Canadian NPR Decreasing Policy Making: A Computational Simulation Method using E-CARGO

The Canadian government aims to reduce the non-permanent resident (NPR) population to alleviate pressure on housing, social services, and employment. The rapid growth of NPRs in recent years has strained resources and intensified affordability challenges in urban areas. Restructuring the NPR population supports sustainable development and resource planning while addressing demographic issues through Role-Based Collaboration (RBC). RBC is a computational method that leverages the Environments-Classes, Agents, Roles, Groups, and Objects (E-CARGO) model as a mechanism for facilitating cooperation. This paper focuses on the implementation of Role-Based Collaboration (RBC) in the context of immigration policy, employing GMRA (Group Multi-Role Assignment)-based modeling with solutions derived from Genetic Algorithms (GA) and K-Means clustering. It aims to propose strategies for achieving more sustainable and equitable outcomes for Non-Permanent Residents (NPRs) and permanent resident demographics in Canada, while effectively addressing the policy objective of a 5% reduction in NPRs. This includes aspects such as problem abstraction, classification, planning, policy-making cooperation, coordination, and decision-making. The life cycle of RBC encompasses three major phases: role negotiation, role assignment, and role execution.