Convergence to the Truth
It addresses philosophical foundations for evaluating scientific inference methods, relevant to formal epistemology and data science, but is largely a review and development of existing ideas.
This paper reviews and develops convergentism, an epistemological tradition assessing inference methods by their ability to converge to the truth across scenarios, comparing it with explanationism, instrumentalism, and Bayesianism.
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.