The connection between messaging and action is fundamental both to web applications, such as web search and sentiment analysis, and to economics. However, while prominent online applications exploit messaging in natural (human) language in order to predict non-strategic action selection, the economics literature focuses on the connection between structured stylized messaging to strategic decisions in games and multi-agent encounters. This paper aims to connect these two strands of research, which we consider highly timely and important due to the vast online textual communication on the web. Particularly, we introduce the following question: can free text expressed in natural language serve for the prediction of action selection in an economic context, modeled as a game? In order to initiate the research on this question, we introduce the study of an individual's action prediction in a one-shot game based on free text he/she provides, while being unaware of the game to be played. We approach the problem by attributing commonsensical personality attributes via crowd-sourcing to free texts written by individuals, and employing transductive learning to predict actions taken by these individuals in one-shot games based on these attributes. Our approach allows us to train a single classifier that can make predictions with respect to actions taken in multiple games. In experiments with three well-studied games, our algorithm compares favorably with strong alternative approaches. In ablation analysis, we demonstrate the importance of our modeling choices---the representation of the text with the commonsensical personality attributes and our classifier---to the predictive power of our model.

中文翻译：

---

从自由文本预测战略行为

消息传递和操作之间的联系对于 Web 应用程序（例如 Web 搜索和情感分析）以及经济学都至关重要。然而，虽然著名的在线应用程序利用自然（人类）语言的消息传递来预测非战略行动选择，但经济学文献侧重于结构化风格化消息传递与游戏中的战略决策和多代理遭遇之间的联系。本文旨在将这两个研究方向联系起来，由于网络上有大量的在线文本交流，我们认为这些研究非常及时和重要。特别是，我们引入了以下问题：以自然语言表达的自由文本能否用于预测经济背景下的动作选择，建模为游戏？为了启动对这个问题的研究，我们基于他/她提供的免费文本介绍了对个人在单次游戏中的动作预测的研究，同时不知道要玩的游戏。我们通过众包将常识性人格属性归因于个人撰写的自由文本，并采用转导学习来预测这些个人在基于这些属性的一次性游戏中采取的行动来解决该问题。我们的方法允许我们训练一个分类器，该分类器可以对多个游戏中采取的动作进行预测。在三个经过充分研究的游戏的实验中，我们的算法与强大的替代方法相比具有优势。在消融分析中，