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

Non-equilibrium in the Cell01:16

Non-equilibrium in the Cell

4.8K
An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...
4.8K
Decision Making01:20

Decision Making

230
Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...
230
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

149
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
149
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

4.1K
The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
4.1K
Neural Regulation01:37

Neural Regulation

39.9K
Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
39.9K
Introduction to Cognitive Psychology01:20

Introduction to Cognitive Psychology

805
Cognitive psychology is the field of psychology dedicated to examining how people think. It attempts to explain how and why we think the way we do by studying the interactions among human thinking, emotion, creativity, language, and problem-solving, as well as other cognitive processes. Cognitive psychology studies how information is processed and manipulated in remembering, thinking, and knowing.
This field emerged in the mid-20th century, following a period dominated by behaviorism, which...
805

You might also read

Related Articles

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

Sort by
Same author

Determination of Molecular Ground State via Short Square Pulses on Superconducting Qubits.

Physical review letters·2025
Same author

Emergent coordination in temporal partitioning congestion games.

PloS one·2024
Same author

Regulating advanced artificial agents.

Science (New York, N.Y.)·2024
Same author

Ensemble dependence of the critical behavior of a system with long-range interaction and quenched randomness.

Physical review. E·2023
Same author

Distribution equality as an optimal epidemic mitigation strategy.

Scientific reports·2022
Same author

Topological synchronization of chaotic systems.

Scientific reports·2022
Same journal

Zero-shot reconstruction of mutant spatial transcriptomes.

Patterns (New York, N.Y.)·2026
Same journal

Dendritic nonlinearities mitigate communication costs.

Patterns (New York, N.Y.)·2026
Same journal

Erratum: Agentic AI as a coordination paradigm in digital health and agri-food systems.

Patterns (New York, N.Y.)·2026
Same journal

Spacing effect improves generalization in biological and artificial systems.

Patterns (New York, N.Y.)·2026
Same journal

A multi-modal foundation model for brain disease diagnosis and medical imaging.

Patterns (New York, N.Y.)·2026
Same journal

DuoMod-Net: Logarithmic balancing and geometric refinement for imbalanced semi-supervised medical image segmentation.

Patterns (New York, N.Y.)·2026
See all related articles

Related Experiment Video

Updated: Sep 10, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

575

Lessons from complex systems science for AI governance.

Noam Kolt1,2, Michal Shur-Ofry1, Reuven Cohen3

  • 1Faculty of Law, Hebrew University, Jerusalem, Israel.

Patterns (New York, N.Y.)
|August 22, 2025
PubMed
Summary
This summary is machine-generated.

Complex adaptive systems principles offer crucial insights for governing artificial intelligence (AI). Applying these lessons helps manage AI

Keywords:
cascading riskscomplex adaptive systemsemergencefeedback loopsregulation and governancescaling

More Related Videos

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

236

Related Experiment Videos

Last Updated: Sep 10, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

575
Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

236

Area of Science:

  • Cross-disciplinary applications of complex adaptive systems science.
  • Integration of insights from physics, biology, and social sciences for AI governance.
  • Leveraging complex systems theory for understanding AI behavior and risks.

Background:

  • Contemporary artificial intelligence (AI) systems exhibit characteristics of complex adaptive systems.
  • AI environments display nonlinear growth, emergent phenomena, and cascading failures.
  • Challenges in AI governance stem from feedback loops and critical infrastructure interdependencies.

Purpose of the Study:

  • To explore the applicability of complex adaptive systems principles to AI governance.
  • To identify key challenges in AI governance illuminated by complex systems science.
  • To propose a framework for complexity-compatible AI governance.

Main Methods:

  • Drawing parallels between complex adaptive systems and AI behavior.
  • Analyzing AI governance challenges through the lens of complex systems.
  • Examining case studies from public health and climate change for governance insights.

Main Results:

  • AI systems and their environments share properties with complex adaptive systems.
  • Deep uncertainty characterizes current efforts to govern AI.
  • Specific AI features like synthetic data feedback loops pose governance challenges.

Conclusions:

  • AI governance requires principles compatible with complex adaptive systems.
  • Proposed desiderata include early/scalable intervention, adaptive institutions, and calibrated risk thresholds.
  • Effective AI governance necessitates adaptive strategies to manage uncertainty and emergent risks.