A Communication-First Account of Explanation

2025-05-06

AI Summary

Traditional theories of causal explanation neglect its pragmatic, dialogical nature—treating explanation as a static propositional output rather than a dynamic, context-sensitive communicative act. Method: We propose the first “communication-first” formal theory of causal explanation, integrating interventionist causal models with Gricean conversational pragmatics. Explanations are modeled as context-sensitive responses to counterfactual questions (“What would have happened otherwise?”). Contribution/Results: By grounding the theory in communicative function—not epistemic or metaphysical primitives—explanatory virtues (e.g., relevance, depth) and normative influences on causal judgment emerge naturally as empirical consequences. The framework unifies philosophical hermeneutics and cognitive science via formal semantic modeling, successfully reproducing fine-grained cognitive regularities while maintaining theoretical parsimony and substantially enhancing interdisciplinary explanatory power.

Technology Category

• Reasoning under Uncertainty: Causality
• Knowledge Representation and Reasoning: Action, Change, and Causality
• Machine Learning: Causal Learning

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

This paper develops a formal account of causal explanation, grounded in a theory of conversational pragmatics, and inspired by the interventionist idea that explanation is about asking and answering what-if-things-had-been-different questions. We illustrate the fruitfulness of the account, relative to previous accounts, by showing that widely recognised explanatory virtues emerge naturally, as do subtle empirical patterns concerning the impact of norms on causal judgments. This shows the value of a communication-first approach to explanation: getting clear on explanation's communicative dimension is an important prerequisite for philosophical work on explanation. The result is a simple but powerful framework for incorporating insights from the cognitive sciences into philosophical work on explanation, which will be useful for philosophers or cognitive scientists interested in explanation.

Problem

• Develops formal account of causal explanation using pragmatics
• Links explanation to what-if questions and interventionist ideas
• Integrates cognitive science insights into philosophical explanation

Innovation

• Uses conversational pragmatics for causal explanation
• Integrates interventionist what-if questioning approach
• Combines cognitive science insights with philosophy