Enhancing Innovation and Resilience in Entrepreneurial Ecosystems Using Digital Twins and Fuzzy Optimization

Entrepreneurial ecosystems are multi-actor, uncertain, and dynamic environments in which policymakers and investors must balance innovation, resilience, and cost. Despite the growing literature on entrepreneurial ecosystems, much of the existing research has focused on identifying the components and relationships among actors and has provided less prescriptive frameworks for evaluating resource allocation policies before implementation. To address this gap, this study presents a digital twin-based and fuzzy multiobjective optimization framework for resource orchestration in entrepreneurial ecosystems. The proposed framework combines dynamic ecosystem representation with multiobjective decision-making under uncertainty and allows for the testing of different resource allocation and policy scenarios before actual intervention. To solve the model, exact optimization in GAMS was used for small- and medium-sized samples, and NSGA-II and ACO algorithms were used for large-scale problems. The advantage of the proposed method is that, unlike purely descriptive approaches or deterministic models, it simultaneously considers uncertainty, time dynamics, and trade-offs between innovation, resilience, and cost in an integrated decision-making framework. Experimental evaluation was conducted based on simulated data calibrated with reliable public sources, and the performance of the algorithms was compared with reference methods in terms of computational time, solution quality, and stability. The results showed that metaheuristics, especially NSGA-II, significantly reduced the solution time in large-scale problems and at the same time produced solutions closer to the Pareto frontier and with greater stability. Sensitivity analysis also showed that in the designed scenarios, policy budgets have a more prominent effect on innovation, while resource capacity and structural diversification play a more important role in enhancing resilience. Also, improving resource efficiency has had the greatest effect on reducing the total system cost. From a theoretical perspective, the present study operationally models the logic of resource orchestration in entrepreneurial ecosystems through the integration of digital twins and fuzzy multi-objective optimization. From a managerial perspective, this framework acts as a decision-making engine that allows for ex ante testing of policies, clarification of trade-offs, and extraction of resource allocation rules under uncertainty.