论文标题
算法复杂性的反事实分析：可能世界之间的指标
Counterfactual Analysis by Algorithmic Complexity: A metric between possible worlds
论文作者
论文摘要
反事实已成为跨学科兴趣的重要领域，尤其是在逻辑，语言哲学，认识论，形而上学，心理学，决策理论甚至人工智能方面。在这项研究中，我们提出了针对事实的一种新的分析形式：算法复杂性分析。受刘易斯·斯塔纳克（Lewis-Stalnaker）的尼古拉斯·库列（NicholasCorrêa）2 manuscrito-rev的启发。 int。 FIL。 Campinas，2022年。可能的世界语义，拟议的方法允许对David Lewis和Robert Stalnaker之间关于极限和奇异性假设的辩论进行新的解释。除其他结果外，我们还提供了一种新的方法来回答古德曼和奎因在模糊性，上下文依赖性和反事实的非单调性方面提出的问题。与文学进行对话，本研究将寻求为这场辩论带来新的见解和工具。我们希望我们的分析方法可以使反事实的观点更容易理解，并以哲学上合理的方式与我们通常思考反事实命题和富有想象力的推理的方式保持一致。
Counterfactuals have become an important area of interdisciplinary interest, especially in logic, philosophy of language, epistemology, metaphysics, psychology, decision theory, and even artificial intelligence. In this study, we propose a new form of analysis for counterfactuals: analysis by algorithmic complexity. Inspired by Lewis-Stalnaker's Nicholas Corrêa 2 Manuscrito-Rev. Int. Fil. Campinas, 2022. Possible Worlds Semantics, the proposed method allows for a new interpretation of the debate between David Lewis and Robert Stalnaker regarding the Limit and Singularity assumptions. Besides other results, we offer a new way to answer the problems raised by Goodman and Quine regarding vagueness, context-dependence, and the non-monotonicity of counterfactuals. Engaging in a dialogue with literature, this study will seek to bring new insights and tools to this debate. We hope our method of analysis can make counterfactuals more understandable in an intuitively plausible way, and a philosophically justifiable manner, aligned with the way we usually think about counterfactual propositions and our imaginative reasoning.
