Distributed Computing Column 77 Consensus Dynamics

Abstract

Overview. In this edition of the column, we have an exciting contribution from Luca Becchetti, Andrea Clementi, and Emanuele Natale, who provide an in-depth algorithmic perspective on emergent complexity. Roughly, this area aims to characterize non-trivial emergent properties of complex systems, composed of large numbers of relatively simple agents, which can cooperate to express complex global behaviours. Interestingly, over the past two decades, fundamental processes such as consensus or opinion-formation dynamics have been studied independently by different research communities: for instance, in the Distributed Computing community, these dynamics have been studied in the context of population protocols via discrete-time analysis, whereas, in the Control and Optimization community, similar (or even identical) dynamics have been analyzed via continuous-time processes. One very useful feature in this column's contribution is the fact that it provides a unified view of these results, along with the mathematical background to understand and differentiate the underlying results.