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.
翻译:许多研究显示,人类决策的方式有规律性。然而,我们获得能捕捉这种规律性并能准确预测未观察的决定的模型的能力仍然有限。我们从获得与市场价格演变有关的信息并被问及市场方向的个人的角度来处理这个问题。我们利用与随机区块模型(SBM)的网络推论方法找到模型和网络代表最能预测未观察的决定。我们的结果表明,用户大多使用最新信息(关于市场及其先前的决定)来猜测。此外,对SBM集团的分析揭示了行为者用来处理信息并作出类似于其他情况下所观察到的行为的决定的一套战略。我们的研究提供并举例说明如何通过将决定作为网络、使用严格的推论和模型选择方法来量化地探索人类行为战略。