How Collective Intelligence Emerges in a Crowd of People Through Learned Division of Labor: A Case Study

TL;DR

This paper explores how self-organized division of labor among individuals in a large crowd leads to the emergence of collective intelligence, using theoretical analysis, simulations, and a case study involving human players controlling an avatar.

Contribution

It introduces a novel framework linking division of labor to collective intelligence, formulates the problem as a stability analysis of Markov Jump Linear Systems, and proposes methods for individuals to learn social roles.

Findings

Division of labor fosters collective intelligence independently of external stimuli.

A stability condition for CI emergence is formulated using MJLS.

A distributed method enables individuals to learn optimal social roles.

Abstract

This paper investigates the factors fostering collective intelligence (CI) through a case study of *LinYi's Experiment, where over 2000 human players collectively controll an avatar car. By conducting theoretical analysis and replicating observed behaviors through numerical simulations, we demonstrate how self-organized division of labor (DOL) among individuals fosters the emergence of CI and identify two essential conditions fostering CI by formulating this problem into a stability problem of a Markov Jump Linear System (MJLS). These conditions, independent of external stimulus, emphasize the importance of both elite and common players in fostering CI. Additionally, we propose an index for emergence of CI and a distributed method for estimating joint actions, enabling individuals to learn their optimal social roles without global action information of the whole crowd.