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Using cause-effect graphs to elicit expert knowledge for cross-impact balance analysis.

Ivana Stankov1,2, Andres F Useche3,4, Jose D Meisel4,5

  • 1Urban Health Collaborative, Dornsife School of Public Health, Drexel University, 3600 Market St, Philadelphia, PA 19104, USA.

Methodsx
|September 24, 2021
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Summary

This study introduces a new visual questionnaire to gather expert insights for cross-impact balance (CIB) analysis. It helps identify plausible future scenarios by mapping relationships between system factors.

Keywords:
Chronic diseaseComplex SystemsDietEpidemiologyFood environmentScenario analysisSystems thinkingTransportation systemUrban Health

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Area of Science:

  • Systems analysis
  • Futures studies
  • Urban planning

Background:

  • Cross-impact balance (CIB) analysis uses expert knowledge to identify plausible future system scenarios.
  • Scenarios, or storylines, offer qualitative insights into factor interdependencies.
  • Existing methods for eliciting this knowledge can be cumbersome.

Purpose of the Study:

  • To present a novel, visually oriented questionnaire for eliciting expert knowledge for CIB analysis.
  • To facilitate the identification of relationships between key system factors.
  • To improve the efficiency and clarity of CIB analysis data collection.

Main Methods:

  • Development of a visually oriented questionnaire using standardized cause-effect graphical profiles.
  • Experts select graphical profiles representing linear and non-linear relationships between factor pairs.
  • Translation of graphical selections into data suitable for CIB analysis.

Main Results:

  • The questionnaire provides a structured method for experts to define relationship strengths and types.
  • The visual approach simplifies the complex task of mapping factor interdependencies.
  • An applied example demonstrates the questionnaire's utility in urban health analysis for Latin American cities.

Conclusions:

  • The novel visual questionnaire enhances the process of knowledge elicitation for CIB analysis.
  • This method offers a more intuitive and efficient way to gather data for scenario building.
  • The approach is applicable to complex systems, including urban health in diverse geographical contexts.