TY - GEN
T1 - Predicting strategic behavior from free text
AU - Ben-Porat, Omer
AU - Kuchy, Lital
AU - Hirsch, Sharon
AU - Elad, Guy
AU - Reichart, Roi
AU - Tennenholtz, Moshe
N1 - Funding Information:
The authors wish to thank the members of the IE@Technion NLP group for their valuable feedback and advice. The work of O. Ben-Porat is partially funded by a PhD fellowship from JPMorgan Chase & Co. The work of M. Tennenholtz is funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation pro-gramme (grant agreement n? 740435).
Funding Information:
The authors wish to thank the members of the IE@Technion NLP group for their valuable feedback and advice. The work of O. Ben-Porat is partially funded by a PhD fellowship from JPMorgan Chase & Co. The work of M. Tennenholtz is funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement n◦ 740435).
Publisher Copyright:
© 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.
PY - 2020/1/1
Y1 - 2020/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85097356806&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85097356806
T3 - IJCAI International Joint Conference on Artificial Intelligence
SP - 5020
EP - 5024
BT - Proceedings of the 29th International Joint Conference on Artificial Intelligence, IJCAI 2020
A2 - Bessiere, Christian
PB - International Joint Conferences on Artificial Intelligence
T2 - 29th International Joint Conference on Artificial Intelligence, IJCAI 2020
Y2 - 1 January 2021
ER -