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Related Experiment Video

Updated: Feb 24, 2026

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

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

Published on: October 20, 2022

Reply & Supply: Efficient crowdsourcing when workers do more than answer questions.

Thomas C McAndrew¹, Elizaveta A Guseva¹, James P Bagrow¹

¹Mathematics & Statistics, Vermont Complex Systems Center, University of Vermont, Burlington, Vermont, United States of America.

|August 15, 2017

Summary

This summary is machine-generated.

This study introduces algorithms to manage growing question sets in crowdsourcing. These methods efficiently distribute workers to explore new questions and answer existing ones, mitigating bias and ensuring reliable crowd answers.

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Area of Science:

Computer Science
Human-Computer Interaction
Social Computing

Background:

Crowdsourcing leverages large workforces for distributed tasks.
Workers can offer valuable experience and creativity beyond simple task completion.
Growing question sets in crowdsourcing introduce bias towards earlier questions.

Purpose of the Study:

To develop efficient crowdsourcing methods for dynamic, growing question sets.
To mitigate the inherent bias favoring early questions in crowdsourced analyses.
To enable exploration of unbounded question sets while maintaining answer confidence.

Main Methods:

Modeling question sets as networks of interrelated questions.
Developing algorithms for efficient worker distribution between new question exploration and current question answering.
Conducting experiments and simulations to validate algorithmic performance.

Main Results:

Algorithms effectively curtail growth bias in question sets.
Efficient worker distribution balances exploration and exploitation of questions.
Demonstrated ability to explore unbounded question sets without compromising answer reliability.

Conclusions:

The proposed algorithms enable efficient and unbiased crowdsourcing with growing question sets.
Effective management of worker allocation is key to harnessing crowdsourcing potential.
This approach enhances the reliability and scope of crowdsourced information.