Modelling forced migration: A framework for conflict-induced forced migration modelling according to an agent-based approach

Highlights

• Data pertaining to forced migration are largely incomplete.

• Design of agent-based models for simulating conflict instances.

• Modelling decision making of forcibly displaced individuals.

• Model output provides emergent large-scale migration patterns.

• Providing a decision support tool for humanitarian relief.

Abstract

The challenge posed by the management of sudden migration of large groups of people lies in the ability to portray and predict the scale and dynamics of such movement accurately. This is further complicated by the fact that associated data pertaining to such migration are largely incomplete or untrustworthy. In view of the shortcomings in respect of modelling instances of conflict and the lack of data related to the associated movement patterns of forcibly displaced individuals, a generic framework is proposed for aiding in the design of agent-based models for simulating conflict instances along with the localised decision-making processes underlying the movement of refugees, undocumented migrants and internally displaced persons fleeing conflict-affected areas. A concept demonstrator is developed based on the framework in an attempt to demonstrate the usefulness and practicability of the framework in the context of conflict-induced forced migration in Syria. The value of such a model lies in the fact that it produces as output the corresponding emergent large-scale migration patterns which may assist in understanding the movement patterns of forcibly displaced people and predicting anticipated destinations of these individuals, and serve as a decision support tool for humanitarian relief.

Introduction

In 2019, one in every 97 people worldwide was forcibly displaced from his or her place of residence, whether as an asylum-seeker, a refugee or a person displaced within the borders of their own country. The United Nations High Commissioner for Refugees (2020) further estimated the total number of forcibly displaced people in 2019 to be 79.5 million, 45.7 million of whom were people displaced within their countries of origin, and 33.8 million as refugees and asylum-seekers fleeing across international borders.

The Global Opportunity Network (2017) identified several topics pertinent to global unstable regions as some of the most important opportunities to pursue with respect to social impact. The gross impact of persecution and violent conflicts is evident worldwide as millions of people are continually forced to flee their homes and seek refuge elsewhere. By 2030, it is anticipated that 46% of individuals will reside in unstable and conflict-affected areas. A methodological approach is required to manage and mitigate humanitarian crises due to the scale, complexity, continuance and reoccurring nature of this phenomenon in recent times (Global Opportunity Network, 2017).

The potential impact of humanitarian intervention efforts in these crises bring about many challenges that require innovative problem-solving approaches. Researchers such as Alhanaee and Csala (2016) and Greenwood (2005) have attempted to identify the motives of migrants in order to better understand the spatial phenomenon of migration in economic and social contexts. It is imperative for humanitarian support organisations and policy makers to understand the motive behind a person's migration movements in an attempt to plan for necessary resources and logistics aimed at facilitating their arrival (Harrison, 2016).

The lack of adequate and complete data presents a serious problem to humanitarian aid, especially with respect to forced displacement. Efforts towards improving the reliability, quality and scope of data concerning forcibly displaced people are required to address the gaps in these data currently available for assisting in long-term development planning during crises (Sarzin, 2017).

Simulation and the agent-based modelling (ABM) paradigm, in particular, can be utilised to develop models that may potentially explore the behaviour of forcibly displaced people in an attempt to better understand migration patterns. Developing such models, however, is a fairly complex process, especially as it comprises an extensive number of aspects to be considered. The framework proposed in this paper is intended to assist in the development and application of agent-based simulation models for modelling and predicting conflict-induced migration.

Other than this introductory section, the remainder of this paper is structured as follows. In Section 2, a background and literature review of computer simulation modelling and the use thereof in existing models pertaining to forced migration are given. The proposed framework is introduced in Section 3, while a model concept demonstrator aimed at validating the framework is discussed in Section 4. In conclusion, a discussion on the proposed framework and the model concept demonstrator is given in Section 5.