# Designing an Automatic Agent for Repeated Language based Persuasion Games

arXiv: https://arxiv.org/abs/2105.04976 · HTML: https://mlsanity.com/paper/2105.04976/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 65 references that are in the map.
- First posted on arXiv 11 May 2021.

## Builds on (most cited first, 20 of 20)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation](https://mlsanity.com/paper/1611.04558/) (2016)
- [Deal or No Deal? End-to-End Learning for Negotiation Dialogues](https://mlsanity.com/paper/1706.05125/) (2017)
- [Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Good](https://mlsanity.com/paper/1906.06725/) (2019)
- [Multi-Agent Cooperation and the Emergence of (Natural) Language](https://mlsanity.com/paper/1612.07182/) (2016)
- [Emergence of Language with Multi-agent Games: Learning to Communicate with Sequences of Symbols](https://mlsanity.com/paper/1705.11192/) (2017)
- [Winning Arguments: Interaction Dynamics and Persuasion Strategies in Good-faith Online Discussions](https://mlsanity.com/paper/1602.01103/) (2016)
- [Learning to Compose Neural Networks for Question Answering](https://mlsanity.com/paper/1601.01705/) (2016)
- [Language Understanding for Text-based Games Using Deep Reinforcement Learning](https://mlsanity.com/paper/1506.08941/) (2015)
- [Bayesian Incentive-Compatible Bandit Exploration](https://mlsanity.com/paper/1502.04147/) (2015)
- [Evaluating Conversational Recommender Systems via User Simulation](https://mlsanity.com/paper/2006.08732/) (2020)
- [How to Build User Simulators to Train RL-based Dialog Systems](https://mlsanity.com/paper/1909.01388/) (2019)
- [Learning Language Games through Interaction](https://mlsanity.com/paper/1606.02447/) (2016)
- [End-to-End Trainable Non-Collaborative Dialog System](https://mlsanity.com/paper/1911.10742/) (2019)
- [Linguistic Harbingers of Betrayal: A Case Study on an Online Strategy Game](https://mlsanity.com/paper/1506.04744/) (2015)
- [Weakly-Supervised Hierarchical Models for Predicting Persuasive Strategies in Good-faith Textual Requests](https://mlsanity.com/paper/2101.06351/) (2021)
- [Predicting Decisions in Language Based Persuasion Games](https://mlsanity.com/paper/2012.09966/) (2020)
- [Predicting Strategic Behavior from Free Text](https://mlsanity.com/paper/2004.02973/) (2020)
- [Multi-Issue Social Learning](https://mlsanity.com/paper/1605.02489/) (2016)

## Built on by (most cited first, 5 of 5)

- [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)
- [In the Eye of the Beholder: Robust Prediction with Causal User Modeling](https://mlsanity.com/paper/2206.00416/) (2022)
- [Persuasion Strategies in Advertisements](https://mlsanity.com/paper/2208.09626/) (2022)

## Abstract (arXiv)

Persuasion games are fundamental in economics and AI research and serve as the basis for important applications. However, work on this setup assumes communication with stylized messages that do not consist of rich human language. In this paper we consider a repeated sender (expert) -- receiver (decision maker) game, where the sender is fully informed about the state of the world and aims to persuade the receiver to accept a deal by sending one of several possible natural language reviews. We design an automatic expert that plays this repeated game, aiming to achieve the maximal payoff. Our expert is implemented within the Monte Carlo Tree Search (MCTS) algorithm, with deep learning models that exploit behavioral and linguistic signals in order to predict the next action of the decision maker, and the future payoff of the expert given the state of the game and a candidate review. We demonstrate the superiority of our expert over strong baselines, its adaptability to different decision makers, and that its selected reviews are nicely adapted to the proposed deal.