzle solved without prior certainty that each small region was solved correctly. Identifying the alternatives as either circular reasoning or infinite regress, and thus exhibiting the regress problem, Aristotle made foundationalism his own clear choice, positing basic beliefs underpinning others. Descartes, the most famed foundationalist, discovered a foundation in the fact of his own existence and in the "clear and distinct" ideas of reason, whereas Locke found a foundation in experience. Differing foundations may reflect differing epistemological emphases—empiricists emphasizing experience, rationalists emphasizing reason—but may blend both. In the 1930s, debate over foundationalism revived. Whereas Moritz Schlick viewed scientific knowledge like a pyramid where a special class of statements does not require verification through other beliefs and serves as a foundation, Otto Neurath argued that scientific knowledge lacks an ultimate foundation and acts like a raft. In the 1950s, foundationalism fell into decline – largely due to the influence of Willard Van Orman Quine, whose ontological relativity found any belief networked to one's beliefs on all of reality, while auxiliary beliefs somewhere in the vast network are readily modified to protect desired beliefs. Classically, foundationalism had posited infallibility of basic beliefs and deductive reasoning between beliefs—a strong foundationalism. Around 1975, weak foundationalism emerged. Thus recent foundationalists have variously allowed fallible basic beliefs, and inductive reasoning between them, either by enumerative induction or by inference to the best explanation.

Out of their minds? Externalist challenges for using AI in forensic psychiatry

The effects of model complexity and size on metabolic flux distribution and control: case study in Escherichia coli

On the performance of random reshuffling in stochastic learning

The effects of model complexity and size on metabolic flux distribution and control: case study in Escherichia coli

Out of their minds? Externalist challenges for using AI in forensic psychiatry

On the performance of random reshuffling in stochastic learning