Scientific Realism vs. Anti-Realism: Toward a Common Ground

TL;DR

This paper proposes a common ground for the scientific realism vs. anti-realism debate by leveraging insights from statistics and machine learning to address core challenges and clarify irreconcilable differences.

Contribution

It introduces a novel framework connecting both positions through the epistemic value of truths and scientific inference insights.

Findings

Identifies a shared epistemic value of truths.

Proposes leveraging scientific inference from statistics and machine learning.

Clarifies the epistemic roots of the debate's irreconcilability.

Abstract

The debate between scientific realism and anti-realism remains at a stalemate, making reconciliation seem hopeless. Yet, important work remains: exploring a common ground, even if only to uncover deeper points of disagreement and, ideally, to benefit both sides of the debate. I propose such a common ground. Specifically, many anti-realists, such as instrumentalists, have yet to seriously engage with Sober's call to justify their preferred version of Ockham's razor through a positive account. Meanwhile, realists face a similar challenge: providing a non-circular explanation of how their version of Ockham's razor connects to truth. The common ground I propose addresses these challenges for both sides; the key is to leverage the idea that everyone values some truths and to draw on insights from scientific fields that study scientific inference -- namely, statistics and machine learning. This common ground also isolates a distinctively epistemic root of the irreconcilability in the realism debate.