An agent-based model for the emigration of highly-skilled labour.

We hypothesise that there are two main factors that impact the decision and ability to move abroad: desire to maximise individual utility and network effects. Accordingly, several factors play role in brain drain such as the overall economic and social differences between the home and host countries, people’s ability and capacity to obtain good jobs and start a life abroad, the barriers of moving abroad, and people’s social network who are already working abroad.

Schelling and Sakoda prominently proposed computational models suggesting that strong ethnic residential segregation can be the unintended outcome of a self-reinforcing dynamic driven by choices of individuals with rather tolerant ethnic preferences. There are only few attempts to apply this view to school choice, another important arena in which ethnic segregation occurs. In the current paper, we explore with an agent-based theoretical model similar to those proposed for residential segregation, how ethnic tolerance among parents can affect the level of school segregation. More specifically, we ask whether and under which conditions school segregation could be reduced if more parents hold tolerant ethnic preferences. We move beyond earlier models of school segregation in three ways. First, we model individual school choices using a random utility discrete choice approach. Second, we vary the pattern of ethnic segregation in the residential context of school choices systematically, comparing residential maps in which segregation is unrelated to parents’ level of tolerance to residential maps reflecting their ethnic preferences. Third, we introduce heterogeneity in tolerance levels among parents belonging to the same group. Our simulation experiments suggest that ethnic school segregation can be a very robust phenomenon, occurring even when about half of the population prefers mixed to segregated schools. However, we also identify a “sweet spot” in the parameter space in which a larger proportion of tolerant parents makes the biggest difference. This is the case when parents have moderate preferences for nearby schools and there is only little residential segregation. Further experiments are presented that unravel the underlying mechanisms.

Cooperation is essential for all domains of life. Yet, ironically, it is intrinsically vulnerable to exploitation by cheats. Hence, an explanatory necessity spurs many evolutionary biologists to search for mechanisms that could support cooperation. In general, cooperation can emerge and be maintained when cooperators are sufficiently interacting with themselves. This communication provides a kind of assortment and reciprocity. The most crucial and common mechanisms to achieve that task are kin selection, spatial structure, and enforcement (punishment). Here, we used agent-based simulation models to investigate these pivotal mechanisms against conditional defector strategies. We concluded that the latter could easily violate the former and take over the population. This surprising outcome may urge us to rethink the evolution of cooperation, as it illustrates that maintaining cooperation may be more difficult than previously thought. Moreover, empirical applications may support these theoretical findings, such as invading the cooperator population of pathogens by genetically engineered conditional defectors, which could be a potential therapy for many incurable diseases.

The first simple movement models used unbiased and uncorrelated random walks (RW). In such models of movement, the direction of the movement is totally independent of the previous movement direction. In other words, at each time step the direction, in which an individual is moving is completely random. This process is referred to as a Brownian motion.
On the other hand, in correlated random walks (CRW) the choice of the movement directions depends on the direction of the previous movement. At each time step, the movement direction has a tendency to point in the same direction as the previous one. This movement model fits well observational movement data for many animal species.
The presented agent based model simulated the movement of the agents as a correlated random walk (CRW). The turning angle at each time step follows the Von Mises distribution with a ϰ of 10. The closer ϰ gets to zero, the closer the Von Mises distribution becomes uniform. The larger ϰ gets, the more the Von Mises distribution approaches a normal distribution concentrated around the mean (0°).
This model is implemented in python and can be used as a building block for more complex agent based models that would rely on describing the movement of individuals with CRW.

After a little work experience, we realize that different kinds of people prefer different work environments: some enjoy a fast-paced challenge; some want to get by; and, others want to show off.

From that experience, we also realize that different kinds of people affect their work environments differently: some increase the pace; some slow it down; and, others make it about themselves.

This model concerns how three different kinds of people affect their work environment and how that work environment affects them in return. The model explores how this circular relation between people’s preferences and their environment creates patterns of association and performance over time.

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Developed as a part of a project in the University of Augsburg, Institute of Geography, it simulates the traffic in an intersection or junction which uses either regular traffic lights or traffic lights with a countdown timer. The model tracks the average speed of cars before and after traffic lights as well as the throughput.

This is a preliminary attempt in creating an Agent-Based Model of capital flows. This is based on the theory of capital flows based on interest-rate differentials. Foreign capital flows to a country with higher interest rates relative to another. The model shows how capital volatilty and wealth concentration are affected by the speed of capital flow, number of investors, magnitude of changes in interest rate due to capital flows and the interest differential threshold that investors set in deciding whether to move capital or not. Investors in the model are either “regional” investors (only investing in neighboring countries) and “global” investors (those who invest anywhere in the world).

In the future, the author hopes to extend this model to incorporate capital flow based on changes in macroeconomic fundamentals, exchange rate volatility, behavioral finance (for instance, herding behavior) and the presence of capital controls.

This documentation provides an overview and explanation of the NetLogo simulation code for modeling skilled workers’ migration in Iran. The simulation aims to explore the dynamics of skilled workers’ migration and their transition through various states, including training, employment, and immigration.

The flow of elite and talent migration, or “brain drain,” is a complex issue with far-reaching implications for developing countries. The decision to migrate is made due to various factors including economic opportunities, political stability, social factors and personal circumstances.
Measuring individual interests in the field of immigration is a complex task that requires careful consideration of various factors. The agent-based model is a useful tool for understanding the complex factors that are involved in talent migration. By considering the various social, economic, and personal factors that influence migration decisions, policymakers can provide more effective strategies to retain skilled and talented labor and promote sustainable growth in developing countries. One of the main challenges in studying the flow of elite migration is the complexity of the decision-making process and a set of factors that lead to migration decisions. Agent-based modeling is a useful tool for understanding how individual decisions can lead to large-scale migration patterns.

This is a replication of the altruistic trait selection model described in Pepper & Smuts (2000, 2002).

LUXE is a land-use change model featuring different levels of land market implementation. It integrates utility measures, budget constraints, competitive bidding, and market interactions to model land-use change in exurban environment.