Convergence to the Truth

TL;DR

This paper explores convergentism, an epistemological approach emphasizing the importance of inference methods that reliably converge to the truth across various scenarios, and compares it with other scientific inference traditions.

Contribution

It develops the convergentism framework within formal epistemology and data science, highlighting its historical roots and recent advancements, and contrasts it with explanationism, instrumentalism, and Bayesianism.

Findings

Convergentism emphasizes the convergence of inference methods to the truth.

Historical analysis links convergentism to C. S. Peirce's work.

Recent developments integrate convergentism into formal epistemology and data science.

Abstract

This article reviews and develops an epistemological tradition in the philosophy of science, known as convergentism, which holds that inference methods should be assessed based on their ability to converge to the truth across a range of possible scenarios. Emphasis is placed on its historical origins in the work of C. S. Peirce and its recent developments in formal epistemology and data science (including statistics and machine learning). Comparisons are made with three other traditions: (1) explanationism, which holds that theory choice should be guided by a theory's overall balance of explanatory virtues, such as simplicity and fit with data; (2) instrumentalism, which maintains that scientific inference should be driven by the goal of obtaining useful models rather than true theories; and (3) Bayesianism, which shifts the focus from all-or-nothing beliefs to degrees of belief.