Refugee Resettlement: Why a Computational Method using E-CARGO is Better?

Refugee resettlement (RR) is a crucial component of a comprehensive response to forced displacement. Refugees bring diverse skills, talents, and perspectives, enriching the cultural and economic fabric of their host communities. Allocating refugees to communities where their needs are adequately met, and they can best use their skills will contribute to the wellbeing of both refugees and host communities. In this paper, we propose that computational methods can drastically reduce the cost and improve the efficiency of refugee allocation in Canada. We present an initial simulation to demonstrate that using computational methodologies to allocate refugees systematically is better than conventional manual work. The proposed simulations suggest assignments of refugees by maximizing overall evaluation values. The Role-Based Collaboration (RBC) methodology and its Environments - Classes, Agents, Roles, Groups, and Objects (E-CARGO) model have been verified to be a promising method for simulating social phenomena. This work uses RBC/E-CARGO to model the RR problem and obtains the RR results by simulations. These simulation results can also reflect the current manual resettlement experiences. This methodology is innovative and original. It is the first trial using RBC/E-CARGO in dealing with RR problems.