| This dissertation addresses the issue of influence in self-organizing multi-agent systems by using traffic jams as a prototypical example of self-organized behavior. Specifically, the problem of ascertaining the influence of a set of agents on the ensemble dynamics is addressed through two complementary approaches. In the first approach, discussed in Part 1 of the dissertation, the ability to influence ensemble dynamics is studied as a function of changing agent population demographics. Statistical mechanics-inspired methodologies, such as the master equation and the generalized Ising model, are used to study the effect of introduction of vehicles equipped with adaptive cruise control (Ace) algorithms on the self-organized dynamics of traffic jams. Results indicate mixed positive and negative effects of introduction of Ace-equipped vehicles at various traffic densities.;While this approach can help guide long-term intelligent vehicle deployment strategies on the time scale of years or decades, population demographic control is not a feasible solution for influencing large-scale multi-agent systems on the time scale of minutes or hours. Thus, the second approach, discussed in Part II of this dissertation, addresses the problem by identifying appropriate regions of the state space within which the control efforts exerted by a small set of agents can influence the self-organized dynamics of the ensemble. The methodologies adopted in this approach make use of the kinematic wave theory of traffic flow and the notion of controllability to present the novel concept of influential subspaces. Results indicate that there exists a strong spatial dependence that governs an agent's ability to influence the self-organized dynamics of large-scale multi-agent systems. |
