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

Mathematical Modeling: Problem Solving01:29

Mathematical Modeling: Problem Solving

521
Mathematical modeling transforms real-world scenarios into mathematical expressions, allowing for structured problem-solving and analysis. This process involves defining the situation, assigning variables to measurable quantities, selecting an appropriate model, and solving the resulting equation. Such models are invaluable in finance, providing precise methods to evaluate investments, loans, and repayment structures.A widely used example is the calculation of fixed monthly payments on a loan,...
521
Problem-Solving01:29

Problem-Solving

628
Effective problem-solving consists of two steps: 1. identifying the problem and 2. selecting the appropriate problem-solving strategy (i.e., a plan of action used to find a solution). Humans use four problem-solving strategies:
628
Principle of Virtual Work: Problem Solving01:13

Principle of Virtual Work: Problem Solving

1.8K
The principle of virtual work is an essential concept in the field of mechanics and engineering. This is used to solve problems related to the equilibrium of a structure or system. It is based on the assumption that if a system is in equilibrium, the work done by all the forces during a virtual displacement is zero. This principle is applied by considering virtual displacements of the system and the corresponding work done by internal and external forces.
To apply the principle of virtual work,...
1.8K
Principle of Moments: Problem Solving01:30

Principle of Moments: Problem Solving

1.3K
The principle of moments is a fundamental concept in physics and engineering. It refers to the balancing of forces and moments around a point or axis, also known as the pivot. This principle is used in many real-life scenarios, including construction, sports, and daily activities like opening doors and pushing objects.
One such scenario involves a pole placed in a three-dimensional system with a cable attached. When a tension is applied to the cable, the moment about the z-axis passing through...
1.3K
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

1.2K
Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
1.2K
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

1.3K
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...
1.3K

You might also read

Related Articles

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

Sort by
Same author

Conspiracy as a Defense: The Role of Defensive Styles in the Endorsement of Conspiracy Theories.

Psychological reports·2026
Same author

Orthorexia Nervosa and Its Associations with Novel Foods and Body Image Concerns.

Behavioral sciences (Basel, Switzerland)·2025
Same author

Psychopathological Correlates of Dysfunctional Smartphone and Social Media Use: The Role of Personality Disorders in Technological Addiction and Digital Life Balance.

European journal of investigation in health, psychology and education·2025
Same author

Development and Validation of the Fomsumerism Scale (FOMS): A New Measure for Fear of Missing Out-Driven Consumerism.

Psychological reports·2025
Same author

Expectancy Violation: Climate Change Associations May Reveal Underlying Brain-Evoked Responses of Implicit Attitudes.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

The complexity of caffeine's effects on regular coffee consumers.

Heliyon·2025

Related Experiment Video

Updated: Mar 30, 2026

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

9.9K

Modeling crowdsourcing as collective problem solving.

Andrea Guazzini1,2, Daniele Vilone3, Camillo Donati1

  • 1Department of Science of Education and Psychology, University of Florence, Florence, Italy.

Scientific Reports
|November 11, 2015
PubMed
Summary

This study explores crowdsourcing effectiveness using a modeling framework. Findings reveal an optimal group size based on problem difficulty and collectivism, enhancing crowdsourcing potential.

More Related Videos

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

6.5K
The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm
06:18

The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm

Published on: October 20, 2022

2.7K

Related Experiment Videos

Last Updated: Mar 30, 2026

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

9.9K
The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

6.5K
The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm
06:18

The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm

Published on: October 20, 2022

2.7K

Area of Science:

  • Social Sciences
  • Computer Science
  • Behavioral Economics

Background:

  • Crowdsourcing leverages collective intelligence from numerous participants via modern information technologies.
  • Despite widespread use, the full potential of crowdsourcing remains underexplored.
  • Understanding factors influencing crowdsourcing effectiveness is crucial for optimizing its application.

Purpose of the Study:

  • To introduce a modeling framework for analyzing crowdsourcing effectiveness.
  • To investigate the relationship between crowdsourcing group size, problem difficulty, and collectivism.
  • To identify optimal conditions for maximizing crowdsourcing outcomes.

Main Methods:

  • Development of a theoretical modeling framework.
  • Simulation of crowdsourcing scenarios with varying participant numbers and problem complexities.
  • Analysis of the impact of collectivism on group performance.

Main Results:

  • An intricate relationship exists between the number of participants and problem difficulty.
  • The study identifies an optimal size for crowdsourced groups based on these factors.
  • Collectivism significantly influences the effectiveness of crowdsourcing efforts.

Conclusions:

  • The developed framework provides insights into optimizing crowdsourcing strategies.
  • Findings suggest that tailored group sizes and consideration of cultural dimensions like collectivism are key to maximizing crowdsourcing success.
  • This research contributes to a deeper understanding of crowdsourcing dynamics in modern applications.