Controlling Evolutionary Dynamics in Networks: A Case Study

Due to their wide adaptability to different application fields spanning from opinion dynamics to biology, the analysis of evolutionary dynamics is a compelling problem in the science of networks and systems. In this paper, we deal with controlled evolutionary dynamics in networks. We discuss a novel approach to model these phenomena, which enables us to estimate the duration of the process depending on the network topology and on the control policy adopted. In a previous work, we have presented some preliminary results including a feedback control policy to speed up the dynamics. These encouraging results have pushed us toward deeper analysis of the problem. Here, we exhibit some critical issues concerning the feedback control policy originally proposed, which limit its applicability to real-world scenarios, and we address them by proposing a new improved control policy. Finally, using Monte Carlo simulations, we test the effectiveness of our approach to evolutionary dynamics and of the new control policy proposed here, against a real-world scenario, obtaining an extremely promising outcome for our future research.