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BIBO stability of continuous and discrete -time systems

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Transfer Function in Control Systems01:21

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Related Experiment Video

Updated: May 12, 2026

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
06:04

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

Published on: February 14, 2025

Evolution of controllability in interbank networks.

Danilo Delpini1, Stefano Battiston, Massimo Riccaboni

  • 1Department of Economics and Business-DiSEA, University of Sassari, Sassari, Italy.

Scientific Reports
|April 10, 2013
PubMed
Summary

Statistical physics reveals crucial financial institutions for interbank market stability. Policies must adapt to network time scales, as key drivers are not always the largest or most connected institutions.

Related Experiment Videos

Last Updated: May 12, 2026

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
06:04

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

Published on: February 14, 2025

Area of Science:

  • Statistical physics
  • Network science
  • Financial economics

Background:

  • Complex networks offer theoretical tools for policymakers.
  • Interbank markets are complex systems with dynamic structures and multi-timescale interactions.

Purpose of the Study:

  • To extend network controllability concepts for identifying critical financial institutions (drivers) in interbank markets.
  • To analyze how time resolution affects the identification of these crucial drivers.

Main Methods:

  • Application of network controllability theory to interbank market data.
  • Analysis of system dynamics across various time scales.
  • Investigation of correlations between drivers and institutional characteristics (connectivity, lending volume).

Main Results:

  • A scale-free decay in the proportion of drivers was observed as time resolution increased.
  • Crucial drivers were frequently not the most connected institutions (hubs) or largest lenders.
  • Findings challenge conventional assumptions about systemic importance in financial networks.

Conclusions:

  • Policy effectiveness in financial markets is dependent on aligning interventions with relevant time scales.
  • Identifying systemic drivers requires methods beyond simple measures of connectivity or size.
  • Quantitative indicators derived from network analysis can inform regulatory supervision and intervention strategies.