Operations in critical areas of importance to society, such as healthcare, transportation and logistics, power systems, and emergency response, profoundly affect multiple stakeholders with diverse perspectives. These operations are often modeled using discrete programming methods to capture the various decision-making factors through centrally-selected objectives and constraints. Unfortunately, centralized modeling and solution methodologies may overlook the perspectives and needs of certain stakeholders, potentially leading to the exclusion of certain stakeholders. Additionally, discrete programming problems suffer from the curse of combinatorial complexity, which can result in suboptimal solutions and difficulties in achieving a transparent, intuitive, fair, and equitable outcome. To address these challenges and foster inclusive synergy, we propose an approach to democratize complex problem-solving through distributed modeling and computation methods to enable participation as well as to increase fairness, accountability, and transparency. Our approach employs the latest versions of Lagrangian Relaxation to decompose complex problems into subproblems, empowering stakeholders to actively and autonomously participate in independent decision-making by incorporating constraints and preferences in accordance with their values. The fast coordination of subproblems based on the economic “supply and demand” principle ensures that the optimization outcomes are economically efficient. In addition, this approach harnesses “cyber-human” collective intelligence to enable efficient decision-making.