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Related Experiment Video

Updated: Oct 27, 2025

Setting Limits on Supersymmetry Using Simplified Models

Setting Limits on Supersymmetry Using Simplified Models

Published on: November 15, 2013

Inconsistent idealizations and inferentialism about scientific representation.

¹Fordham University, Department of Philosophy, Lowenstein 916C, 113 W. 60th St, New York, NY 10023, USA.

Studies in History and Philosophy of Science

|July 22, 2021

Summary

This summary is machine-generated.

Inferentialism in scientific representation struggles with inconsistent models. This study proposes a solution by comparing representational "gameplay" to exploitable game glitches, preserving model function.

Keywords:

Idealization Inconsistency Models Scientific representation

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Area of Science:

Philosophy of Science
Scientific Representation
Epistemology

Background:

Inferentialism posits that scientific representation relies on "surrogative inferences."
A key challenge arises from internal inconsistencies within scientific theories and models.

Purpose of the Study:

To address the problem of inconsistent scientific models within inferentialism.
To demonstrate that unamended inferentialism grants unlimited representational power to inconsistent models.

Main Methods:

Analysis of inferentialist theories of scientific representation.
Development of an analogy between scientific models and exploitable glitches in video games.
Examination of proposed inferentialist responses to the inconsistency problem.

Main Results:

Unamended inferentialism incorrectly implies that inconsistent models possess unlimited representational power.
The proposed solution reframes representational capacity by considering the intended function of model components.
Violating the intended function, akin to exploiting a game glitch, invalidates representational claims.

Conclusions:

Inferentialism requires amendment to adequately handle scientific models with internal inconsistencies.
A functional, or "gameplay," approach can preserve the representational integrity of inconsistent models.
This framework offers a nuanced understanding of how flawed models can still represent target systems effectively.