Complex decision-making strategies in a stock market experiment explained as the combination of few simple strategies
Department of Physics, Ecole Normale Supérieure of Lyon, 69342, Lyon, France
2 ERIC Lab, Université de Lyon, 69361, Lyon, France
3 Department of Chemical Engineering, Universitat Rovira i Virgili, 43007, Tarragona, Spain
4 Department of Mathematics and Computer Science, Universitat Rovira i Virgili, 43007, Tarragona, Spain
5 ICREA, 08010, Barcelona, Spain
Accepted: 3 May 2021
Published online: 18 May 2021
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.
Key words: Stochastic block model / Decision making process / Human behavior / Choice mechanisms
© The Author(s) 2021
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.