Articles producció científica> Enginyeria Química

Complex decision-making strategies in a stock market experiment explained as the combination of few simple strategies

  • Dades identificatives

    Identificador: imarina:9216729
    Autors:
    Poux-Medard, GaelCobo-Lopez, SergioDuch, JordiGuimera, RogerSales-Pardo, Marta
    Resum:
    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.
  • Altres:

    Autor segons l'article: Poux-Medard, Gael; Cobo-Lopez, Sergio; Duch, Jordi; Guimera, Roger; Sales-Pardo, Marta
    Departament: Enginyeria Química
    Autor/s de la URV: Cobo Lopez, Sergio / Duch Gavaldà, Jordi / Guimera Manrique, Roger / Sales Pardo, Marta
    Paraules clau: Stochastic block model Prediction Persistence Human behavior Decision making process Choice mechanisms Blockmodels
    Resum: 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.
    Àrees temàtiques: Social sciences, mathematical methods Modeling and simulation Mathematics, interdisciplinary applications Engenharias iv Engenharias i Computer science applications Computational mathematics Ciencias sociales Ciência da computação
    Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
    Adreça de correu electrònic de l'autor: sergio.cobo@urv.cat roger.guimera@urv.cat jordi.duch@urv.cat marta.sales@urv.cat
    Identificador de l'autor: 0000-0002-3597-4310 0000-0003-2639-6333 0000-0002-8140-6525
    Data d'alta del registre: 2024-10-19
    Volum de revista: 10
    Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
    Enllaç font original: https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-021-00280-z
    URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referència a l'article segons font original: Epj Data Science. 10 (1): 26-
    Referència de l'ítem segons les normes APA: Poux-Medard, Gael; Cobo-Lopez, Sergio; Duch, Jordi; Guimera, Roger; Sales-Pardo, Marta (2021). Complex decision-making strategies in a stock market experiment explained as the combination of few simple strategies. Epj Data Science, 10(1), 26-. DOI: 10.1140/epjds/s13688-021-00280-z
    DOI de l'article: 10.1140/epjds/s13688-021-00280-z
    Entitat: Universitat Rovira i Virgili
    Any de publicació de la revista: 2021
    Tipus de publicació: Journal Publications
  • Paraules clau:

    Computational Mathematics,Computer Science Applications,Mathematics, Interdisciplinary Applications,Modeling and Simulation,Social Sciences, Mathematical Methods
    Stochastic block model
    Prediction
    Persistence
    Human behavior
    Decision making process
    Choice mechanisms
    Blockmodels
    Social sciences, mathematical methods
    Modeling and simulation
    Mathematics, interdisciplinary applications
    Engenharias iv
    Engenharias i
    Computer science applications
    Computational mathematics
    Ciencias sociales
    Ciência da computação
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