New Perspectives on Multiagent Dynamics
Abstract
This project is focused on network-based multiagent dynamics. Its main ambition is to uncover basic principles that underlie the emergence of collective behavior from local interaction among autonomous agents. To carry out this ``local-to-global" research program, the plan is to draw on techniques from dynamical systems, statistical mechanics, and algorithms. We intend to target a number of specific applications in opinion dynamics, consensus systems, swarming, iterated learning, and time-varying social networks. The emphasis of the work is on methods that cut horizontally across a diversity of application areas. Indeed, we believe that the weak link in the field of multiagent dynamic is the current dearth of general techniques. Most systems are investigated in an ad hoc fashion. Our guiding objective, therefore, is to build a suite of analytical tools applicable to a broad spectrum of systems. The project consists of two overlapping parts and an applications component. 1. Opinion dynamics How do groups reach consensus without a leader? Under what conditions do they polarize into separate clusters? Most models of opinion dynamics treat agents as points in a metric space that move about under the influence of neighboring agents. This gives us an ideal platform to study network-based dynamics. Our attention will focus on two building blocks: the s-energy and the concept of an algorithmic proof. The former is a new type of generating function associated with dynamic networks (interestingly, it can also be used to rederive mixing bounds for random walks). Algorithmic proofs are protocols for distributed Lyapunov functions. Their investigation can be seen as an effort to build a bridge between the fields of dynamics and data structures. 2. Influence systems As one of the most expressive models of multiagent dynamics, influence systems are the perfect vehicle to investigate the conflict between energy and entropy that drives the bifurcation analysis of many multiagent systems. (Briefly, these seek to model open systems that absorb free energy into work while dissipating heat and producing entropy in the process). A significant part of this effort will be devoted to network-based renormalization, a fundamental tool for reducing the dimension of large systems. This in turn will require further work on network parsing, a new technique we have been developing for the semantic hierarchical clustering of dynamic networks. 3. Applications The s-energy has been found to play a key role in elucidating old questions about bird flocking. Our interest in this area extends beyond bird behavior: indeed, swarming raises deep questions about non-Markovian averaging systems that might lead to useful, general insights into a wide variety of multiagent dynamical systems. We also intend to use the framework of influence systems to study the tradeoff between robustness and evolvability in biology.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Feb 25, 2019
- Source ID
- W911NF1710078
Entities
People
- Bernard Chazelle
Organizations
- Army Contracting Command
- Defense Advanced Research Projects Agency
- Princeton University