Title
On Convergence Rate Of A Continuous-Time Distributed Self-Appraisal Model With Time-Varying Relative Interaction Matrices
Abstract
This paper studies a recently proposed continuous-time distributed self-appraisal model with time-varying interactions among a network of n individuals which are characterized by a sequence of time-varying relative interaction matrices. The model describes the evolution of the social-confidence levels of the individuals via a reflected appraisal mechanism in real time. We show that when the relative interaction matrices are doubly stochastic, the n individuals' self-confidence levels will all converge to 1/n, which indicates a democratic state, exponentially fast under appropriate assumptions, and provide an explicit expression for the convergence rate. Numerical examples are provided to verify the theoretical results and to show that when the relative interaction matrices are stochastic (not doubly stochastic), the social-confidence levels of the individuals may not converge to a steady state.
Year
Venue
Field
2017
2017 IEEE 56TH ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC)
Convergence (routing),Mathematical optimization,Matrix (mathematics),Stochastic process,Rate of convergence,Steady state,Reflected appraisal,Mathematics,Exponential growth
DocType
ISSN
Citations 
Conference
0743-1546
0
PageRank 
References 
Authors
0.34
12
4
Name
Order
Citations
PageRank
Weiguo Xia112215.78
Ji Liu214626.61
Tamer Basar33497402.11
Xi-Ming Sun485062.94