.
ANNOUNCEMENT SEV26
Synthese Topical Collection CFP: Severity and Learning from Error
This Topical Collection examines how inquiry learns from error by focusing on a basic principle of evidence in science, statistics, medicine, law, epistemology, and day-to-day learning: a claim is not well-tested, known or epistemically warranted, if it is based on a method that makes it easy to accept, conclude or infer the claim, even if it is false. Such a claim may accord well with the data, but it has not passed a stringent or severe test. While this overarching intuition is widely shared, the problem of how to understand or satisfy it remains unsolved. C. S. Peirce emphasizes randomization and (what is now called) pre-designation to achieve self-correcting methods. Popper viewed severity in terms of satisfying novel predictive success and surviving stringent attempts at falsification. Deborah Mayo (1996, 2018) combines elements from Popper and Peirce with the use of error probabilities from statistical methods: proposed solutions to problems earn warrant by surviving probes that were capable of showing them wrong or inadequate. This Topical Collection takes “severity” to be a broad meta-level concept according to which a claim – whether a report of a perception, a prediction, a hypothesis, or part of a model – is assessed according to whether, and how readily, its errors and inadequacies would have been found, if present. Continue reading
