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Related Experiment Video

Updated: Nov 11, 2025

The HoneyComb Paradigm for Research on Collective Human Behavior

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

Bayesian collective learning emerges from heuristic social learning.

P M Krafft¹, Erez Shmueli², Thomas L Griffiths³

¹Creative Computing Institute, University of Arts London, London, England, United Kingdom.

|March 26, 2021

Summary

This summary is machine-generated.

This study introduces a Bayesian social sampling model to enhance group decision-making by optimizing information aggregation and solving the exploration-exploitation dilemma. Simple individual learning rules can collectively achieve complex Bayesian rationality for better collective knowledge accumulation.

Keywords:

Bayesian models Big data Collective intelligence Exploration-exploitation dilemma Social learning Wisdom of crowds

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Last Updated: Nov 11, 2025

The HoneyComb Paradigm for Research on Collective Human Behavior

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Published on: January 19, 2019

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

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Published on: October 20, 2022

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Area of Science:

Integrates cognitive science, economics, and evolutionary biology.
Focuses on social learning and collective decision-making dynamics.

Background:

Social learning, using others' decisions, enhances individual outcomes.
Group decision-making faces challenges in information aggregation and the exploration-exploitation dilemma.

Purpose of the Study:

To present a Bayesian social sampling model for optimal information aggregation.
To demonstrate how this model addresses the exploration-exploitation dilemma in group decision-making.
To link population-level Bayesian rationality with individual-level heuristic social learning.

Main Methods:

Developed a Bayesian social sampling model.
Proposed that simple individual learning heuristics can implement population-level Bayesian rationality.
Validated the model using a large-scale dataset from an online financial trading platform.

Main Results:

The proposed model can simultaneously achieve optimal information aggregation.
The model nearly optimally solves the exploration-exploitation dilemma.
Individual-level heuristics effectively function as a distributed algorithm tracking population statistics.

Conclusions:

Bayesian rationality at the population level can emerge from simple individual social learning mechanisms.
This framework offers a novel approach to understanding and improving collective intelligence.
The findings have implications for various fields involving group decision-making and knowledge accumulation.