Nonlinear dynamical social and political prediction algorithm for city planning and public participation using the...

Area of Science:

• Complex Systems Science
• Computational Social Science
• Urban Planning

Background:

• Traditional city planning struggles to predict complex stakeholder dynamics and outcomes.
• Existing models often lack the adaptability to changing social and political landscapes.

Purpose of the Study:

• To introduce a novel nonlinear-dynamical algorithm, impulse pattern formulation (IPF), for city planning.
• To demonstrate IPF's capability in predicting key parameters like public health, artistic freedom, and financial developments.
• To outline a workflow for implementing IPF in real-world urban development scenarios.

Main Methods:

• Developed a social and political impulse pattern formulation (IPF) based on three core equations.
• Modeled stakeholder dynamics through self-adaptation, adaptive interactions, and external impact terms.
• Utilized system parameter adjustments to simulate various planning scenarios and stakeholder behaviors.

Main Results:

• IPF demonstrated high predictive precision with low computational cost in prior simulations.
• The algorithm effectively models stakeholder reactions, system stability, convergence, and complex dynamics.
• A practical workflow integrating machine learning for parameter optimization in city planning is proposed.

Conclusions:

• The impulse pattern formulation (IPF) offers a powerful, adaptable tool for predictive city planning.
• IPF can guide urban development towards desired outcomes by understanding and modeling stakeholder interactions.
• This approach facilitates data-driven, best-practice planning for complex urban environments.