  Many studies have shown that there are regularities in the way human beings
make decisions. However, our ability to obtain models that capture such
regularities and can accurately predict unobserved decisions is still limited.
We tackle this problem in the context of individuals who are given information
relative to the evolution of market prices and asked to guess the direction of
the market. We use a networks inference approach with stochastic block models
(SBM) to find the model and network representation that is most predictive of
unobserved decisions. Our results suggest that users mostly use recent
information (about the market and about their previous decisions) to guess.
Furthermore, the analysis of SBM groups reveals a set of strategies used by
players to process information and make decisions that is analogous to
behaviors observed in other contexts. Our study provides and example on how to
quantitatively explore human behavior strategies by representing decisions as
networks and using rigorous inference and model-selection approaches.
