The ‘Big Picture’: The Problem of Extrapolation in Basic Research

Abstract

Both clinical research and basic science rely on the epistemic practice of extrapolation from surrogate models, to the point that explanatory accounts presented in review papers and biology textbooks are in fact composite pictures reconstituted from data gathered in a variety of distinct experimental setups. This raises two new challenges to previously proposed mechanistic-similarity solutions to the problem of extrapolation: one pertaining to the absence of mechanistic knowledge in the early stages of research and the second to the large number of extrapolations underpinning explanatory accounts. An analysis of the strategies deployed in experimental research supports the conclusion that while results from validated surrogate models are treated as a legitimate line of evidence supporting claims about target systems, the overall structure of research projects also demonstrates that extrapolative inferences are not considered definitive or sufficient evidence, but only partially justified hypotheses subjected to further testing. 1. Introduction 2. Surrogate Models2.1. What exactly is a surrogate model? 2.2. Why use surrogate models? 3. Prior Validation of Surrogate Models3.1. The validation and ranking of surrogate models in the early stages of basic research 3.2. The validation of surrogate models in later stages of basic research and in clinical research 4. ‘Big Picture’ Accounts and the Extrapolations Underpinning Them4.1. The mosaic nature of mechanistic descriptions in basic science 4.2. Challenges for mechanistic-similarity-based validation protocols 5. Retrospective Testing of Extrapolated Knowledge5.1. Holistic confirmation 5.2. Fallback strategies 6. Conclusions

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 62 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.