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The Neglected Pieces of Designing Collective Decision-Making Processes.

Yara Khaluf1, Pieter Simoens1, Heiko Hamann2

  • 1IDLab, Ghent University-Imec, Ghent, Belgium.

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|January 27, 2021
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Summary

This study highlights critical overlooked aspects in artificial collective decision-making, focusing on information processing beyond voting mechanisms for more intelligent systems.

Keywords:
collective system design (CSD)decision-making processinformation processingmulti-agent system (MAS)swarm robotics

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Area of Science:

  • Artificial Intelligence
  • Collective Behavior
  • Decision Theory

Background:

  • Autonomous decision-making is crucial for intelligent agents and systems.
  • Current research on collective artificial systems primarily adopts natural systems' models, often focusing on voting.

Purpose of the Study:

  • To identify and emphasize under-addressed components in designing effective collective decision-making mechanisms.
  • To shift focus towards information processing aspects, specifically stimuli and option sets.

Main Methods:

  • Literature review of existing collective decision-making approaches in artificial systems.
  • Conceptual analysis of information processing in decision-making frameworks.

Main Results:

  • Existing models predominantly utilize voting mechanisms, neglecting other vital factors.
  • Information processing, encompassing stimuli reception and option evaluation, is identified as a key area for improvement.

Conclusions:

  • Efficient collective decision-making requires a more comprehensive approach beyond simple voting.
  • Future research should integrate detailed analysis of information processing, including stimuli and option sets, for enhanced artificial system intelligence.