An Agent-based Approach to Human Migration Movement
Shih-Fen Cheng
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Abstract
How are the populations of the world likely to shift? Which countries will be impacted by sea-level rise? This paper uses a country-level agent-based dynamic network model to examine shifts in population given network relations among countries, which influences overall population change. Some of the networks considered include: alliance networks, shared language networks, economic influence networks, and proximity networks. Validation of model is done for migration probabilities between countries, as well as for country populations and distributions. The proposed framework provides a way to explore the interaction between climate change and policy factors at a global scale. 1 INTRODUCTION Human migration is an important research topic, with major economic effects (OECD 2014). At the same time, the decision to migrate is also determined by economic factors (Pew Research Center 2013). This intertwining effect is best captured by agent-based models, where agents interact with their environment, and changes in the environment affects the decisions of agents. Previous studies on human migration generally make simplistic assumptions about the decision model of migration, or do not consider fluctuations in birth/death rates, age distributions as well as networks ties between countries. With recent economic trends (Grant, Mark 2016), birth policy changes (Buckley, Chris 2015) and climate trends in mind, it is important to develop an agent-based model that is sensitive to these changes. In this work, we developed a country-level agent-based model which aims to mimic the agent's decision-making process for migration. This is done through consideration of a range of country networks, ranging from alliances to linguistic similarities to climate and migrant networks, just to name a few. Additionally, we initialize the age distributions of countries according to actual data (US Census Bureau 2016). The age distribution is then shifted throughout the simulation through an aging process, as well as actual births and deaths in population. We then validate our model against data for migration probabilities, and country-level observations (population and age distributions). The results are promising, as we illustrate through performance measures such as average of prediction error.
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