Nonlinear dynamical social and political prediction algorithm for city planning and public participation using the Impulse Pattern Formulation
This addresses city planning and public participation by providing a predictive tool for stakeholders, but it appears incremental as it adapts an existing method to a new application area.
The authors tackled the problem of predicting social and political parameters in city planning by proposing a nonlinear-dynamical algorithm based on the Impulse Pattern Formulation, which has shown high predictive precision at low computational cost in other domains.
A nonlinear-dynamical algorithm for city planning is proposed as an Impulse Pattern Formulation (IPF) for predicting relevant parameters like health, artistic freedom, or financial developments of different social or political stakeholders over the cause of a planning process. The IPF has already shown high predictive precision at low computational cost in musical instrument simulations, brain dynamics, and human-human interactions. The social and political IPF consists of three basic equations of system state developments, self-adaptation of stakeholders, two adaptive interactions, and external impact terms suitable for respective planning situations. Typical scenarios of stakeholder interactions and developments are modeled by adjusting a set of system parameters. These include stakeholder reaction to external input, enhanced system stability through self-adaptation, stakeholder convergence due to adaptive interaction, as well as complex dynamics in terms of fixed stakeholder impacts. A workflow for implementing the algorithm in real city planning scenarios is outlined. This workflow includes machine learning of a suitable set of parameters suggesting best-practice planning to aim at the desired development of the planning process and its output.