Abstract
Stability and analysis of multiagent network systems with state-dependent switching typologies have been a fundamental and longstanding challenge in control, social sciences, and many other related fields. These already complex systems become further complicated once one accounts for asymmetry or heterogeneity of the underlying agents/dynamics. Despite extensive progress in analysis of conventional networked decision systems where the network evolution and state dynamics are driven by independent or weakly coupled processes, most of the existing results fail to address multiagent systems where the network and state dynamics are highly coupled and evolve based on status of heterogeneous agents. Motivated by numerous applications of such dynamics in social sciences, in this paper we provide a new direction toward analysis of dynamic networks of heterogeneous agents under complex time-varying environments. As a result we show Lyapunov stability and convergence of several challenging problems from opinion dynamics using a simple application of our framework. In particular, we introduce a new class of asymmetric opinion dynamics, namely, nearest neighbor dynamics, and show how our framework can be used to analyze their behavior. Finally, we extend our results to game-theoretic settings and provide new insights toward analysis of complex networked multiagent systems using exciting field of sequential optimization.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1757-1782 |
| Number of pages | 26 |
| Journal | SIAM Journal on Control and Optimization |
| Volume | 57 |
| Issue number | 3 |
| DOIs | |
| State | Published - Jan 1 2019 |
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Keywords
- Block coordinate descent
- Game theory
- Lyapunov stability
- Multiagent decision systems
- Opinion dynamics
- State-dependent dynamics
- Switching network dynamics
ASJC Scopus subject areas
- Control and Optimization
- Applied Mathematics
Cite this
A simple framework for stability analysis of state-dependent networks of heterogeneous agents. / Etesami, S. Rasoul.
In: SIAM Journal on Control and Optimization, Vol. 57, No. 3, 01.01.2019, p. 1757-1782.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - A simple framework for stability analysis of state-dependent networks of heterogeneous agents
AU - Etesami, S. Rasoul
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Stability and analysis of multiagent network systems with state-dependent switching typologies have been a fundamental and longstanding challenge in control, social sciences, and many other related fields. These already complex systems become further complicated once one accounts for asymmetry or heterogeneity of the underlying agents/dynamics. Despite extensive progress in analysis of conventional networked decision systems where the network evolution and state dynamics are driven by independent or weakly coupled processes, most of the existing results fail to address multiagent systems where the network and state dynamics are highly coupled and evolve based on status of heterogeneous agents. Motivated by numerous applications of such dynamics in social sciences, in this paper we provide a new direction toward analysis of dynamic networks of heterogeneous agents under complex time-varying environments. As a result we show Lyapunov stability and convergence of several challenging problems from opinion dynamics using a simple application of our framework. In particular, we introduce a new class of asymmetric opinion dynamics, namely, nearest neighbor dynamics, and show how our framework can be used to analyze their behavior. Finally, we extend our results to game-theoretic settings and provide new insights toward analysis of complex networked multiagent systems using exciting field of sequential optimization.
AB - Stability and analysis of multiagent network systems with state-dependent switching typologies have been a fundamental and longstanding challenge in control, social sciences, and many other related fields. These already complex systems become further complicated once one accounts for asymmetry or heterogeneity of the underlying agents/dynamics. Despite extensive progress in analysis of conventional networked decision systems where the network evolution and state dynamics are driven by independent or weakly coupled processes, most of the existing results fail to address multiagent systems where the network and state dynamics are highly coupled and evolve based on status of heterogeneous agents. Motivated by numerous applications of such dynamics in social sciences, in this paper we provide a new direction toward analysis of dynamic networks of heterogeneous agents under complex time-varying environments. As a result we show Lyapunov stability and convergence of several challenging problems from opinion dynamics using a simple application of our framework. In particular, we introduce a new class of asymmetric opinion dynamics, namely, nearest neighbor dynamics, and show how our framework can be used to analyze their behavior. Finally, we extend our results to game-theoretic settings and provide new insights toward analysis of complex networked multiagent systems using exciting field of sequential optimization.
KW - Block coordinate descent
KW - Game theory
KW - Lyapunov stability
KW - Multiagent decision systems
KW - Opinion dynamics
KW - State-dependent dynamics
KW - Switching network dynamics
UR - http://www.scopus.com/inward/record.url?scp=85070689863&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85070689863&partnerID=8YFLogxK
U2 - 10.1137/18M1217681
DO - 10.1137/18M1217681
M3 - Article
AN - SCOPUS:85070689863
VL - 57
SP - 1757
EP - 1782
JO - SIAM Journal on Control and Optimization
JF - SIAM Journal on Control and Optimization
SN - 0363-0129
IS - 3
ER -