Panjwani, Raja and Lehtinen, Aki (2023) Soundness Analysis: Justifying Deductive Reasoning in Model-Based Sciences. [Preprint]

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Abstract

Scientists often draw deductive inferences from highly idealized models. These models characterize true features of their targets, but they also invariably misrepresent some of their attributes, often with explicit falsehoods. This article explains why this prima facie unsound practice can systematically lead to insights. We develop a method,
which we call `Soundness Analysis', for extracting from a model a set of true statements about a target system, which can be used to construct a sound argument for a model's conclusion. We illustrate the method on the Hoff-Stiglitz model from economics, and we argue that the possibility of such an analysis sheds new light on mathematical
explanation in model-based sciences.

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Item Type:	Preprint
Creators:	Creators Email ORCID Panjwani, Raja rp2662@nyu.edu Lehtinen, Aki aki.lehtinen@helsinki.fi
Subjects:	Specific Sciences > Economics General Issues > Explanation General Issues > Models and Idealization General Issues > Structure of Theories
Depositing User:	Raja Panjwani
Date Deposited:	29 Apr 2023 12:30
Last Modified:	29 Apr 2023 12:30
Item ID:	22025
Subjects:	Specific Sciences > Economics General Issues > Explanation General Issues > Models and Idealization General Issues > Structure of Theories
Date:	28 April 2023
URI:	https://philsci-archive.pitt.edu/id/eprint/22025

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