Counterfactual Analysis by Algorithmic Complexity: A metric between possible worlds

Abstract

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. In addition to other results, we offer a new way to address the problems raised by Goodman and Quine regarding vagueness, context-dependence, and the non-monotonicity of counterfactuals. Engaging with literature in dialogue, this study will seek to bring new insights and tools to this debate. Our method of analysis can make counterfactuals more understandable in an intuitively plausible and philosophically justifiable way, aligned with how we usually think about counterfactual propositions and our imaginative reasoning.

BibTeX

@article{correa2021complexity,
  title={Counterfactual Analysis by Algorithmic Complexity: A metric between possible worlds},
  author={Corr{\^e}a, Nicholas Kluge and de Oliveira, Nythamar},
  journal={Manuscrito},
  number={45},
  year={2022}
}