Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Groupthink01:34

Groupthink

50.2K
When in group settings, we are often influenced by the thoughts, feelings, and behaviors around us. Groupthink is another phenomenon of conformity where modification of the opinions of members in a group aligns with what they believe is the group consensus (Janis, 1972). In such situations, the group often takes action that individuals would not perform outside the group setting because groups make more extreme decisions than individuals do. Moreover, groupthink can hinder opposing trains of...
50.2K
Group Polarization01:01

Group Polarization

39.2K
Group polarization is the strengthening of an original group attitude following the discussion of views within a group (Teger & Pruitt, 1967). That is, if a group initially favors a viewpoint, after discussion the group consensus is likely a stronger endorsement of the viewpoint. Conversely, if the group was initially opposed to a viewpoint, group discussion would likely lead to stronger opposition.
39.2K
Feedback control systems01:26

Feedback control systems

733
Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
733
Open and closed-loop control systems01:17

Open and closed-loop control systems

1.8K
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...
1.8K
Control Systems01:10

Control Systems

1.9K
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...
1.9K
Feedback Inhibition00:46

Feedback Inhibition

57.4K
Biochemical reactions are occurring constantly in cells, converting starting substances to different products, usually with the help of enzymes that speed the reactions. Without enzymes, it would take far too long for most reactions to occur to be useful to the cell!
57.4K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Parents' Satisfaction with Juvenile Idiopathic Arthritis Care: Findings from a Cohort of Italian Children Using the JAMAR Questionnaire.

Medicina (Kaunas, Lithuania)·2025
Same author

Cardiogenic shock in a patient with combined severe aortic and mitral regurgitation treated by a totally percutaneous approach: a case report.

European heart journal. Case reports·2025
Same author

Emergence and Control of Synchronization in Networks with Directed Many-Body Interactions.

Physical review letters·2023
Same author

A model-based opinion dynamics approach to tackle vaccine hesitancy.

Scientific reports·2022
Same author

Gastrointestinal involvement in paediatric COVID-19 - from pathogenesis to clinical management: A comprehensive review.

World journal of gastroenterology·2021
Same author

Pediatric admissions to emergency departments of North-Western Italy during COVID-19 pandemic: A retrospective observational study.

The Lancet regional health. Europe·2021
Same journal

Graph convolutional networks: a comprehensive review.

Computational social networks·2023
Same journal

Modeling the transmission dynamics of racism propagation with community resilience.

Computational social networks·2021
Same journal

A model for the co-evolution of dynamic social networks and infectious disease dynamics.

Computational social networks·2021
Same journal

Influence spreading model used to analyse social networks and detect sub-communities.

Computational social networks·2018
Same journal

Network partitioning algorithms as cooperative games.

Computational social networks·2018
Same journal

Social learning for resilient data fusion against data falsification attacks.

Computational social networks·2018
See all related articles

Related Experiment Video

Updated: Feb 16, 2026

Continuous Theta Burst Stimulation of the Posterior Medial Frontal Cortex to Experimentally Reduce Ideological Threat Responses
06:42

Continuous Theta Burst Stimulation of the Posterior Medial Frontal Cortex to Experimentally Reduce Ideological Threat Responses

Published on: September 28, 2018

12.3K

Steering opinion dynamics via containment control.

Pietro DeLellis1, Anna DiMeglio1, Franco Garofalo1

  • 1Department of Electrical Engineering and Information Technology, University of Naples Federico II, via Claudio, 21, 80125 Napoli, Italy.

Computational Social Networks
|December 22, 2017
PubMed
Summary
This summary is machine-generated.

This study models opinion containment using dynamic strategies. A stochastic approach is effective for small networks, while topology-aware methods work best for large, complex social networks.

Keywords:
Agent-based modelsComplex networksContainment controlExtremistOpinion dynamicsPinning selectionProximity

More Related Videos

Modeling Verbal Behavior Deficits with the Stimulus Control Ratio Equation, SCoRE
06:57

Modeling Verbal Behavior Deficits with the Stimulus Control Ratio Equation, SCoRE

Published on: May 14, 2019

10.9K
Irrelevant Stimuli and Action Control: Analyzing the Influence of Ignored Stimuli via the Distractor-Response Binding Paradigm
12:12

Irrelevant Stimuli and Action Control: Analyzing the Influence of Ignored Stimuli via the Distractor-Response Binding Paradigm

Published on: May 14, 2014

11.0K

Related Experiment Videos

Last Updated: Feb 16, 2026

Continuous Theta Burst Stimulation of the Posterior Medial Frontal Cortex to Experimentally Reduce Ideological Threat Responses
06:42

Continuous Theta Burst Stimulation of the Posterior Medial Frontal Cortex to Experimentally Reduce Ideological Threat Responses

Published on: September 28, 2018

12.3K
Modeling Verbal Behavior Deficits with the Stimulus Control Ratio Equation, SCoRE
06:57

Modeling Verbal Behavior Deficits with the Stimulus Control Ratio Equation, SCoRE

Published on: May 14, 2019

10.9K
Irrelevant Stimuli and Action Control: Analyzing the Influence of Ignored Stimuli via the Distractor-Response Binding Paradigm
12:12

Irrelevant Stimuli and Action Control: Analyzing the Influence of Ignored Stimuli via the Distractor-Response Binding Paradigm

Published on: May 14, 2014

11.0K

Area of Science:

  • Social network analysis
  • Opinion dynamics
  • Computational social science

Background:

  • Bounded confidence models explain opinion formation based on similarity.
  • Confirmation bias influences individuals to engage with similar opinions.
  • Existing models often assume uniform confidence levels, which may not reflect real-world dynamics.

Purpose of the Study:

  • To model opinion containment as a control problem.
  • To investigate how varying confidence thresholds affect influence in different societal structures.
  • To compare the effectiveness of different containment strategies in artificial societies.

Main Methods:

  • Developed a containment control model for opinion dynamics.
  • Incorporated opinion-dependent confidence thresholds, varying with societal extremism.
  • Simulated three containment strategies (stochastic time-varying pinning, static topology-based) across artificial societies of varying extremism and size.

Main Results:

  • In small networks, a stochastic, topology-agnostic strategy outperformed static, topology-aware methods.
  • In large networks, strategies leveraging network topology were more effective.
  • The effectiveness of strategies varied based on the level of societal extremism and network size.

Conclusions:

  • Opinion containment strategies must account for opinion-dependent confidence and network characteristics.
  • The choice between dynamic and static influence strategies depends critically on network scale.
  • This research offers insights into controlling opinion spread in diverse social structures.