探讨科学推理如何逐步逼近真理，为方法选择提供新标准。
Convergence to the Truth
- 以皮尔士思想为基础，主张推理方法应能在多种情境下趋向真理。
- 对比解释主义、工具主义与贝叶斯主义，突出收敛至真性的独特优势。
- 适合关注科学哲学、统计推断与机器学习理论基础的研究者。
本文回顾并发展了科学哲学中一种称为“收敛主义”的认识论传统，该传统认为科学推理方法应根据其在多种可能情境下趋向真理的能力进行评估。文章强调其源于皮尔士的思想，并结合形式认识论与数据科学（包括统计学与机器学习）的最新进展。通过与三种其他传统对比：(1) 解释主义，主张理论选择应基于解释力（如简洁性与拟合度）的综合平衡；(2) 工具主义，认为科学推理应追求有用模型而非真实理论；(3) 贝叶斯主义，将焦点从非黑即白的信念转向信念程度。本文系统阐明收敛主义的核心价值与理论地位。
原文摘要 · Abstract (English)
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.
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