  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.
