# Predicting Decisions in Language Based Persuasion Games

arXiv: https://arxiv.org/abs/2012.09966 · HTML: https://mlsanity.com/paper/2012.09966/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2020.

## Built on by (most cited first, 6 of 6)

- [GLEE: A Unified Framework and Benchmark for Language-based Economic Environments](https://mlsanity.com/paper/2410.05254/) (2024)
- [Can LLMs Replace Economic Choice Prediction Labs? The Case of Language-based Persuasion Games](https://mlsanity.com/paper/2401.17435/) (2024)
- [Human Choice Prediction in Language-based Persuasion Games: Simulation-based Off-Policy Evaluation](https://mlsanity.com/paper/2305.10361/) (2023)
- [Designing an Automatic Agent for Repeated Language based Persuasion Games](https://mlsanity.com/paper/2105.04976/) (2021)
- [In the Eye of the Beholder: Robust Prediction with Causal User Modeling](https://mlsanity.com/paper/2206.00416/) (2022)
- [Mind the Gap! Choice Independence in Using Multilingual LLMs for Persuasive Co-Writing Tasks in Different Languages](https://mlsanity.com/paper/2502.09532/) (2025)

## Abstract (arXiv)

Sender-receiver interactions, and specifically persuasion games, are widely researched in economic modeling and artificial intelligence. However, in the classic persuasion games setting, the messages sent from the expert to the decision-maker (DM) are abstract or well-structured signals rather than natural language messages. This paper addresses the use of natural language in persuasion games. For this purpose, we conduct an online repeated interaction experiment. At each trial of the interaction, an informed expert aims to sell an uninformed decision-maker a vacation in a hotel, by sending her a review that describes the hotel. While the expert is exposed to several scored reviews, the decision-maker observes only the single review sent by the expert, and her payoff in case she chooses to take the hotel is a random draw from the review score distribution available to the expert only. We also compare the behavioral patterns in this experiment to the equivalent patterns in similar experiments where the communication is based on the numerical values of the reviews rather than the reviews' text, and observe substantial differences which can be explained through an equilibrium analysis of the game. We consider a number of modeling approaches for our verbal communication setup, differing from each other in the model type (deep neural network vs. linear classifier), the type of features used by the model (textual, behavioral or both) and the source of the textual features (DNN-based vs. hand-crafted). Our results demonstrate that given a prefix of the interaction sequence, our models can predict the future decisions of the decision-maker, particularly when a sequential modeling approach and hand-crafted textual features are applied. Further analysis of the hand-crafted textual features allows us to make initial observations about the aspects of text that drive decision making in our setup