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

Reason and Intuition01:37

Reason and Intuition

6.4K
The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...
6.4K
Schemas01:42

Schemas

11.6K
A schema is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.
11.6K
The Availability Heuristic01:08

The Availability Heuristic

5.9K
A heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. Different types of heuristics are used in different types of situations, and the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):
5.9K
Rational Emotive Behavior Therapy01:24

Rational Emotive Behavior Therapy

44
Cognitive-behavioral therapies (CBTs) are grounded in the belief that our thoughts profoundly influence our emotions and actions. Advocates of CBT emphasize three core assumptions: first, that cognitions are identifiable and measurable; second, that they are central to psychological functioning; and third, that irrational or maladaptive beliefs can be replaced with rational and adaptive ones. This transformative approach to therapy has paved the way for specific models such as Albert...
44
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

4.0K
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...
4.0K
Decision Making: P-value Method01:09

Decision Making: P-value Method

5.3K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
5.3K

You might also read

Related Articles

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

Sort by
Same author

The visual stimuli attributes instrumental for collective-motion-related decision-making in locusts.

PNAS nexus·2024
See all related articles

Related Experiment Video

Updated: Jun 12, 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

9.3K

Swarms can be rational.

Gal A Kaminka1

  • 1Department of Computer Science & Gonda Brain Science Center & BINA Nano-Technology Center Bar Ilan University, Bar Ilan University, Israel.

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|January 29, 2025
PubMed
Summary
This summary is machine-generated.

Cooperative swarms achieve collective goals through individual actions that maximize group utility, even at personal cost. This study shows how local rewards can align individual and collective rationality in swarm systems.

Keywords:
collective intelligencedistributed intelligencegame theorymulti-robot systemsrationalityswarms

More Related Videos

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware
08:13

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware

Published on: December 25, 2017

8.1K
Quantifying Bacterial Surface Swarming Motility on Inducer Gradient Plates
05:57

Quantifying Bacterial Surface Swarming Motility on Inducer Gradient Plates

Published on: January 5, 2022

3.5K

Related Experiment Videos

Last Updated: Jun 12, 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

9.3K
SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware
08:13

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware

Published on: December 25, 2017

8.1K
Quantifying Bacterial Surface Swarming Motility on Inducer Gradient Plates
05:57

Quantifying Bacterial Surface Swarming Motility on Inducer Gradient Plates

Published on: January 5, 2022

3.5K

Area of Science:

  • Multi-disciplinary research spanning biology, sociology, computer science, robotics, physics, and economics.
  • Focus on swarm intelligence and collective behavior systems.

Background:

  • Cooperative swarms exhibit perplexing behavior where individuals act to maximize collective utility, seemingly contradicting individual rationality.
  • Individuals in cooperative swarms make decisions without full knowledge of their impact on the collective.

Purpose of the Study:

  • To resolve the paradox of cooperative swarms where individual actions benefit the collective at personal expense.
  • To demonstrate how individual rationality can be reconciled with collective utility maximization in swarm systems.

Main Methods:

  • Utilizing game theory, machine learning, and robotic simulations to model swarm behavior.
  • Transforming collective utility into locally measurable individual rewards.

Main Results:

  • A novel reward system is proposed where individual actions are rewarded based on interaction resolution speed, individual work time, and impact on others.
  • Independent maximization of these internal rewards through learning leads to a stable equilibrium.
  • This equilibrium ensures that individual learned responses simultaneously maximize both individual rewards and collective utility.

Conclusions:

  • The study reconciles individual and collective rationality within cooperative swarm systems.
  • The proposed mechanism allows individuals to rationally pursue actions that benefit the entire swarm.
  • This framework provides a foundation for designing more effective and coordinated swarm systems.