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We simulate the dynamics of diffusion and establishment of norms, variants adopted by the majority of agents, in a large social influence network with scale-free small-world properties. Diffusion is modeled as the probabilistic uptake of one of several competing variants by agents of unequal social standing. We find that novel variants diffuse following an(More)
—Sociological models of human behavior can explain population-level phenomena within social systems; computer modeling can simulate a wide variety of scenarios and allow one to pose and test hypotheses about the social system. Here we model and examine the spread of information through personal conversations in a simulated socio-technical network that(More)
In this paper we describe a multiagent simulation model of human behavior in the aftermath of a hypothetical, large-scale, human-initiated crisis in the center of Washington D.C. Prior studies of this scenario have focused on modeling the physical effects of the attack, such as thermal and blast effects , prompt radiation, and fallout. Casualty and(More)
—Eliminating interactions among individuals is an important means of blocking contagion spread; e.g., closing schools during an epidemic or shutting down electronic communication channels during social unrest. We study contagion blocking in networked populations by identifying edges to remove from a network, thus blocking contagion transmission pathways. We(More)
1.1. The social dynamics of language change Language change is a historical process rooted in synchronic social dynamics. In countless communicative interactions between individuals, novel linguistic variants can emerge, diffuse widely, become integrated into the grammar of the speech community and be transmitted to future generations. When the time course(More)
We describe InterSim, a general purpose flexible framework for simulating graph dynamical systems (GDS) and their generalizations. GDS provide a powerful formalism to model and analyze agent-based systems (ABS) because there is a direct mapping between nodes and edges (which denote interactions) in a GDS and agents and interactions in an ABS, thereby(More)
Disasters affect a society at many levels. Simulation-based studies often evaluate the effectiveness of 1 or 2 response policies in isolation and are unable to represent impact of the policies to coevolve with others. Similarly, most in-depth analyses are based on a static assessment of the "aftermath" rather than capturing dynamics. We have developed a(More)
We analyze and extend a recently proposed model of linguistic diffusion in social networks, to analytically derive time to convergence, and to account for the innovation phase of lexical dynamics in networks. Our new model, the degree-biased voter model with innovation, shows that the probability of existence of a norm is inversely related to innovation(More)