Computational model to predict human behavior developed by U.S. Army lab
NewsOctober 23, 2018
U.S. ARMY RESEARCH LABORATORY--ABERDEEN PROVING GROUND, Md. Researchers at the U.S. Army Research Lab (ARL) have developed for the first time an analytic model to show how groups of people influence individual behavior.
The breakthrough is the product of ongoing research to model how an individual adapts to group behavior, under the umbrella of the ARL program in network science that seeks to determine collective group behavior emerging from the dynamic behavior of individuals. In the past, the collaborative work of Drs. Bruce West, a senior scientist at the Army Research Office, and Malgorzata Turalska, a postdoctoral researcher at ARL, focused on constructing and interpreting the output of large-scale computer models of complex dynamic networks from which collective properties -- including swarming, collective intelligence, and decision-making -- could be determined.
"Dr. Turalska and I had developed and explored a network model of decision-making for a number of years," said Dr. West. "But recently it occurred to us to change the question from 'How does the individual change group behavior?' to 'How does the group change individual behavior?' Turning the question on its head allowed us to pursue the holy grail of social science for the Army, which has been to find a way to predict the sensitivity of individuals to persuasion, propaganda, and outright deception. Models developed for this purpose have evolved to the point that they require large-scale calculations that are as complex and as difficult to interpret as the results of psychological experiments involving humans. Consequently, the present study suggests a way to bypass these time-consuming calculations and represent the sought-for sensitivity in a single parameter."
The calculus used by the ARL researchers has, over the past decade, been applied to complex physical problems such as turbulence, the behavior of non-Newtonian fluids, and the relaxation of disturbances in viscoelastic materials; no one, however, had previously applied fractional operators to the description and interpretation of social/psychological dynamic phenomena. The idea of collapsing the effect of the interactions between members of a social group into a single parameter that determines the level of influence of the collective on the individual has never previously been accomplished mathematically.
Dr. West asserts that this research opens the door to a new area of study: that of the dovetailing of network science and fractional calculus, where the large-scale numerical calculations of the dynamics of complex networks can be represented through the noninteger indices of derivatives. It may also, West sayd, suggest a new approach to artificial intelligence in which memory is incorporated into the dynamic structure of neural networks.
