Overview 2010 of ARL Program on Network Science for Human Decision Making

Abstract

The Army Research Laboratory program on the Network Science of Human Decision Making brings together researchers from a variety of disciplines to work on a complex research problem that defies confinement within any single discipline. Consequently, new and rewarding solutions have been obtained for a problem of importance to society and the Army, that being, the human dimension of complex networks. This program investigates the basic research foundation of a science of networks supporting the linkage between the cognitive and social domains as they relate to human decision making. The research strategy extends recent methods of non-equilibrium statistical physics to non-stationary, renewal stochastic processes characteristic of the interactions among nodes in complex networks. The theoretical analyses of complex networks, although mathematically rigorous, often elude analytic solutions and require simulation and computation to analyze the underlying dynamic process. The information transfer between two complex networks is calculated using the principle of complexity management as well as direct numerical calculation of the decision making model developed within the project.

Keywords: complex networks; decision making model; principle complexity management.

Figure 1

The cross-correlation cube. The asymptotic cross-correlation function defined by Eq. 3 is graphed as a function of the two power–law indices of the perturbed network S and the perturbing network P. (From West and Grigolini, , with permission.)

Figure 2

(A) Temporal evolution of a single unit and (B) of the global order parameter for the DMM realized on a square lattice with L = 50, g₀ = 0.01, and K = 1.70. Notice the different time scales on the two plots. (From Turalska et al., , with permission.)

Figure 3

The phase diagram for the global variable ξ_eq. The thin solid line and dashed line are theoretical predictions for the fully connected and 2D regular networks, respectively. In both cases L = ∞ and the latter case is the Onsager prediction for a 2D regular lattice. The thick solid line corresponds to the global states observed for a 2D regular lattice with L = 100 and g₀ = 0.01. Periodic boundary conditions were applied. (From Turalska et al., , with permission.)

Figure 4

Survival probability function Ψ(τ) for the global order parameter evaluated on a 2D lattice with L = 50, g₀ = 0.01 and increasing values of the coupling constant K. The straight line corresponds to a slope of −0.50, namely μ = 1.5 since Ψ(τ)∝1/τ^μ − 1. (From Turalska et al., , with permission.)