Nov 11, 2022 01:15 PM - 03:15 PM(America/New_York)
20221111T1315 20221111T1515 America/New_York Formal Epistemology Forbes PSA 2022
Accuracy-first epistemology and scientific progressView Abstract
Contributed Papers 01:15 PM - 01:45 PM (America/New_York) 2022/11/11 18:15:00 UTC - 2022/11/11 18:45:00 UTC
The accuracy-first program attempts to ground epistemology in the norm that one’s beliefs should be as accurate as possible, where accuracy is measured using a scoring rule. We argue that considerations of scientific progress suggest that such a monism about epistemic value is untenable. In particular, we argue that counterexamples to the standard scoring rules are ubiquitous in the history of science, and hence that these scoring rules cannot be regarded as a precisification of our intuitive concept of epistemic value.
Peter Lewis
Dartmouth College
Don Fallis
Northeastern University
Branden Fitelson
Northeastern University
Absolutely Zero EvidenceView Abstract
Contributed Papers 01:45 PM - 02:15 PM (America/New_York) 2022/11/11 18:45:00 UTC - 2022/11/11 19:15:00 UTC
Statistical analysis is often used to evaluate the strength of evidence for or against scientific hypotheses. Here we consider evidence measurement from the point of view of representational measurement theory, focusing in particular on the 0-points of measurement scales. We argue that a properly calibrated evidence measure will need to count up from absolute 0, in a sense to be defined, and that this 0-point is likely to be something other than what one might have expected. This suggests the need for a new theory of statistical evidence in the context of which calibrated evidence measurement becomes tractable.
Veronica Vieland
Professor Emerita, The Ohio State University
Sang-Cheol Seok
Nationwide Children's Hospital
On the Logical Structure of Best ExplanationsView Abstract
Contributed Papers 02:15 PM - 02:45 PM (America/New_York) 2022/11/11 19:15:00 UTC - 2022/11/11 19:45:00 UTC
Standard articulations of Inference to the Best Explanation (IBE) imply the uniqueness claim that exactly one explanation should be inferred in response to an explanandum. This claim has been challenged as being both too strong (sometimes agnosticism between candidate explanatory hypotheses seems the rational conclusion) and too weak (in cases where multiple hypotheses might sensibly be conjointly inferred). I propose a novel interpretation of IBE that retains the uniqueness claim while also allowing for agnostic and conjunctive conclusions. I then argue that a particular probabilistic explication of explanatory goodness helpfully guides us in navigating such options when using IBE.
Jonah Schupbach
University Of Utah
Against theory-motivated data collection in scienceView Abstract
Contributed Papers 02:45 PM - 03:15 PM (America/New_York) 2022/11/11 19:45:00 UTC - 2022/11/11 20:15:00 UTC
We study the epistemic success of different data collection strategies. We develop a computational multi-agent model of the scientific process that jointly formalizes its core aspects: data collection, data explanation, and social learning. We find that agents who choose new experiments at random develop the most accurate accounts of the world. On the other hand, the agents following the confirmation, falsification, crucial experimentation (theoretical disagreement), or novelty-motivated strategies end up with an illusion of epistemic success: they develop promising accounts for the data they collected, while completely misrepresenting the ground truth that they intended to learn about.
Marina Dubova
PhD Student, Indiana University
Arseny Moskvichev
University Of California Irvine
Kevin Zollman
Carnegie Mellon University
Dartmouth College
Professor Emerita
The Ohio State University
University of Utah
PhD student
Indiana University
 Kenny Easwaran
Texas A&M
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