Probabilistic modeling of rational communication with conditionals

TL;DR

This paper introduces a probabilistic model for pragmatic reasoning about indicative conditionals, explaining various inference patterns and belief updates in context through a flexible, structured belief framework.

Contribution

It presents a novel probabilistic approach that models pragmatic interpretation of conditionals, integrating gradient beliefs and contextual assumptions to explain complex inferences.

Findings

Model explains epistemic inferences and conditional perfection.

Accounts for dependency between antecedent and consequent.

Resolves puzzles about belief updates with conditionals.

Abstract

While a large body of work has scrutinized the meaning of conditional sentences, considerably less attention has been paid to formal models of their pragmatic use and interpretation. Here, we take a probabilistic approach to pragmatic reasoning about indicative conditionals which flexibly integrates gradient beliefs about richly structured world states. We model listeners' update of their prior beliefs about the causal structure of the world and the joint probabilities of the consequent and antecedent based on assumptions about the speaker's utterance production protocol. We show that, when supplied with natural contextual assumptions, our model uniformly explains a number of inferences attested in the literature, including epistemic inferences, conditional perfection and the dependency between antecedent and consequent of a conditional. We argue that this approach also helps explain three puzzles introduced by Douven (2012) about updating with conditionals: depending on the utterance context, the listener's belief in the antecedent may increase, decrease or remain unchanged.