  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.
