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Despite their diversity, the Gulf Cooperation Council (GCC) states share a common threat that stems from their dependency on oil revenues. Though oil is what fueled their economic growth, its extreme price volatility proved to be a major destabilizing force to their path of sustainable and stable economic development. In response, diversification strategies to reduce their exposure were adopted by member countries with varying degrees of success. This paper analyzes the relationship between economic growth and three economic diversification indicators: export diversification index, export quality index and fiscal breakeven oil price. The model’s architectural design is a hybrid system that combines artificial neural networks with genetic training. Connections between the three diversification indicators and economic growth are measured to determine the strength of their transmission pulse to each of the six GCC states and to form inferences on their impact on economic stabilization.

Modeling Diversification and Economic Growth in the GCC Using Artificial Neural Networks