Method for the estimation of institutional quality indexes using fuzzy logic

Area of Science:

• Economics
• Social Sciences
• Data Science

Background:

• Measuring institutional environments is complex and challenging.
• Existing methods often rely on secondary, macro-level data.
• There is a need for robust methods to quantify institutional factors.

Purpose of the Study:

• To present a novel method for estimating institutional environment indexes using fuzzy modeling.
• To demonstrate the application of fuzzy inference systems for index creation.
• To address the limitations of traditional institutional analysis methods.

Main Methods:

• Utilized a Mamdani expert system of the MIMO (Multiple-Input, Multiple-Output) type.
• Developed a fuzzy inference system to process complex institutional data.
• Applied the method to primary data collected at the firm level.

Main Results:

• Successfully generated institutional environment indexes for the aquaculture sector in Brazil.
• Demonstrated the effectiveness of fuzzy modeling with firm-level primary data.
• Presented institutional ambient scores related to tilapia production.

Conclusions:

• Fuzzy modeling offers a viable approach to quantify complex institutional environments.
• The method enhances the analysis of institutional factors using primary data.
• This approach can benefit researchers and policymakers studying economic performance.