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This study introduces a dual-space agent and information modeling framework (AIM) to analyze system dynamics. The AIM framework enhances agent-based models (ABMs) by separating information and material spaces for clearer interaction analysis.

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Area of Science:

  • Complex Systems Modeling
  • Information Theory
  • Agent-Based Modeling

Background:

  • Understanding internal system interactions is crucial for accurate dynamic modeling.
  • Existing models often lack explicit differentiation between information and material aspects of systems.

Purpose of the Study:

  • To explore finer categories of information definition.
  • To develop a novel framework for modeling systems with distinct information and material spaces.
  • To enhance analytical methods for agent-based models (ABMs).

Main Methods:

  • Development of a dual-space agent and information modeling framework (AIM).
  • Explicitly distinguishing between an information space and a material space.
  • Analyzing system evolution and interactions within this dual-space construct.

Main Results:

  • The dual-space AIM framework allows independent and interactive evolution of information and material spaces.
  • Analysis in the dual-space provides clearer insights into system interactions and dynamics.
  • Demonstrated applicability across diverse examples like money distribution and artificial stock markets.

Conclusions:

  • The dual-space AIM framework offers a powerful new approach for modeling and analyzing interactive and intelligent systems.
  • This methodology improves the clarity and depth of analysis for agent-based models.
  • The framework shows broad applicability in understanding complex system behaviors.