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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Two visualization tools for analyzing agent-based simulations in political science.

R Jordan Crouser, Daniel E Kee, Dong Hyun Jeong

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    Summary
    This summary is machine-generated.

    MDSVis and SocialVis enhance agent-based model analysis. These tools help identify simulation patterns, dominant factors, and relationships across time and space.

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    Area of Science:

    • Computational Social Science
    • Data Visualization
    • Agent-Based Modeling

    Background:

    • Agent-based models (ABMs) generate complex simulation data.
    • Analyzing ABM outputs requires effective visualization and pattern identification tools.
    • Current methods may lack scalability for large simulation spaces or detailed temporal-spatial analysis.

    Purpose of the Study:

    • Introduce MDSVis (Multidimensional Scaling Visualization) for aggregated pattern discovery in ABM simulation spaces.
    • Introduce SocialVis for in-depth analysis of single simulation runs, focusing on time-step and geographic relationships.
    • Enhance the analytical capabilities for researchers working with agent-based models.

    Main Methods:

    • MDSVis utilizes Multidimensional Scaling to visualize the aggregated simulation space, identifying similar patterns.
    • SocialVis provides tools to explore relationships within a single simulation run across different time steps and geographic regions.
    • Both tools aim to facilitate the identification of dominant factors influencing agent behavior and simulation outcomes.

    Main Results:

    • MDSVis enables the discovery of macro-level patterns and similarities across multiple agent-based model simulations.
    • SocialVis allows for detailed micro-level analysis, revealing temporal and spatial dynamics within individual simulations.
    • The combined approach supports a comprehensive understanding of agent behavior and model dynamics.

    Conclusions:

    • MDSVis and SocialVis offer complementary approaches for analyzing agent-based model outputs.
    • These visualization tools improve the interpretability of complex simulation data.
    • The methods facilitate the identification of key drivers and patterns in agent-based modeling research.