用统计与机器学习方法，为科学实在论与反实在论找到可对话的共同基础。
Scientific Realism vs. Anti-Realism: Toward a Common Ground
- 以统计与机器学习中的科学推理为桥梁，构建双方可共享的理论框架。
- 揭示实在论与反实在论分歧的根源在于对真理价值的不同理解。
- 适合哲学、科学方法论及人工智能伦理领域的研究者阅读。
科学实在论与反实在论之间的争论长期陷入僵局，看似难以调和。然而，探索共同基础仍具重要意义，即便仅能揭示更深层分歧，也或可惠及双方。本文提出一种共同基础：许多反实在论者（如工具主义者）尚未认真回应索伯关于以正面论证支持其版本奥卡姆剃刀的呼吁；而实在论者同样面临挑战，需提供非循环的解释，说明其奥卡姆剃刀如何关联真理。本文提出的共同基础正针对上述两方难题，核心在于承认各方均重视某些真理，并借鉴科学研究推断领域的洞见——即统计学与机器学习。该框架还揭示了实在论争论中不可调和性的独特认识论根源。
原文摘要 · Abstract (English)
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.
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