Fibra OpticaAlessandro Vespignani ha estado trabajando en la teoría y la modelación de redes complejas y ahora cree que esta disciplina junto con las crecientes mejoras en el acceso a datos, nos permitirá a los humanos alcanzar una capacidad de predicción notable en áreas nunca antes imaginadas.

Vespignani considera que un día predeciremos con una anticipación, especificidad y escala sin precedentes, cosas como los efectos económicos y sociales de miles de millones de nuevos usuarios de internet en China e India, o la ubicación y la cantidad exactas de vuelos de aerolíneas que conviene cancelar en todo el mundo para frenar la expansión de una pandemia.

Vespignani es profesor de informática y profesor adjunto de física y estadística en la Universidad de Indiana, donde también es el director del Centro para la Investigación de Sistemas y Redes Complejas.

Ya se ha demostrado que es posible rastrear el movimiento de tantas como 100.000 personas a la vez durante seis meses utilizando datos de los teléfonos móviles, y también usar el movimiento del dinero a escala mundial como un indicador de la movilidad humana. Existen sensores y otros elementos que generan datos a escala individual, tales como los Sistemas de Posicionamiento Global, el Bluetooth y el WiFi, los cuales dejan rastros detallados de algunos aspectos de nuestras vidas.

Esas son algunas de las nuevas fuentes de información básica que los investigadores están utilizando para conocer más sobre el comportamiento humano colectivo. Esta nueva «minería de datos» debería a su vez mejorar la habilidad de los científicos para pronosticar con precisión los efectos de fenómenos tales como eventos catastróficos, movimientos masivos de poblaciones, o invasiones de nuevos organismos en ecosistemas específicos.

Vespignani ha presentado un nuevo estudio sobre estas cuestiones, que ha realizado en colaboración con otros autores, incluyendo a Frank Schweitzer, del Instituto Federal Suizo de Tecnología en Zúrich, quien dirigió la tarea de examinar las interdependencias de las redes económicas como sistemas complejos.

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