Related Experiment Video
Updated: May 12, 2026
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
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.
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.
Related Concept Videos
01:24BIBO stability of continuous and discrete -time systems
System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system.
01:21Transfer Function in Control Systems
The transfer function is a fundamental concept in the analysis and design of linear time-invariant (LTI) systems. It offers a concise way to understand how a system responds to different inputs in the frequency domain. It serves as a bridge between the time-domain differential equations that describe system dynamics and the frequency-domain representation that facilitates easier manipulation and analysis.
To derive the transfer function, consider a general nth-order linear time-invariant...
To derive the transfer function, consider a general nth-order linear time-invariant...
01:10Control Systems
Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
At the heart...
At the heart...
01:28Global Regulatory Systems
Global regulatory systems in bacteria enable rapid and coordinated responses to environmental changes by integrating sensory inputs with gene expression, ensuring efficient adaptation to fluctuating conditions. Key global regulatory mechanisms include regulons, two-component systems, sigma factors, and secondary messengers.Regulons and Global RegulatorsA regulon is a collection of genes and operons controlled by a common global regulator. These regulators enable bacteria to prioritize resource...
01:25Multimachine Stability
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
01:17Open and closed-loop control systems
Control systems are foundational elements in automation and engineering. They are broadly categorized into open-loop and closed-loop systems. These classifications hinge on the presence or absence of feedback mechanisms, significantly influencing the system's performance, complexity, and application.
An open-loop control system operates without feedback from the output. It consists of two primary elements: the controller and the controlled process. The controller receives an input signal and...
An open-loop control system operates without feedback from the output. It consists of two primary elements: the controller and the controlled process. The controller receives an input signal and...