COUNTERFACTUAL ANALYSIS BY ALGORITHMIC COMPLEXITY: A METRIC BETWEEN POSSIBLE WORLDS

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 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.

Automated summary

This study proposes a new method of counterfactual analysis using algorithmic complexity, which offers a new interpretation of the debate between Lewis and Stalnaker and provides solutions to problems related to counterfactuals. It aims to bring new insights to the understanding and reasoning of counterfactuals.