Your browser doesn't support javascript.
loading
Getting closer to the goal by being less capable.
Manrique, Pedro D; Klein, Mason; Li, Yao Sheng; Xu, Chen; Hui, Pak Ming; Johnson, Neil F.
Afiliación
  • Manrique PD; Physics Department, University of Miami, Coral Gables, FL 33126, USA.
  • Klein M; Physics Department, University of Miami, Coral Gables, FL 33126, USA.
  • Li YS; College of Physics, Optoelectronics and Energy, Soochow University, Suzhou 215006, China.
  • Xu C; College of Physics, Optoelectronics and Energy, Soochow University, Suzhou 215006, China.
  • Hui PM; Department of Physics, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China.
  • Johnson NF; Physics Department, George Washington University, Washington D.C., 20052, USA.
Sci Adv ; 5(2): eaau5902, 2019 02.
Article en En | MEDLINE | ID: mdl-30775434
ABSTRACT
Understanding how systems with many semi-autonomous parts reach a desired target is a key question in biology (e.g., Drosophila larvae seeking food), engineering (e.g., driverless navigation), medicine (e.g., reliable movement for brain-damaged individuals), and socioeconomics (e.g., bottom-up goal-driven human organizations). Centralized systems perform better with better components. Here, we show, by contrast, that a decentralized entity is more efficient at reaching a target when its components are less capable. Our findings reproduce experimental results for a living organism, predict that autonomous vehicles may perform better with simpler components, offer a fresh explanation for why biological evolution jumped from decentralized to centralized design, suggest how efficient movement might be achieved despite damaged centralized function, and provide a formula predicting the optimum capability of a system's components so that it comes as close as possible to its target or goal.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Modelos Teóricos Tipo de estudio: Prognostic_studies Idioma: En Revista: Sci Adv Año: 2019 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Modelos Teóricos Tipo de estudio: Prognostic_studies Idioma: En Revista: Sci Adv Año: 2019 Tipo del documento: Article País de afiliación: Estados Unidos