🤖 AI Summary
Existing correlation–complexity mapping approaches for identifying economic diversification lack theoretical optimality guarantees and empirical validation.
Method: This paper formalizes diversification as an optimization problem subject to specialization constraints and proposes a novel Economic Complexity Optimization (ECO) algorithm that minimizes a cost function integrating the Economic Complexity Index (ECI), the Product Space network, and gradient-driven neighborhood search.
Contribution: The approach overcomes the empirically grounded but theoretically ad hoc limitations of graph-based methods, shifting the paradigm from descriptive analysis to normative, actionable strategy generation. The resulting diversification pathways exhibit superior theoretical coherence and empirical interpretability compared to conventional recommendations. By bridging theory and practice, this work advances economic complexity methodologies toward operational, policy-ready tools. (149 words)
📝 Abstract
Efforts to apply economic complexity to identify diversification opportunities often rely on diagrams comparing the relatedness and complexity of products, technologies, or industries. Yet, the use of these diagrams, is not based on empirical or theoretical evidence supporting some notion of optimality. Here, we introduce a method to identify diversification opportunities based on the minimization of a cost function that captures the constraints imposed by an economy's pattern of specialization and show that this ECI optimization algorithm produces recommendations that are substantially different from those obtained using relatedness-complexity diagrams. This method advances the use of economic complexity methods to explore questions of strategic diversification.