Participatory modelling for poverty alleviation using fuzzy cognitive maps and OWA learning aggregation

Area of Science:

• Decision Science
• Complex Systems Modeling
• Policy Analysis

Background:

• Participatory modeling engages stakeholders in representing complex system dynamics.
• Fuzzy Cognitive Maps (FCMs) are used to build quantitative models for development strategies.
• Aggregating stakeholder knowledge enhances model reliability.

Purpose of the Study:

• To propose a novel aggregation method using learning Ordered Weighted Averaging (OWA) operators for FCMs.
• To compare the performance of learning OWA aggregation against traditional average aggregation.
• To evaluate the theory of change for a major poverty alleviation program in India.

Main Methods:

• Developed a new learning OWA aggregation method for FCM weights.
• Implemented FCMWizard, a web-based tool for participatory modeling.
• Conducted a comparative analysis of proposed and conventional aggregation methods.

Main Results:

• The learning OWA aggregation method offers improved policy outcome evaluation.
• The study validated the approach by analyzing India's poverty alleviation program.
• FCMWizard demonstrated the utility of the new method for policy analysis.

Conclusions:

• The proposed learning OWA aggregation enhances participatory modeling with FCMs.
• This method provides policymakers with better tools for assessing development interventions.
• The approach supports evaluation of social resilience and economic mobility in policy outcomes.