Cargando…

Modeling other minds: Bayesian inference explains human choices in group decision-making

To make decisions in a social context, humans have to predict the behavior of others, an ability that is thought to rely on having a model of other minds known as “theory of mind.” Such a model becomes especially complex when the number of people one simultaneously interacts with is large and action...

Descripción completa

Detalles Bibliográficos
Autores principales: Khalvati, Koosha, Park, Seongmin A., Mirbagheri, Saghar, Philippe, Remi, Sestito, Mariateresa, Dreher, Jean-Claude, Rao, Rajesh P. N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Association for the Advancement of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6881156/
https://www.ncbi.nlm.nih.gov/pubmed/31807706
http://dx.doi.org/10.1126/sciadv.aax8783
Descripción
Sumario:To make decisions in a social context, humans have to predict the behavior of others, an ability that is thought to rely on having a model of other minds known as “theory of mind.” Such a model becomes especially complex when the number of people one simultaneously interacts with is large and actions are anonymous. Here, we present results from a group decision-making task known as the volunteer’s dilemma and demonstrate that a Bayesian model based on partially observable Markov decision processes outperforms existing models in quantitatively predicting human behavior and outcomes of group interactions. Our results suggest that in decision-making tasks involving large groups with anonymous members, humans use Bayesian inference to model the “mind of the group,” making predictions of others’ decisions while also simulating the effects of their own actions on the group’s dynamics in the future.