Creating a Real Life Version of Psychohistory from Isaac Asimov’s Foundation Series

Illustration by D. Balcan, B. Goncalves, H. Hu and A. Vespignani, Credit: Indiana University

This epidemic invasion tree identifies 3,200 separate population nodes worldwide linked to the H5N1 avian flu pandemic of 2003-04 that originated in Hanoi. Time ordering of the epidemic’s movement is displayed from the earliests paths in dark red, moving to light red, yellow, green and blue as time progressed.

On the path to creating a real life version of the fictional psychohistory from Isaac Asimov’s Foundation Series.

Psychohistory, a fictional science in Isaac Asimov’s Foundation universe, combines history, sociology, and mathematical statistics to make (nearly) exact predictions of the collective actions of very large groups of people, such as the Galactic Empire.

Indiana University’s Alessandro Vespignani believes we will one day predict with unprecedented foresight, specificity and scale such things as the economic and social effects of billions of new Internet users in China and India, or the exact location and number of airline flights to cancel around the world in order to halt the spread of a pandemic.

In July 24, 2009 “Perspectives” section of the journal Science, Vespignani writes that advances in complex networks theory and modeling, along with access to new data, will enable humans to achieve true predictive power in areas never before imagined. This capability will be realized as the one wild card in the mix — the social behavior of large aggregates of humans — becomes more definable through progress in data gathering, new informatics tools and increases in computational power.

Researchers have already shown they can track the movement of as many as 100,000 people at a time over six months using mobile phone data, and use worldwide currency traffic as a proxy for human mobility. There are sensors and tags generating data at micro, one-to-one interaction levels, much as Bluetooth, Global Positioning Systems and WiFi leave behind detailed traces of our lives.

Such are some of the new sources of basic information that researchers are using to gain knowledge about aggregated human behavior. This new “reality mining” should in turn enhance the ability of researchers and scientists to accurately forecast the effects of phenomena like catastrophic events, mass population movements or invasions of new organisms into ecosystems, Vespignani said.

“While the needed integrated approach is still in its infancy, using network theory, mathematical biology, statistics, computer science and nonequilibrium statistical physics will play a key role in the creation of computational forecasting infrastructures,” Vespignani said. “And that should help us design better energy distribution systems, plan for traffic-free cities and manage the deployment of the world’s resources.”

The Indiana University methods will eventually be combined with the work of Bruce Beuno de Mesquita who has advanced political prediction capabilities.

Bruce Bueno de Mesquita predictions from February 2009 on Iran.

– Prediction based on self-interest
– Need to look at all of the influencers on the key decision maker
– 5 people is 120 interactions, 10 people is 3.6 million interactions
– CIA notes that the computer model is right 90% of the time when the experts who provide the inputs are wrong
– Inputs are what what people have a stake in a decision, people say they want, how focused are they on the issue, how much influence do they have

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