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The HoneyComb Paradigm for Research on Collective Human Behavior

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Expertise, Social Influence, and Knowledge Aggregation in Distributed Information Processing.

Asimina Mertzani¹, Jeremy Pitt², Andrzej Nowak³

¹Imperial College London, Department of Electrical Electronic Engineering. asimina.mertzani20@imperial.ac.uk.

Artificial Life

|October 6, 2022

Summary

This study introduces a new algorithm for distributed information processing units (DIPs) based on social influence theory. It enhances group effectiveness and resource conservation by fostering expertise in autonomous systems.

Keywords:

Knowledge aggregation distributed information processing emergence and diversity of expertise multi-agent systems self-organisation social influence

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Area of Science:

Multi-agent systems
Social influence theory
Cyber-physical systems
Socio-technical systems

Background:

Autonomous peers in social, cyber-physical, and socio-technical systems face knowledge aggregation challenges without central authority.
Distributed Information Processing units (DIPs) require effective self-organization mechanisms.
Existing models often lack integration with psychological theories of social influence.

Purpose of the Study:

To specify and implement a novel algorithm for knowledge aggregation in DIPs.
To evaluate the algorithm's effectiveness using the Regulatory Theory of Social Influence (RTSI).
To examine how RTSI enhances DIP performance and resource management.

Main Methods:

Development of a new knowledge aggregation algorithm based on RTSI.
Implementation of a multi-agent simulator, SMARTSIS.
Experimental evaluation using a linear public goods game scenario focused on distributive justice.

Main Results:

The RTSI-based algorithm enhances the effectiveness of multi-agent DIPs.
The approach conserves individual agents' resources while improving group performance.
RTSI facilitates the emergence and divergence of expertise, balancing systemic drivers.

Conclusions:

Psychological theories like RTSI are crucial for developing effective agent-based models.
This approach offers a pathway for improved knowledge management and self-improvement in complex systems.
The study demonstrates the practical application of social psychology in artificial intelligence and systems design.