# A pragmatic account of the weak evidence effect

arXiv: https://arxiv.org/abs/2112.03799 · HTML: https://mlsanity.com/paper/2112.03799/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 80 references that are in the map.
- First posted on arXiv 7 Dec 2021.

## Builds on (most cited first, 4 of 4)

- [AI safety via debate](https://mlsanity.com/paper/1805.00899/) (2018)
- [On the Utility of Learning about Humans for Human-AI Coordination](https://mlsanity.com/paper/1910.05789/) (2019)
- [Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC](https://mlsanity.com/paper/1507.04544/) (2015)
- [Learning Representations by Humans, for Humans](https://mlsanity.com/paper/1905.12686/) (2019)

## Built on by (most cited first, 2 of 2)

- [Retrieval-augmented systems can be dangerous medical communicators](https://mlsanity.com/paper/2502.14898/) (2025)
- [Cognitive models can reveal interpretable value trade-offs in language models](https://mlsanity.com/paper/2506.20666/) (2025)

## Abstract (arXiv)

Language is not only used to transmit neutral information; we often seek to persuade by arguing in favor of a particular view. Persuasion raises a number of challenges for classical accounts of belief updating, as information cannot be taken at face value. How should listeners account for a speaker's "hidden agenda" when incorporating new information? Here, we extend recent probabilistic models of recursive social reasoning to allow for persuasive goals and show that our model provides a pragmatic account for why weakly favorable arguments may backfire, a phenomenon known as the weak evidence effect. Critically, this model predicts a systematic relationship between belief updates and expectations about the information source: weak evidence should only backfire when speakers are expected to act under persuasive goals and prefer the strongest evidence. We introduce a simple experimental paradigm called the Stick Contest to measure the extent to which the weak evidence effect depends on speaker expectations, and show that a pragmatic listener model accounts for the empirical data better than alternative models. Our findings suggest further avenues for rational models of social reasoning to illuminate classical decision-making phenomena.