Predicting Strategic Behavior from Free Text (Extended Abstract) release_kbp4p2slsrculnqg2ig2dvchde

by Omer Ben-Porat, Lital Kuchy, Sharon Hirsch, Guy Elad, Roi Reichart, Moshe Tennenholtz

Published in International Joint Conference on Artificial Intelligence by International Joint Conferences on Artificial Intelligence Organization.

2020   p4943-4950

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? We initiate research on this question by providing preliminary positive results.
In application/xml+jats format

Archived Files and Locations

application/pdf  157.3 kB
file_cfspdj3ibjbbhgzjlkk77q7oqu
www.ijcai.org (web)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  paper-conference
Stage   published
Year   2020
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 0f5d995d-34ba-4064-8c74-633c077cce6b
API URL: JSON