A contraction based, singular perturbation approach to near-decomposability in complex systems

We revisit the classical concept of near-decomposability in complex systems, introduced by Herbert Simon in his foundational article The Architecture of Complexity, by developing an explicit quantitative analysis based on singular perturbations and nonlinear contraction theory. Complex systems are often modular and hierarchic, and a central question is whether the whole system behaves approximately as the "sum of its parts", or whether feedbacks between modules modify qualitatively the modules behavior, and perhaps also generate instabilities. We show that, when the individual nonlinear modules are contracting (i.e., forget their initial conditions exponentially), a critical separation of timescales exists between the dynamics of the modules and that of the macro system, below which it behaves approximately as the stable sum of its parts. Our analysis is fully nonlinear and provides explicit conditions and error bounds, thus both quantifying and qualifying existing results on near-decomposability.

[1]  P. Olver Nonlinear Systems , 2013 .

[2]  Jean-Jacques E. Slotine,et al.  Modularity, evolution, and the binding problem: a view from stability theory , 2001, Neural Networks.

[3]  Domitilla Del Vecchio,et al.  Modular Composition of Gene Transcription Networks , 2014, PLoS computational biology.

[4]  J. Bowen,et al.  Singular perturbation refinement to quasi-steady state approximation in chemical kinetics , 1963 .

[5]  L. A. Segel,et al.  The Quasi-Steady-State Assumption: A Case Study in Perturbation , 1989, SIAM Rev..

[6]  H. Kennedy,et al.  A Large-Scale Circuit Mechanism for Hierarchical Dynamical Processing in the Primate Cortex , 2015, Neuron.

[7]  Jean-Jacques E. Slotine,et al.  Modular stability tools for distributed computation and control , 2003 .

[8]  Domitilla Del Vecchio,et al.  A Contraction Theory Approach to Singularly Perturbed Systems , 2011, IEEE Transactions on Automatic Control.

[9]  D. Lathrop Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry, and Engineering , 2015 .

[10]  Richard F. Betzel,et al.  Modular Brain Networks. , 2016, Annual review of psychology.

[11]  HERBERT A. SIMON,et al.  The Architecture of Complexity , 1991 .

[12]  Alessandro Vespignani,et al.  Network Science: Theory, Tools, and Practice , 2012 .

[13]  Jean-Jacques E. Slotine,et al.  On Contraction Analysis for Nonlinear Systems Analyzing stability differentially leads to a new perspective on nonlinear dynamic systems , 1999 .

[14]  Weiping Li,et al.  Applied Nonlinear Control , 1991 .

[15]  Domitilla Del Vecchio,et al.  Retroactivity Attenuation in Bio-Molecular Systems Based on Timescale Separation , 2011, IEEE Transactions on Automatic Control.

[16]  Mark E. J. Newman,et al.  The Structure and Function of Complex Networks , 2003, SIAM Rev..