Knowledge from multiple experiences

Abstract

This paper models knowledge in cases where an agent has multiple experiences over time. Using this model, we introduce a series of observations that undermine the pretheoretic idea that the evidential significance of experience depends on the extent to which that experience matches the world. On the basis of these observations, we model knowledge in terms of what is likely given the agent's experience. An agent knows p when p is implied by her epistemic possibilities. A world is epistemically possible when its probability given the agent's experiences is not significantly lower than the probability of the actual world given that experience.

Epistemology Normalized

“…We give a formal model of this kind of case in Goodman and Salow (2021, section 5). 30 29 This distinguishes the present account from that of Goldstein and Hawthorne (2022), whose 'Probabilistic Margin for Error' (a version of which we also express sympathy for in Goodman and Salow (2021, note 10)) entails that people sometimes believe less than this. 30 Carter and Goldstein (2021) propose a different, non-contextualist way of accommodating knowledge that things are not in all respects normal.…”

“…This is a good prediction: additional readings really are epistemically beneficial when they are highly accurate, and epistemically harmful when they are highly inaccurate; cf. Goldstein and Hawthorne (2022).…”

“…41 Our account here is closely related to that of Goldstein and Hawthorne (2022), who also model knowledge about a given quantity based on multiple observations, and do so in terms of comparative normality relations that are generated from probabilities. The main difference is that they predict failures of no defeat even given margins, and do not predict current knowledge to be sensitive to future observations.…”