Predicting Strategic Behavior from Free Text

• URL: http://arxiv.org/abs/2004.02973v2
• Date: Tue, 19 May 2020 08:17:52 GMT
• Title: Predicting Strategic Behavior from Free Text
• Authors: Omer Ben-Porat, Sharon Hirsch, Lital Kuchy, Guy Elad, Roi Reichart, Moshe Tennenholtz
• Abstract summary: 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.
• Score: 38.506665373140876
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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