Abstract
Abstract
Collective dynamics is ubiquitous in various physical, biological, and social systems, where simple local interactions between individual units lead to complex global patterns. A common feature of diverse collective behaviors is that the units exhibit either convergent or divergent evolution in their behaviors, i.e. becoming increasingly similar or distinct, respectively. The associated dynamics changes across time, leading to complex consequences on a global scale. In this study, we propose a generalized Laplacian dynamics model to describe both convergent and divergent collective behaviors, where the trends of convergence and divergence compete with each other and jointly determine the evolution of global patterns. We empirically observe non-trivial phase-transition-like phenomena between the convergent and divergent evolution phases, which are controlled by local interaction properties. We also propose a conjecture regarding the underlying phase transition mechanisms and outline the main theoretical difficulties for testing this conjecture. Overall, our framework may serve as a minimal model of collective behaviors and their intricate dynamics.
Funder
the Tsinghua University Initiative Scientific Research Program
the Artificial and General Intelligence Research Program of Guo Qiang Research Institute at Tsinghua University
Subject
Artificial Intelligence,Computer Networks and Communications,Computer Science Applications,Information Systems