Predicting Decisions in Language Based Persuasion Games

• URL: http://arxiv.org/abs/2012.09966v2
• Date: Thu, 7 Jan 2021 09:08:52 GMT
• Title: Predicting Decisions in Language Based Persuasion Games
• Authors: Reut Apel, Ido Erev, Roi Reichart, and Moshe Tennenholtz
• Abstract summary: This paper addresses the use of natural language in persuasion games. It aims to construct effective models for the prediction of these decisions. Our results demonstrate that given a prefix of the interaction sequence, our models can predict the future decisions of the decision-maker.
• Score: 31.63182861594025
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sender-receiver interactions, and specifically persuasion games, are widely researched in economic modeling and artificial intelligence, and serve as a solid foundation for powerful applications. However, in the classic persuasion games setting, the messages sent from the expert to the decision-maker are abstract or well-structured application-specific signals rather than natural (human) language messages, although natural language is a very common communication signal in real-world persuasion setups. This paper addresses the use of natural language in persuasion games, exploring its impact on the decisions made by the players and aiming to construct effective models for the prediction of these decisions. 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. The expert's payoff, in turn, depends on the number of times the decision-maker chooses the hotel. We consider a number of modeling approaches for this setup, differing from each other in the model type (deep neural network (DNN) 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.

Related papers

• Trustworthy Alignment of Retrieval-Augmented Large Language Models via Reinforcement Learning [84.94709351266557]
  We focus on the trustworthiness of language models with respect to retrieval augmentation. We deem that retrieval-augmented language models have the inherent capabilities of supplying response according to both contextual and parametric knowledge. Inspired by aligning language models with human preference, we take the first step towards aligning retrieval-augmented language models to a status where it responds relying merely on the external evidence.
  arXiv  Detail & Related papers  (2024-10-22T09:25:21Z)
• Rationalizing Predictions by Adversarial Information Calibration [65.19407304154177]
  We train two models jointly: one is a typical neural model that solves the task at hand in an accurate but black-box manner, and the other is a selector-predictor model that additionally produces a rationale for its prediction. We use an adversarial technique to calibrate the information extracted by the two models such that the difference between them is an indicator of the missed or over-selected features.
  arXiv  Detail & Related papers  (2023-01-15T03:13:09Z)
• Probing via Prompting [71.7904179689271]
  This paper introduces a novel model-free approach to probing, by formulating probing as a prompting task. We conduct experiments on five probing tasks and show that our approach is comparable or better at extracting information than diagnostic probes. We then examine the usefulness of a specific linguistic property for pre-training by removing the heads that are essential to that property and evaluating the resulting model's performance on language modeling.
  arXiv  Detail & Related papers  (2022-07-04T22:14:40Z)
• Designing an Automatic Agent for Repeated Language based Persuasion Games [32.20930723085839]
  We consider a repeated sender (expert) -- receiver (decision maker) game. 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.
  arXiv  Detail & Related papers  (2021-05-11T12:25:57Z)
• Learning from the Best: Rationalizing Prediction by Adversarial Information Calibration [39.685626118667074]
  We train two models jointly: one is a typical neural model that solves the task at hand in an accurate but black-box manner, and the other is a selector-predictor model that additionally produces a rationale for its prediction. We use an adversarial-based technique to calibrate the information extracted by the two models. For natural language tasks, we propose to use a language-model-based regularizer to encourage the extraction of fluent rationales.
  arXiv  Detail & Related papers  (2020-12-16T11:54:15Z)
• Indecision Modeling [50.00689136829134]
  It is important that AI systems act in ways which align with human values. People are often indecisive, and especially so when their decision has moral implications.
  arXiv  Detail & Related papers  (2020-12-15T18:32:37Z)
• Knowledgeable Dialogue Reading Comprehension on Key Turns [84.1784903043884]
  Multi-choice machine reading comprehension (MRC) requires models to choose the correct answer from candidate options given a passage and a question. Our research focuses dialogue-based MRC, where the passages are multi-turn dialogues. It suffers from two challenges, the answer selection decision is made without support of latently helpful commonsense, and the multi-turn context may hide considerable irrelevant information.
  arXiv  Detail & Related papers  (2020-04-29T07:04:43Z)
• Predicting Strategic Behavior from Free Text [38.506665373140876]
  We study the connection between messaging and action in an economic context, modeled as a game. We introduce the study of an individual's action prediction in a one-shot game based on free text he/she provides. We employ transductive learning to predict actions taken by these individuals in one-shot games based on these attributes.
  arXiv  Detail & Related papers  (2020-04-06T20:05:30Z)
• "Wait, I'm Still Talking!" Predicting the Dialogue Interaction Behavior Using Imagine-Then-Arbitrate Model [24.560203199376478]
  In real human-human conversations, human often sequentially sends several short messages for readability instead of a long message in one turn. We propose a novel Imagine-then-Arbitrate (ITA) neural dialogue model to help the agent decide whether to wait or to make a response directly.
  arXiv  Detail & Related papers  (2020-02-22T04:05:41Z)
• I love your chain mail! Making knights smile in a fantasy game world: Open-domain goal-oriented dialogue agents [69.68400056148336]
  We train a goal-oriented model with reinforcement learning against an imitation-learned chit-chat'' model. We show that both models outperform an inverse model baseline and can converse naturally with their dialogue partner in order to achieve goals.
  arXiv  Detail & Related papers  (2020-02-07T16:22:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.