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Incentivos individuales que promueven la inteligencia colectiva

Guocheng Wang1,2, Qi Su3,4, Long Wang1

  • 1Center for Systems and Control, College of Engineering, Peking University, Beijing 100871, China.

Proceedings of the National Academy of Sciences of the United States of America
|December 15, 2025
PubMed
Resumen

La inteligencia colectiva emerge en grupos a través del aprendizaje social, incluso sin control central. Recompensar a las personas que reducen el error colectivo (reformadores) es más eficaz que recompensar a las personas precisas (expertos) para promover la resolución de problemas en grupo.

Palabras clave:
comportamiento colectivodinámica evolutivaimitaciónaprendizajeresolución de problemas

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Área de la Ciencia:

  • Ciencias Sociales Computacionales
  • Economía Conductual
  • Teoría de Juegos Evolutiva

Sus antecedentes:

  • La inteligencia colectiva, donde los grupos superan a los individuos, se observa en la naturaleza pero no se explica completamente por algoritmos que requieren control central.
  • El aprendizaje social a través de la imitación generalmente reduce la diversidad y dificulta la inteligencia colectiva en las poblaciones naturales.
  • Los modelos existentes luchan por explicar la evolución de la inteligencia colectiva en sistemas que carecen de una planificación central sofisticada.

Objetivo del estudio:

  • Investigar cómo la inteligencia colectiva puede surgir en poblaciones naturales a través del aprendizaje social sin control central.
  • Proponer y analizar nuevas estructuras de recompensa que promuevan la inteligencia colectiva.
  • Comparar la eficacia de diferentes mecanismos de recompensa para fomentar la resolución de problemas en grupo.

Principales métodos:

  • Simulación de una tarea de predicción donde los individuos observan un factor de un resultado continuo.
  • Desarrollo de dos estructuras de recompensa: una para la precisión personal (expertos) y otra para reducir el error colectivo (reformadores).
  • Análisis matemático para demostrar la emergencia de la inteligencia colectiva y la diversidad bajo estos esquemas de recompensa.

Principales resultados:

  • Tanto las estructuras de recompensa para expertos como para reformadores pueden mantener la diversidad y establecer la inteligencia colectiva de forma demostrable.
  • Las recompensas basadas en el error colectivo (reformadores) son más robustas en diversos entornos de problemas que las recompensas basadas en la precisión personal (expertos).
  • Identificar y recompensar a los reformadores es más eficaz que identificar a los expertos para promover la inteligencia colectiva.

Conclusiones:

  • La inteligencia colectiva puede evolucionar en poblaciones naturales a través del aprendizaje social guiado por estructuras de recompensa apropiadas.
  • Los mecanismos de recompensa que se centran en el beneficio colectivo, en lugar de la precisión individual, son superiores para fomentar una resolución de problemas grupal robusta.
  • Los hallazgos ofrecen información sobre el diseño de sistemas que mejoran el rendimiento grupal en tareas de predicción complejas.