We review agent-based models (ABM) of human migration with respect to their decision-making rules. The most prominent behavioural theories used as decision rules are the random utility theory, as implemented in the discrete choice model, and the theory of planned behaviour. We identify the critical choices that must be made in developing an ABM, namely the modelling of decision processes and social networks. We also discuss two challenges that hamper the widespread use of ABM in the study of migration and, more broadly, demography and the social sciences: (a) the choice and the operationalisation of a behavioural theory (decision-making and social interaction) and (b) the selection of empirical evidence to validate the model. We offer advice on how these challenges might be overcome.

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.Keywords: international migration; life course modelling; Theory of Planned Behaviour; multistate modelling; microsimulation; agent-based modelling IntroductionFor policymakers and researchers alike, it is of paramount interest to be able to predict how people make demographically relevant decisions. In this paper, we present a novel approach to projection by incorporating decision-making and interaction between individuals in a demographic projection model. We use an individual-based model rather than a population-based model such as the popular cohort-component model. For an overview of approaches to forecasting migration, see Bijak (2011), as well as other recent contributions from Hatton and Williamson (2011), Azose and Raftery (2013), and Abel and Sander (2014. This individual-based micro perspective enables us to incorporate behavioural mechanisms and social processes that influence demographic behaviour and population change into our model. We can thus predict the effect of external shocks, such as policy changes, by making the causal mechanism through which shocks alter behaviour explicit. Comparing model predictions with empirical outcomes facilitates the subsequent drawing of conclusions on the plausibility of the assumed behavioural mechanism. In this way, the model can be improved gradually over time, by refining the parameters and functional forms of its main component, in this case the decision-making on international migration.In the model, as in real life, demographically relevant decisions are embedded in the human life course. Choices are motivated by aspirations and preferences in different domains of life, such as work and family, and constrained by available resources (not only financial, but also cognitive and social resources, and time). We take into consideration that people do not have perfect foresight and usually lack the time and cognitive abilities to acquire the full and unbiased information necessary to make a rational choice. Preferences vary over the life course, as do both the availability of and need for resources, because of events or changing conditions (e.g., available social support). As a result, t...

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.

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