Agent-Based Models and Complexity Science in the Age of Geospatial Big Data by Liliana Perez Eun-Kyeong Kim & Raja Sengupta

Agent-Based Models and Complexity Science in the Age of Geospatial Big Data by Liliana Perez Eun-Kyeong Kim & Raja Sengupta

Author:Liliana Perez, Eun-Kyeong Kim & Raja Sengupta
Language: eng
Format: epub
Publisher: Springer International Publishing, Cham


2.2 Complex Systems Theory

The conceptual framework of complex systems theory focuses on the many characteristic behaviours of dynamic systems such as self-organization, emergence, non-linearity, path dependence, bifurcation and sensitivity to initial conditions, amongst others [18, 19]. The modelling approaches that draw on complex systems theory, can be used to investigate how the interactions between parts can create collective behaviour within a dynamic system [20, 21] such as human migration. With the objective of modelling and examining the laws governing the behaviour of complex geographic phenomena such as forced migration, agent-based modelling (ABM) approach can be used to study the spatial patterns resulting from the complexities of human migration.

As it is the case with all complex system models, when studying human forced migration, it is important to understand the elements that characterise this phenomenon. In the case of forced migration in Syria, the movement of the population as well as the uniqueness of each individual, in addition to every aspect of the conflict creating the forced migration, are important considerations. Some research has been done in with these considerations [22, 23], but at a very local scale. The decision-making process with autonomous individuals in a bounded-rational environment, such as that of a refugee migration, is in nature heterogeneous and lends itself well to ABM as a tool for analysis [24]. Individuals represented by agents are dynamically interacting with other agents based on simple rules that will give rise to complex behaviours and patterns of displacement.



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