Developing An Agent-based Architecture to Model Population Displacement

Abstract

United Nations High Commissioner for Refugees data reports the dislocation of millions from their native or accustomed environments. Statistical models authenticate the numbers exiting and ascertain proximate reasons for decisions to leave. These models employ static data and thus provide static outputs. This is problematic because when a decision to exit is concluded factors driving that decision can change or additional variables can present. A more efficient model is needed to envisage a broader range of outputs proffering why, when, and where migration will occur. Additionally, possessing the means to assess what-if scenarios allows for anticipating a range of expected/unexpected outcomes. This paper presents a unique architecture to state (environment) and population (agent) representation for agent-based modeling (ABM) as a means to analyze the decision-making process of individuals threatened with population displacement. This architecture facilitates agent-based modeling that can represent both fluid conditions in the environment and fluctuations in the decision-making process by people under duress. The predominant population displacement modeling application has been statistical, exclusive of dynamic inputs. The conclusions and recommendations within the literature validate implementing a new architecture for this research, which can model root, proximate, and triggering variables associated with this multi-layered, human-factors laden phenomenon.

Keywords: Population Displacement, Early Warning Model, United Nations Human Rights Council (UNHRC), Agent-Based Modeling (ABM), ABM Environment Matrix, ABM Agent Matrix

DOI: 10.54941/ahfe100201