Methods for Assessing Industrial Integration in the Technological Complexity Matrix Based on the Digital Economic DNA Platform and Network Reallocation Models

This study develops and tests a methodology for assessing industrial integration in a technological complexity matrix for an emerging economy, where static input–output and typological approaches cannot capture the non-stationary nature of investment potential and export orientation. The paper proposes (i) a Digital Economic DNA index, a symmetric measure of digital-technological proximity between industrial sectors; (ii) an expert-matrix procedure, based on Kendall’s coefficient of concordance and Harrington’s desirability function, for calibrating the elasticity weights of the determinants of platform-based investment attractiveness; and (iii) a dynamic network reallocation model based on the Multiplicative Weights Update (Hedge) algorithm. The empirical basis is official statistics for 14 regions of Uzbekistan over 2015–2025 and the rankings of 20 experts. Expert agreement is strong (W = 0.913; χ2 = 73.0, p < 0.001), with technological complexity and algorithmic liquidity as the dominant determinants. In simulation, dynamic reallocation yields a cumulative growth multiplier of 10.03 against 9.82 for equal-share allocation (+2.1%). The proximity analysis identifies the metallurgy–machinery–chemicals nexus as the most integrated segment (index 0.325–0.418), while textiles remain weakly connected (0.189–0.248). The framework offers a transparent and reproducible tool for targeting investment in digital industrial policy; its main limitations are the short panel, a single simulated path and the associational nature of the proximity measure.

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