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.

许多研究表明，人类决策的方式是有规律的。但是，我们获得捕获此类规律性并可以准确预测未观察到的决策的模型的能力仍然有限。我们在向个人提供有关市场价格变化的信息并要求猜测市场方向的个人的背景下解决这个问题。我们使用带有随机块模型（SBM）的网络推理方法来找到最能预测未观察到的决策的模型和网络表示形式。我们的结果表明，用户大多使用最新信息（有关市场及其先前的决定）进行猜测。此外，对SBM群体的分析揭示了玩家用来处理信息和做出类似于其他情况下观察到的行为的策略。我们的研究提供了一个示例，说明了如何通过将决策表示为网络并使用严格的推理和模型选择方法来定量地探索人类行为策略。