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

Aggregates Classification01:29

Aggregates Classification

584
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
584
Prediction Intervals01:03

Prediction Intervals

2.9K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.9K
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

961
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
961
Maximum Size of Aggregate01:12

Maximum Size of Aggregate

373
The maximum size of aggregate is defined as the aperture of the sieve retaining 15 percent or more of the particles present in the aggregate sample. The aggregate's maximum size impacts the concrete's water requirement, workability, and strength. Larger aggregates reduce the surface area needing cement paste coverage, which can lower water needs, thereby allowing a decrease in the water-to-cement ratio when the desired workability and richness of the mix are to be maintained, which can...
373
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

165
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
165
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

387
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
387

You might also read

Related Articles

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

Sort by
Same author

Reply to Krefeld-Schwalb et al.: Measuring population heterogeneity requires upholding good scientific practice.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

Vascular reconstructions in living donor kidney transplantation: a single-center experience over the last 17 years.

Frontiers in transplantation·2024
Same author

Heterogeneity in effect size estimates.

Proceedings of the National Academy of Sciences of the United States of America·2024
Same author

Subjective evidence evaluation survey for many-analysts studies.

Royal Society open science·2024
Same author

Competition and moral behavior: A meta-analysis of forty-five crowd-sourced experimental designs.

Proceedings of the National Academy of Sciences of the United States of America·2023
Same author

Acceptance or rejection of welfare migration-an experimental investigation.

SN business & economics·2022
Same journal

Individual and contextual effects of attention in risky choice.

Experimental economics·2025
Same journal

The role of self-confidence in teamwork: experimental evidence.

Experimental economics·2024
Same journal

Task completion without commitment.

Experimental economics·2024
Same journal

On the stability of norms and norm-following propensity: a cross-cultural panel study with adolescents.

Experimental economics·2024
Same journal

Does choice change preferences? An incentivized test of the mere choice effect.

Experimental economics·2023
Same journal

Editorial: Symposium "Pre-results review".

Experimental economics·2023
See all related articles

Related Experiment Video

Updated: Dec 2, 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

9.7K

Aggregation mechanisms for crowd predictions.

Stefan Palan1,2, Jürgen Huber2, Larissa Senninger2

  • 1Department of Banking and Finance, University of Graz, Universitätsstraße 15, 8010 Graz, Austria.

Experimental Economics
|November 2, 2020
PubMed
Summary
This summary is machine-generated.

The wisdom of crowds aggregates information effectively, but market prices from continuous double auctions best predict unknown values compared to other methods like means or medians.

Keywords:
Asymmetric informationInformation aggregationWisdom of crowds

More Related Videos

Generation of Aggregates of Mouse Embryonic Stem Cells that Show Symmetry Breaking, Polarization and Emergent Collective Behaviour In Vitro
11:37

Generation of Aggregates of Mouse Embryonic Stem Cells that Show Symmetry Breaking, Polarization and Emergent Collective Behaviour In Vitro

Published on: November 24, 2015

18.3K
Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
10:52

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Published on: April 13, 2016

9.0K

Related Experiment Videos

Last Updated: Dec 2, 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

9.7K
Generation of Aggregates of Mouse Embryonic Stem Cells that Show Symmetry Breaking, Polarization and Emergent Collective Behaviour In Vitro
11:37

Generation of Aggregates of Mouse Embryonic Stem Cells that Show Symmetry Breaking, Polarization and Emergent Collective Behaviour In Vitro

Published on: November 24, 2015

18.3K
Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
10:52

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Published on: April 13, 2016

9.0K

Area of Science:

  • Decision Sciences
  • Behavioral Economics
  • Information Aggregation

Background:

  • The "wisdom of crowds" effect suggests aggregated forecasts outperform individual experts.
  • Effective aggregation mechanisms are crucial for leveraging collective intelligence.
  • Asymmetric information structures are common in real-world forecasting scenarios.

Purpose of the Study:

  • To compare the efficacy of various aggregation mechanisms in an asymmetric information setting.
  • To determine which aggregation method best harnesses the "wisdom of crowds" for prediction.
  • To analyze how information asymmetry impacts forecasting accuracy and excess returns.

Main Methods:

  • A controlled experiment was conducted to compare aggregation methods.
  • Methods evaluated included arithmetic/geometric means, medians, continuous double auction markets, and sealed bid-offer call markets.
  • Participants possessed different subsets of information to estimate an asset's value.

Main Results:

  • Continuous double auction market prices significantly outperformed all other aggregation methods.
  • The best-informed participants were able to generate excess returns.
  • Less informed participants' forecasts did not significantly contribute to the aggregate's accuracy.

Conclusions:

  • Continuous double auction markets provide a superior mechanism for aggregating dispersed information.
  • Market-based aggregation effectively utilizes collective intelligence, even with asymmetric information.
  • Understanding information distribution is key to optimizing forecasting and market efficiency.