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

Random Sampling Method01:09

Random Sampling Method

13.5K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
13.5K
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

4.7K
In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
4.7K
Randomized Experiments01:13

Randomized Experiments

8.5K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
8.5K
Masking and Demasking Agents01:19

Masking and Demasking Agents

3.0K
EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on...
3.0K
Law of Independent Assortment02:03

Law of Independent Assortment

60.1K
While Mendel’s Law of Segregation states that the two alleles for one gene are separated into different gametes, a different question of how different genes are inherited remains. For example, is the gene for tall plants inherited with the gene for green peas? Mendel asked this question by experimenting with a dihybrid cross; a cross in which both parents are homozygous for two distinct traits resulting in an F1 generation that are heterozygous for both traits.
60.1K
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

875
A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of the...
875

You might also read

Related Articles

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

Sort by
Same author

5E management protocol for enhanced recovery after total knee arthroplasty: stratified RCT.

Journal of orthopaedic surgery and research·2026
Same author

Large language models instantiate evolutionarily robust strategies of cooperation.

PNAS nexus·2026
Same author

Ocean acidification exacerbates UVR-induced inhibition of photosystem II and I in Corallina officinalis.

Journal of photochemistry and photobiology. B, Biology·2026
Same author

Bivalent impact of social networks on overarming: Insights on the alignment between social and individual interests.

Science advances·2026
Same author

Direct Photocatalytic Conversion of Methane to Acetone Through a Synergistic Ta Single-Atom/Ga Lewis Acid Catalyst.

Angewandte Chemie (International ed. in English)·2026
Same author

Opioid Receptor Independent Effects of Opioid Peptides.

Current reviews in clinical and experimental pharmacology·2026

Related Experiment Video

Updated: Nov 8, 2025

The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

9.6K

Random choices facilitate solutions to collective network coloring problems by artificial agents.

Matthew I Jones1, Scott D Pauls1, Feng Fu1,2

  • 1Department of Mathematics, Dartmouth College, 27 N. Main Street, 6188 Kemeny Hall, Hanover, NH 03755, USA.

Iscience
|April 19, 2021
PubMed
Summary

Adding random choices to artificial agents improves collective action problem-solving. This behavioral randomness is key for distributed greedy algorithms to overcome local minima and achieve global coordination.

Keywords:
Artificial IntelligenceComputer ScienceHuman-Computer Interaction

More Related Videos

Manipulation of Color Patterns in Jumping Spiders for Use in Behavioral Experiments
09:03

Manipulation of Color Patterns in Jumping Spiders for Use in Behavioral Experiments

Published on: May 21, 2019

9.8K
A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
06:28

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

Published on: August 26, 2018

6.1K

Related Experiment Videos

Last Updated: Nov 8, 2025

The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

9.6K
Manipulation of Color Patterns in Jumping Spiders for Use in Behavioral Experiments
09:03

Manipulation of Color Patterns in Jumping Spiders for Use in Behavioral Experiments

Published on: May 21, 2019

9.8K
A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
06:28

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

Published on: August 26, 2018

6.1K

Area of Science:

  • Artificial intelligence
  • Network science
  • Game theory

Background:

  • Collective action problems require global coordination.
  • Noisy agents can improve human performance in coordination games.
  • Understanding behavioral randomness is crucial for optimizing collective performance.

Purpose of the Study:

  • To analyze the impact of behavioral randomness in artificial agents on solving network coloring problems.
  • To provide analytical insights into how random choices affect collective performance.
  • To identify optimal strategies for distributed greedy algorithms.

Main Methods:

  • Studied myopic artificial agents using local information and random decision updates.
  • Implemented heuristic reasoning with random choices at various stages.
  • Analyzed the efficacy of resolving color conflicts based on agent behavior and population characteristics.

Main Results:

  • The effectiveness of resolving color conflicts depends on the implementation of random behavior.
  • Specific population characteristics influence the success of random choices.
  • Distributed greedy optimization algorithms benefit from occasional random exploration.

Conclusions:

  • Behavioral randomness in artificial agents is essential for overcoming local minima.
  • Combining local information exploitation with random exploration enhances global coordination.
  • The findings offer insights for designing more effective distributed algorithms for collective action problems.