Making Smart Cities more sustainable, resilient and democratic is emerging as an endeavor of satisfying hard constraints, for instance meeting net-zero targets. Decentralized multi-agent methods for socio-technical optimization of large-scale complex infrastructures such as energy and transport networks are scalable and more privacy-preserving by design. However, they mainly focus on satisfying soft constraints to remain cost-effective. This paper introduces a new model for decentralized hard constraint satisfaction in discrete-choice combinatorial optimization problems. The model solves the cold start problem of partial information for coordination during initialization that can violate hard constraints. It also preserves a low-cost satisfaction of hard constraints in subsequent coordinated choices during which soft constraints optimization is performed. Strikingly, experimental results in real-world Smart City application scenarios demonstrate the required behavioral shift to preserve optimality when hard constraints are satisfied. These findings are significant for policymakers, system operators, designers and architects to create the missing social capital of running cities in more viable trajectories.

翻译：提升智慧城市的可持续性、韧性和民主性正在成为一项满足硬约束（例如实现净零排放目标）的重要任务。针对能源和交通网络等大规模复杂基础设施的社会技术优化，采用分散式多智能体方法具有可扩展性且在设计中更具隐私保护性。然而，这些方法主要侧重于满足软约束以保持成本效益。本文提出了一种用于离散选择组合优化问题中分散式硬约束满足的新模型。该模型解决了初始化协调过程中因部分信息导致的冷启动问题（该问题可能违反硬约束），同时保留了后续协调选择中低成本的硬约束满足特性（在此过程中进行软约束优化）。值得注意的是，在真实智慧城市应用场景中的实验结果表明：当硬约束得到满足时，为保持最优性所需的必要行为转变得以实现。这些发现对政策制定者、系统运营商、设计师和架构师而言具有重要意义，有助于创建缺失的社会资本，使城市运行走上更具可行性的发展轨道。