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A Framework for Considering Comprehensibility in Modeling.

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  • 1Department of Computer Sciences, University of Wisconsin-Madison , Madison, Wisconsin.

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Summary
This summary is machine-generated.

This study introduces a framework to address challenges in model comprehensibility, aiming to improve stakeholder understanding of modeling processes. It explores various facets to identify solutions and opportunities for better comprehension.

Keywords:
data analysishuman-computer interactionmachine learningstatistical modelingvisual analyticsvisualization

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Area of Science:

  • Computer Science
  • Information Science
  • Systems Engineering

Background:

  • Model comprehensibility is crucial for stakeholder engagement and effective decision-making.
  • Existing approaches often lack a structured framework to address diverse comprehensibility challenges.

Purpose of the Study:

  • To present a comprehensive framework for analyzing and addressing comprehensibility challenges in modeling.
  • To guide the exploration of factors influencing stakeholder understanding of models.

Main Methods:

  • A faceted approach is used, organized around key questions: Who, Why, Where, How to help, and How to measure comprehension.
  • The framework considers a broad range of options within each facet.

Main Results:

  • Identifies key dimensions of comprehensibility challenges across the modeling lifecycle.
  • Highlights the utility of a broad perspective in uncovering potential solutions and areas for improvement.

Conclusions:

  • A structured framework enhances the identification of issues and opportunities related to model comprehensibility.
  • Adopting a holistic view is essential for developing effective strategies to improve stakeholder understanding in modeling processes.