Decision-Making in Agent-Based Models of Migration: State of the Art and Challenges

Coastal adaptation and migration dynamics under future shoreline changes

In this study, we present a novel modeling framework that provides a stylized representation of coastal adaptation and migration dynamics under sea level rise (SLR). We develop an agent-based model that simulates household and government agents adapting to shoreline change and increasing coastal flood risk. This model is coupled to a gravity-based model of migration to simulate coastward migration. Household characteristics are derived from local census data from 2015, and household decisions are calibrated based on empirical survey data on household adaptation in France. We integrate projections of shoreline retreat and flood inundation levels under two Representative Concentration Pathways (RCPs) and account for socioeconomic development under two Shared Socioeconomic Pathways (SSPs). The model is then applied to simulate coastal adaptation and migration between 2015 and 2080. Our results indicate that without coastal adaptation, SLR could drive the cumulative net outmigration of 13,100 up to as many as 21,700 coastal inhabitants between 2015 and 2080 under SSP2-RCP4.5 and SSP5-RCP8.5, respectively. This amounts to between 3.0 %-3.7 % of the coastal population residing in the 1/100-year flood zone in 2080 under a scenario of SLR. We find that SLR-induced migration is largely dependent on the adaptation strategies pursued by households and governments. Household implementation of floodproofing measures combined with beach renourishment reduces the projected SLR-induced migration by 31 %-36 % when compared to a migration under a scenario of no adaptation. A sensitivity analysis indicates that the effect of beach renourishment on SLR-induced migration largely depends on the level of coastal flood protection offered by sandy beaches. By explicitly modeling household behavior combined with governmental protection strategies under increasing coastal risks, the framework presented in this study allows for a comparison of climate change impacts on coastal communities under different adaptation strategies.

An agent-based framework to study forced migration: A case study of Ukraine

The ongoing Russian aggression against Ukraine has forced over eight million people to migrate out of Ukraine. Understanding the dynamics of forced migration is essential for policy-making and for delivering humanitarian assistance. Existing work is hindered by a reliance on observational data which is only available well after the fact. In this work, we study the efficacy of a data-driven agent-based framework motivated by social and behavioral theory in predicting outflow of migrants as a result of conflict events during the initial phase of the Ukraine war. We discuss policy use cases for the proposed framework by demonstrating how it can leverage refugee demographic details to answer pressing policy questions. We also show how to incorporate conflict forecast scenarios to predict future conflict-induced migration flows. Detailed future migration estimates across various conflict scenarios can both help to reduce policymaker uncertainty and improve allocation and staging of limited humanitarian resources in crisis settings.

Selection and adaptation in human migration

This article reviews the ways migration shapes human biology. This includes the physiological and genetic, but also socio-cultural aspects such as organization, behavior, and culture. Across disciplines I highlight the multiple levels of cultural and genetic selection whereby individuals and groups adapt to pressures along a migration timeline: the origin, transit, and destination. Generally, the evidence suggests that selective pressures and adaptations occur at the individual, family, and community levels. Consequently, across levels there are negotiations, interactions, and feedbacks that shape migration outcomes and the trajectory of evolutionary change. The rise and persistence of migration-relevant adaptations emerges as a central question, including the maintenance of cumulative culture adaptations, the persistence of "cultures of migration," as well as the individual-level physiological and cognitive adaptations applied to successful transit and settlement in novel environments.

A coupled agent-based model for France for simulating adaptation and migration decisions under future coastal flood risk

In this study, we couple an integrated flood damage and agent-based model (ABM) with a gravity model of internal migration and a flood risk module (DYNAMO-M) to project household adaptation and migration decisions under increasing coastal flood risk in France. We ground the agent decision rules in a framework of subjective expected utility theory. This method addresses agent's bounded rationality related to risk perception and risk aversion and simulates the impact of push, pull, and mooring factors on migration and adaptation decisions. The agents are parameterized using subnational statistics, and the model is calibrated using a household survey on adaptation uptake. Subsequently, the model simulates household adaptation and migration based on increasing coastal flood damage from 2015 until 2080. A medium population growth scenario is used to simulate future population development, and sea level rise (SLR) is assessed for different climate scenarios. The results indicate that SLR can drive migration exceeding 8000 and 10,000 coastal inhabitants for 2080 under the Representative Concentration Pathways 4.5 and 8.5, respectively. Although household adaptation to flood risk strongly impacts projected annual flood damage, its impact on migration decisions is small and falls within the 90% confidence interval of model runs. Projections of coastal migration under SLR are most sensitive to migration costs and coastal flood protection standards, highlighting the need for better characterization of both in modeling exercises. The modeling framework demonstrated in this study can be upscaled to the global scale and function as a platform for a more integrated assessment of SLR-induced migration.

The Effects of Information on the Formation of Migration Routes and the Dynamics of Migration

Most models of migration simply assume that migrants somehow make their way from their point of origin to their chosen destination. We know, however, that-especially in the case of asylum migration-the migrant journey often is a hazardous, difficult process where migrants make decisions based on limited information and under severe material constraints. Here we investigate the dynamics of the migration journey itself using a spatially explicit, agent-based model. In particular we are interested in the effects of limited information and information exchange. We find that under limited information, migration routes generally become suboptimal, their stochasticity increases, and migrants arrive much less frequently at their preferred destination. Under specific circumstances, self-organised consensus routes emerge that are largely unpredictable. Limited information also strongly reduces the migrants' ability to react to changes in circumstances. We conclude, first, that information and information exchange is likely to have considerable effects on all aspects of migration and should thus be included in future modelling efforts and, second, that there are many questions in theoretical migration research that are likely to profit from the use of agent-based modelling techniques.

Agent-based modelling for Urban Analytics: State of the art and challenges

Agent-based modelling (ABM) is a facet of wider Multi-Agent Systems (MAS) research that explores the collective behaviour of individual ‘agents’, and the implications that their behaviour and interactions have for wider systemic behaviour. The method has been shown to hold considerable value in exploring and understanding human societies, but is still largely confined to use in academia. This is particularly evident in the field of Urban Analytics; one that is characterised by the use of new forms of data in combination with computational approaches to gain insight into urban processes. In Urban Analytics, ABM is gaining popularity as a valuable method for understanding the low-level interactions that ultimately drive cities, but as yet is rarely used by stakeholders (planners, governments, etc.) to address real policy problems. This paper presents the state-of-the-art in the application of ABM at the interface of MAS and Urban Analytics by a group of ABM researchers who are affiliated with the Urban Analytics programme of the Alan Turing Institute in London (UK). It addresses issues around modelling behaviour, the use of new forms of data, the calibration of models under high uncertainty, real-time modelling, the use of AI techniques, large-scale models, and the implications for modelling policy. The discussion also contextualises current research in wider debates around Data Science, Artificial Intelligence, and MAS more broadly.

The evolution of demographic methods

Demographic methods have been evolving ever since the birth of demography in response to changes in the field's research contents and theoretical orientations. An early core mission of finding regularities underlying macro-level population phenomena and a later interest in explaining population changes inductively facilitated the development of formal demographic techniques. A more radical methodological shift occurred after the 1960s, with the increasing availability of micro-level survey data and a shift of theoretical focus toward causal mechanisms, leading to the widespread adoption of regression-based models and methods from other social science disciplines. The future development of demographic methods will likely continue to incorporate new methods first developed in other disciplines, including techniques for analyzing unstructured "big" data, but formal demographic techniques will still play a role in population forecasting, measurements improvements, and correction of faulty data, providing foundational knowledge for other social science disciplines.

The challenges of dynamic vulnerability and how to assess it

Recent disasters have demonstrated the challenges faced by society as a result of the increasing complexity of disaster risk. In this perspective article, we discuss the complex interactions between hazards and vulnerability and suggest methodological approaches to assess and include dynamics of vulnerability in our risk assessments, learning from the compound and multi-hazard, socio-hydrology, and socio-ecological research communities. We argue for a changed perspective, starting with the circumstances that determine dynamic vulnerability. We identify three types of dynamics of vulnerability: (1) the underlying dynamics of vulnerability, (2) changes in vulnerability during long-lasting disasters, and (3) changes in vulnerability during compounding disasters and societal shocks. We conclude that there is great potential to capture the dynamics of vulnerability using qualitative and model-based methods, both for reproducing historic and projecting future dynamics of vulnerability. We provide examples using narratives, agent-based models, and system dynamics.

Optimization of Spatial Pattern of Land Use: Progress, Frontiers, and Prospects

Due to high-intensity human disturbance and rapid climate change, optimizing the spatial pattern of land use has become a pivotal path to restoring ecosystem functions and realizing the sustainable development of human–land relationships. This review uses the literature analysis method combined with CiteSpace to determine current research progress and frontiers, challenges, and directions for further improvement in this field. The main conclusions include the following: (a) research on the optimization of spatial pattern of land use has transformed from pattern description orientation to sustainable development orientation to ecological restoration orientation. Its research paradigm has changed from pattern to function to well-being; (b) the research frontier mainly includes spatial pattern of land use that takes into account the unity of spatial structure and functional attributes, the ecological mechanism and feedback effect of change in spatial pattern of land, the theoretical framework and model construction of land use simulation and prediction based on multiple disciplines and fields, and the adaptive management of sustainable land use in the context of climate change; (c) based on current research challenges, we integrate the research on landscape ecology and ecosystem service flows to develop an “element sets–network structure–system functions–human well-being” conceptual model. We also propose the strengthening of future research on theoretical innovation, spatiotemporal mechanism selection, causal emergence mechanism, the transformation threshold, and uncertainty. We provide innovative ideas for achieving sustainable management of land systems and territorial spatial planning with the aim of improving the adaptability of land use spatial optimization. This is expected to strengthen the ability of land systems to cope with ecological security and climate risks.

Climate Change, Drought, and Potential Environmental Migration Flows Under Different Policy Scenarios

Unmitigated climate change will likely produce major problems for human populations worldwide. Although many researchers and policy-makers believe that drought may be an important “push” factor underlying migration in the future, the precise relationship between drought and migration remains unclear. This article models the potential scope of such movements for the emissions policy choices facing all nation-states today. Applying insights from climate science and computational modeling to migration research, we examine the likely surge of drought-induced migration and assess the prospects of different policy scenarios to mitigate involuntary displacement. Using an ensemble of 16 climate models in conjunction with high-resolution geospatial population data and different policy scenarios, we generate drought projections worldwide and estimate the potential for internal and international population movement due to extreme droughts through the remainder of the 21st century. Our simulations suggest that a potential for drought-induced migration increases by approximately 200 percent under the current international policy scenario (corresponding to the current Paris Agreement targets). In contrast, total migration increases by almost 500 percent, should current international cooperation fail and should unrestricted policies toward greenhouse gas emissions prevail. We argue that despite the continued growth projections of drought-induced migration in all cases, international cooperation on climate change can substantially reduce the global potential for such migration, in contrast to unilateral policy approaches to energy demands. This article highlights the importance of modeling future environmental migrations, in order to manage the pressures and unprecedented policy challenges which are expected to dramatically increase under conditions of unmitigated climate change.

Forecasting asylum-related migration flows with machine learning and data at scale

The sudden and unexpected migration flows that reached Europe during the so-called ‘refugee crisis’ of 2015–2016 left governments unprepared, exposing significant shortcomings in the field of migration forecasting. Forecasting asylum-related migration is indeed problematic. Migration is a complex system, drivers are composite, measurement incorporates uncertainty, and most migration theories are either under-specified or hardly actionable. As a result, approaches to forecasting generally focus on specific migration flows, and the results are often inconsistent and difficult to generalise. Here we present an adaptive machine learning algorithm that integrates administrative statistics and non-traditional data sources at scale to effectively forecast asylum-related migration flows. We focus on asylum applications lodged in countries of the European Union (EU) by nationals of all countries of origin worldwide, but the same approach can be applied in any context provided adequate migration or asylum data are available. Uniquely, our approach (a) monitors drivers in countries of origin and destination to detect early onset change; (b) models individual country-to-country migration flows separately and on moving time windows; (c) estimates the effects of individual drivers, including lagged effects; (d) delivers forecasts of asylum applications up to four weeks ahead; (e) assesses how patterns of drivers shift over time to describe the functioning and change of migration systems. Our approach draws on migration theory and modelling, international protection, and data science to deliver what is, to our knowledge, the first comprehensive system for forecasting asylum applications based on adaptive models and data at scale. Importantly, this approach can be extended to forecast other social processes.

Anticipating sea-level rise and human migration: A review of empirical evidence and avenues for future research

Sea-level rise (SLR) threatens millions of people living in coastal areas through permanent inundation and other SLR-related hazards. Migration is one way for people to adapt to these coastal changes, but presents an enormous policy challenge given the number of people affected. Knowledge about the relationship between SLR-related hazards and migration is therefore important to allow for anticipatory policymaking. In recent years, an increasing number of empirical studies have investigated, using survey or census data, how SLR-related hazards including flooding, salinization, and erosion together with non-environmental factors influence migration behavior. In this article, we provide a systematic literature review of this empirical work. Our review findings indicate that flooding is not necessarily associated with increased migration. Severe flood events even tend to decrease long-term migration in developing countries, although more research is needed to better understand the underpinnings of this finding. Salinization and erosion do generally lead to migration, but the number of studies is sparse. Several non-environmental factors including wealth and place attachment influence migration alongside SLR-related hazards. Based on the review, we propose a research agenda by outlining knowledge gaps and promising avenues for future research on this topic. Promising research avenues include using behavioral experiments to investigate migration behavior under future SLR scenarios, studying migration among other adaptation strategies, and complementing empirical research with dynamic migration modeling. We conclude that more empirical research on the SLR-migration nexus is needed to properly understand and anticipate the complex dynamics of migration under SLR, and to design adequate policy responses. This article is categorized under: Climate Economics < Aggregation Techniques for Impacts and Mitigation CostsVulnerability and Adaptation to Climate Change < Learning from Cases and AnalogiesAssessing Impacts of Climate Change < Evaluating Future Impacts of Climate Change.

Toward an Early Warning System for Monitoring Asylum-Related Migration Flows in Europe

Asylum-related migration is highly complex, uncertain, and volatile, which precludes using standard model-based predictions to inform policy and operational decisions. At the same time, asylum's potentially high societal impacts on receiving countries and the resource implications of asylum processes call for more proactive approaches for assessing current and future migration flows. In this article, we propose an alternative approach to asylum modeling, based on the detection of early warning signals by using models originating from statistical control theory. Our empirical analysis of several asylum flows into Europe in 2010–2016 demonstrates the approach's utility and potential in aiding the management of mixed migration flows, while also shedding more light on the work needed to make better use of the “big data” and scenario-based methods for comprehensive and systematic examination of risk, uncertainty, and emerging trends.

Assessing the cascading impacts of natural disasters in a multi-layer behavioral network framework

Natural disasters negatively impact regions and exacerbate socioeconomic vulnerabilities. While the direct impacts of natural disasters are well understood, the channels through which these shocks spread to non-affected regions, still represents an open research question. In this paper we propose modelling socioeconomic systems as spatially-explicit, multi-layer behavioral networks, where the interplay of supply-side production, and demand-side consumption decisions, can help us understand how climate shocks cascade. We apply this modelling framework to analyze the spatial-temporal evolution of vulnerability following a negative food-production shock in one part of an agriculture-dependent economy. Simulation results show that vulnerability is cyclical, and its distribution critically depends on the network density and distance from the epicenter of the shock. We also introduce a new multi-layer measure, the Vulnerability Rank (VRank), which synthesizes various location-level risks into a single index. This framework can help design policies, aimed to better understand, effectively respond, and build resilience to natural disasters. This is particularly important for poorer regions, where response time is critical and financial resources are limited.

Toward Resilient Water-Energy-Food Systems under Shocks: Understanding the Impact of Migration, Pandemics, and Natural Disasters

The historic pandemic faced by the international community today boldly demonstrates the complexity and interconnectedness of the resource challenges we must better understand and address in the future. Further complexity is observed when accounting for the impact of compounded shocks related to natural disasters and forced migration around the world. Effectively addressing these challenges requires the development of research that cuts across disciplines and innovates at their interfaces, in order to develop multifaceted solutions that respond to the social, economic, technological, and policy dimensions of these challenges. Water, energy, and food systems are tightly interconnected. They are faced with pressures of varying natures and levels of urgency which need to be better understood, especially as nations work toward achieving the UN 2030 Agenda’s Sustainable Development Goals by 2030. This paper will review existing models and knowledge gaps related to water-energy-food (WEF) nexus models, as well as models for quantifying the impact of migration, pandemics, and natural disasters on this resource nexus. Specifically, this paper will: (1) explore the WEF nexus literature and identify gaps in current assessment tools and models; (2) explore the literature on tools and models for predicting the shocks of migration, natural disasters, and pandemics; (3) identify interconnections between water, energy, and food systems and the identified shocks; (4) develop a common framework that provides a road map for integrating those shocks in WEF nexus analysis; (5) provide recommendations for future research and policies moving forward.

Assessing personal exposure using Agent Based Modelling informed by sensors technology

Technology innovations create possibilities to capture exposure-related data at a great depth and breadth. Considering, though, the substantial hurdles involved in collecting individual data for whole populations, this study introduces a first approach of simulating human movement and interaction behaviour, using Agent Based Modelling (ABM). A city scale ABM was developed for urban Thessaloniki, Greece that feeds into population-based exposure assessment without imposing prior bias, basing its estimations onto emerging properties of the behaviour of the computerised autonomous decision makers (agents) that compose the city-system. Population statistics, road and buildings networks data were transformed into human, road and building agents, respectively. Survey outputs with time-use patterns were associated with human agent rules, aiming to model representative to real-world behaviours. Moreover, time-geography of exposure data, derived from a local sensors campaign, was used to inform and enhance the model. As a prevalence of an agent-specific decision-making, virtual individuals of different sociodemographic backgrounds express different spatiotemporal behaviours and their trajectories are coupled with spatially resolved pollution levels. Personal exposure was evaluated by assigning PM concentrations to human agents based on coordinates, type of location and intensity of encountered activities. Study results indicated that PM2.5 inhalation adjusted exposure between housemates can differ by 56.5% whereas exposure between two neighbours can vary by as much as 87%, due to the prevalence of different behaviours. This study provides details of a new methodology that permits the cost-effective construction of refined time-activity diaries and daily exposure profiles, taking into account different microenvironments and sociodemographic characteristics. The proposed method leads to a refined exposure assessment model, addressing effectively vulnerable subgroups of population. It can be used for evaluating the probable impacts of different public health policies prior to implementation reducing, therefore, the time and expense required to identify efficient measures.

Challenges to pre-migration interventions to prevent human trafficking: Results from a before-and-after learning assessment of training for prospective female migrants in Odisha, India

Background

Awareness-raising and pre-migration training are popular strategies to prevent human trafficking. Programmatic theories assume that when prospective migrants are equipped with information about risks, they will make more-informed choices, ultimately resulting in safe migration. In 2016, India was estimated to have 8 million people in modern slavery, including those who migrate internally for work. Work in Freedom (WiF) was a community-based trafficking prevention intervention. This study evaluated WiF’s pre-migration knowledge-building activities for female migrants in Odisha to prevent future labour-related exploitation.

Methods

Pre- and post- training questionnaires were administered to women (N = 347) who participated in a two-day pre-migration training session. Descriptive analysis and unadjusted analyses (paired t-tests, McNemar’s tests, Wilcoxon signed ranks tests) examined differences in women’s knowledge scores before and after training. Adjusted analyses used mixed effects models to explore whether receiving information on workers’ rights or working away from home prior to the training was associated with changes in scores. Additionally, we used data from a household survey (N = 4,671) and survey of female migrants (N = 112) from a population sample in the same district to evaluate the intervention’s rationale and implementation strategy.

Results

Female participants were on average 37.3 years-old (SD 11) and most (67.9%) had no formal education. Only 11 participants (3.2%) had previous migration experience. Most participants (90.5%) had previously received information or advice on workers’ rights or working away from home. Compared to female migrants in the population, training participants were different in age, caste and religion. Awareness about migration risks, rights and collective bargaining was very low initially and remained low post-training, e.g. of 13 possible migration risks, before the training, participants named an average of 1.2 risks, which increased only slightly to 2.1 risks after the training (T(346) = -11.64, p<0.001). Changes were modest for attitudes about safe and risky migration practices, earnings and savings. Before the training, only 34 women (10.4%) considered migrating, which reduced to 25 women (7.7%) post-training (X² = 1.88, p = 0.169)—consistent with the low prevalence (7% of households) of female migration locally. Women’s attitudes remained relatively fixed about the shame associated with paid domestic work. Survey data indicated focusing on domestic work did not correspond to regional migration trends, where women migrate primarily for construction or agriculture work.

Conclusion

The apparent low effectiveness of the WiF short-duration migration training may be linked to the assumption that individual changes in knowledge will lead to shifts in social norms. The narrow focus on such individual-level interventions may overestimate an individual’s agency. Findings indicate the importance of intervention development research to ensure activities are conducted in the right locations, target the right populations, and have relevant content. Absent intervention development research, this intervention suffered from operating in a site that had very few migrant women and a very small proportion migrating for domestic work—the focus of the training. To promote better development investments, interventions should be informed by local evidence and subjected to rigorous theory-based evaluation to ensure interventions achieve the most robust design to foster safe labour migration for women.

Modeling human migration across spatial scales in Colombia

Human mobility, both short and long term, are important considerations in the study of numerous systems. Economic and technological advances have led to a more interconnected global community, further increasing the need for considerations of human mobility. While data on human mobility are better recorded in many developed countries, availability of such data remains limited in many low- and middle-income countries around the world, particularly at the fine temporal and spatial scales required by many applications. In this study, we used 5-year census-based internal migration microdata for 32 departments in Colombia (i.e., Admin-1 level) to develop a novel spatial interaction modeling approach for estimating migration, at a finer spatial scale, among the 1,122 municipalities in the country (i.e., Admin-2 level). Our modeling approach addresses a significant lack of migration data at administrative unit levels finer than those at which migration data are typically recorded. Due to the widespread availability of census-based migration microdata at the Admin-1 level, our modeling approach opens up for the possibilities of modeling migration patterns at Admin-2 and Admin-3 levels across many other countries where such data are currently lacking.

Climate Change and Migration: New Insights from a Dynamic Model of Out-Migration and Return Migration

In popular accounts, stories of environmental refugees convey a bleak picture of the impacts of climate change on migration. Scholarly research is less conclusive, with studies finding varying effects. This paper uses an agent-based model (ABM) of land use, social networks, and household dynamics to examine how extreme floods and droughts affect migration in Northeast Thailand. The ABM explicitly models the dynamic and interactive pathways through which climate-migration relationships might operate, including coupled out and return streams. Results suggest minimal effects on out-migration but marked negative effects on return. Social networks play a pivotal role in producing these patterns. In all, the portrait of climate change and migration painted by focusing only on environmental refugees is too simple. Climate change operates on already established migration processes that are part and parcel of the life course, embedded in dynamic social networks, and incorporated in larger interactive systems where out- and return migration are integrally connected.

Soft Randomized Machine Learning Procedure for Modeling Dynamic Interaction of Regional Systems

The paper suggests a randomized model for dynamic migratory interaction of regional systems. The locally stationary states of migration flows in the basic and immigration systems are described by corresponding entropy operators. A soft randomization procedure that defines the optimal probability density functions of system parameters and measurement noises is developed. The advantages of soft randomization with approximate empirical data balance conditions are demonstrated, which considerably reduces algorithmic complexity and computational resources demand. An example of migratory interaction modeling and testing is given.

Accounting for peoples' preferences in establishing new cities: A spatial model of population migration in Kuwait

Modelling of internal migration to new cities is challenging, yet necessary to ensure that these newly established urban areas will be populated and function as intended. In the State of Kuwait, there is a unique set of push and pull factors: government subsidised housing for citizens, the existence of a single urban area, and the initiation of a new and ambitious master plan for the construction of 12 new cities, which are expected to attract not only locals, but also international residents and businesses. On top of these factors, there is an unusual demographic situation, as non-citizens outnumber Kuwaiti citizens by a factor of 2.3, with these groups having widely different preferences in terms of housing. Currently, there is no plan to take these resident groups' opinions into consideration for the new cities project. Besides, the current study simulates the impacts of the involvement of residents in urban planning. Samples from resident groups (citizens and non-citizens) participated in targeted surveys and useful answers were extracted in relation to the migration likelihood, push and pull factors that may affect their decisions, spatial preferences for new cities and their opinions on segregation by nationality. Specifically, the survey results showed significant interest of residents in moving to the new cities. For citizens, the most important factors in deciding whether to move or not were proximity to their close family and housing availability, while for non-citizens the most important factor was the creation of new employment opportunities. Both survey groups agreed that existing city property prices are too high and make the prospect of moving to a new city more attractive. The responses were transferred in an Agent Based Model, and the simulations showed certain differences to the official projections for 2050 without the public responses, in regards to the geographical distribution of the most desirable suburbs. Furthermore, the simulations showed that in the new cities, nationality segregation levels are expected to drop by at least 15% compared to the 2015 levels. The findings may be utilised by the authorities to modify the master plan accordingly.

A data-driven computational model on the effects of immigration policies

Many scholars suggest that visa restrictions push individuals who would have otherwise migrated legally toward illegal channels. This expectation is difficult to test empirically for three reasons. First, unauthorized migration is clandestine and often unobservable. Second, interpersonal ties between migrants and would-be migrants form a self-perpetuating system, which adapts in ways that are difficult to observe or predict. Third, empirical evaluations of immigration policy are vulnerable to endogeneity and other issues of causal inference. In this paper, we pair tailor-made empirical designs with an agent-based computational model (ABM) to capture the dynamics of a migration system that often elude empirical analysis, while grounding agent rules and characteristics with primary data collected in Jamaica, an origin country. We find that some government-imposed restrictions on migrants can deter total migration, but others are ineffective. Relative to a system of free movement, the minimal eligibility conditions required to classify migrants into visa categories alone make migration inaccessible for many. Restrictive policies imposed on student and high-skilled visa categories have little added effect because eligible individuals are likely able to migrate through alternative legal categories. Meanwhile, restrictions on family-based visas result in significant reductions in total migration. However, they also produce the largest reorientation toward unauthorized channels-an unintended consequence that even the highest rates of apprehension do not effectively eliminate.

A Schelling model with adaptive tolerance

We introduce a Schelling model in which people are modelled as agents following simple behavioural rules which dictate their tolerance to others, their corresponding preference for particular locations, and in turn their movement through a geographic or social space. Our innovation over previous work is to allow agents to adapt their tolerance to others in response to their local environment, in line with contemporary theories from social psychology. We show that adaptive tolerance leads to a polarization in tolerance levels, with distinct modes at either extreme of the distribution. Moreover, agents self-organize into communities of like-tolerance, just as they congregate with those of same colour. Our results are robust not only to variations in free parameters, but also experimental treatments in which migrants are dynamically introduced into the native population. We argue that this model provides one possible parsimonious explanation of the political landscape circa 2016.

Modeling marine oily wastewater treatment by a probabilistic agent-based approach

This study developed a novel probabilistic agent-based approach for modeling of marine oily wastewater treatment processes. It begins first by constructing a probability-based agent simulation model, followed by a global sensitivity analysis and a genetic algorithm-based calibration. The proposed modeling approach was tested through a case study of the removal of naphthalene from marine oily wastewater using UV irradiation. The removal of naphthalene was described by an agent-based simulation model using 8 types of agents and 11 reactions. Each reaction was governed by a probability parameter to determine its occurrence. The modeling results showed that the root mean square errors between modeled and observed removal rates were 8.73 and 11.03% for calibration and validation runs, respectively. Reaction competition was analyzed by comparing agent-based reaction probabilities, while agents' heterogeneity was visualized by plotting their real-time spatial distribution, showing a strong potential for reactor design and process optimization.

Modelling and simulating decision processes of linked lives: An approach based on concurrent processes and stochastic race

Individuals' decision processes play a central role in understanding modern migration phenomena and other demographic processes. Their integration into agent-based computational demography depends largely on suitable support by a modelling language. We are developing the Modelling Language for Linked Lives (ML3) to describe the diverse decision processes of linked lives succinctly in continuous time. The context of individuals is modelled by networks the individual is part of, such as family ties and other social networks. Central concepts, such as behaviour conditional on agent attributes, age-dependent behaviour, and stochastic waiting times, are tightly integrated in the language. Thereby, alternative decisions are modelled by concurrent processes that compete by stochastic race. Using a migration model, we demonstrate how this allows for compact description of complex decisions, here based on the Theory of Planned Behaviour. We describe the challenges for the simulation algorithm posed by stochastic race between multiple concurrent complex decisions.

Choosing the choice: Reflections on modelling decisions and behaviour in demographic agent-based models

This paper investigates the issues associated with choosing appropriate models of choice for demographic agent-based models. In particular, we discuss the importance of context, time preference, and dealing with uncertainty in decision modelling, as well as the heterogeneity between agents in their decision-making strategies. The paper concludes by advocating empirically driven, modular, and multi-model approaches to designing simulations of human decision-making, given the lack of an agreed strategy for dealing with any of these issues. Furthermore, we suggest that an iterative process of data collection and simulation experiments, with the latter informing future empirical data collection, should form the basis of such an endeavour. The discussion is illustrated with reference to selected demographic agent-based models, with a focus on migration.

Facilitators and constraints at each stage of the migration decision process

Behavioural models of migration emphasize the importance of migration decision-making for the explanation of subsequent behaviour. But empirical migration research regularly finds considerable gaps between those who intend to migrate and those who actually realize their intention. This paper applies the Theory of Planned Behaviour, enriched by the Rubicon model, to test specific hypotheses about distinct effects of facilitators and constraints on specific stages of migration decision-making and behaviour. The data come from a tailor-made panel survey based on random samples of people drawn from two German cities in 2006-07. The results show that in conventional models the effects of facilitators and constraints on migration decision-making are likely to be underestimated. Splitting the process of migration decision-making into a pre-decisional and a pre-actional phase helps to avoid bias in the estimated effects of facilitators and constraints on both migration decision-making and migration behaviour.

Multistate modelling extended by behavioural rules: An application to migration

We propose to extend demographic multistate models by adding a behavioural element: behavioural rules explain intentions and thus transitions. Our framework is inspired by the Theory of Planned Behaviour. We exemplify our approach with a model of migration from Senegal to France. Model parameters are determined using empirical data where available. Parameters for which no empirical correspondence exists are determined by calibration. Age- and period-specific migration rates are used for model validation. Our approach adds to the toolkit of demographic projection by allowing for shocks and social influence, which alter behaviour in non-linear ways, while sticking to the general framework of multistate modelling. Our simulations yield that higher income growth in Senegal leads to higher emigration rates in the medium term, while a decrease in fertility yields lower emigration rates.

A generalized simulation development approach for predicting refugee destinations

In recent years, global forced displacement has reached record levels, with 22.5 million refugees worldwide. Forecasting refugee movements is important, as accurate predictions can help save refugee lives by allowing governments and NGOs to conduct a better informed allocation of humanitarian resources. Here, we propose a generalized simulation development approach to predict the destinations of refugee movements in conflict regions. In this approach, we synthesize data from UNHCR, ACLED and Bing Maps to construct agent-based simulations of refugee movements. We apply our approach to develop, run and validate refugee movement simulations set in three major African conflicts, estimating the distribution of incoming refugees across destination camps, given the expected total number of refugees in the conflict. Our simulations consistently predict more than 75% of the refugee destinations correctly after the first 12 days, and consistently outperform alternative naive forecasting techniques. Using our approach, we are also able to reproduce key trends in refugee arrival rates found in the UNHCR data.

A generalized simulation development approach for predicting refugee destinations

In recent years, global forced displacement has reached record levels, with 22.5 million refugees worldwide. Forecasting refugee movements is important, as accurate predictions can help save refugee lives by allowing governments and NGOs to conduct a better informed allocation of humanitarian resources. Here, we propose a generalized simulation development approach to predict the destinations of refugee movements in conflict regions. In this approach, we synthesize data from UNHCR, ACLED and Bing Maps to construct agent-based simulations of refugee movements. We apply our approach to develop, run and validate refugee movement simulations set in three major African conflicts, estimating the distribution of incoming refugees across destination camps, given the expected total number of refugees in the conflict. Our simulations consistently predict more than 75% of the refugee destinations correctly after the first 12 days, and consistently outperform alternative naive forecasting techniques. Using our approach, we are also able to reproduce key trends in refugee arrival rates found in the UNHCR data.