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.

An agent-based framework to simulate the diffusion process of a piece of misinformation according to the SBFC model in which the fake news and its debunking compete in a social network. Considering new classes of agents, this model is closer to reality and proposed different strategies how to mitigate and control misinformation.

This is a simulation of an insurance market where the premium moves according to the balance between supply and demand. In this model, insurers set their supply with the aim of maximising their expected utility gain while operating under imperfect information about both customer demand and underlying risk distributions.

There are seven types of insurer strategies. One type follows a rational strategy within the bounds of imperfect information. The other six types also seek to maximise their utility gain, but base their market expectations on a chartist strategy. Under this strategy, market premium is extrapolated from trends based on past insurance prices. This is subdivided according to whether the insurer is trend following or a contrarian (counter-trend), and further depending on whether the trend is estimated from short-term, medium-term, or long-term data.

Customers are modelled as a whole and allocated between insurers according to available supply. Customer demand is calculated according to a logit choice model based on the expected utility gain of purchasing insurance for an average customer versus the expected utility gain of non-purchase.

We develop an agent-based model (U-TRANS) to simulate the transition of an abstract city under an industrial revolution. By coupling the labour and housing markets, we propose a holistic framework that incorporates the key interacting factors and micro processes during the transition. Using U-TRANS, we look at five urban transition scenarios: collapse, weak recovery, transition, enhanced training and global recruit, and find the model is able to generate patterns observed in the real world. For example, We find that poor neighbourhoods benefit the most from growth in the new industry, whereas the rich neighbourhoods do better than the rest when the growth is slow or the situation deteriorates. We also find a (subtle) trade-off between growth and equality. The strategy to recruit a large number of skilled workers globally will lead to higher growth in GDP, population and human capital, but it will also entail higher inequality and market volatility, and potentially create a divide between the local and international workers. The holistic framework developed in this paper will help us better understand urban transition and detect early signals in the process. It can also be used as a test-bed for policy and growth strategies to help a city during a major economic and technological revolution.

This is an extension of the original RAGE model (Dressler et al. 2018), where we add learning capabilities to agents, specifically learning-by-doing and social learning (two processes central to adaptive (co-)management).

The extension module is applied to smallholder farmers’ decision-making - here, a pasture (patch) is the private property of the household (agent) placed on it and there is no movement of the households. Households observe the state of the pasture and their neighrbours to make decisions on how many livestock to place on their pasture every year. Three new behavioural types are created (which cannot be combined with the original ones): E-RO (baseline behaviour), E-LBD (learning-by-doing) and E-RO-SL1 (social learning). Similarly to the original model, these three types can be compared regarding long-term social-ecological performance. In addition, a global strategy switching option (corresponding to double-loop learning) allows users to study how behavioural strategies diffuse in a heterogeneous population of learning and non-learning agents.

An important modification of the original model is that extension agents are heterogeneous in how they deal with uncertainty. This is represented by an agent property, called the r-parameter (household-risk-att in the code). The r-parameter is catch-all for various factors that form an agent’s disposition to act in a certain way, such as: uncertainty in the sensing (partial observability of the resource system), noise in the information received, or an inherent characteristic of the agent, for instance, their risk attitude.

Many archaeological assemblages from the Iberian Peninsula dated to the Last Glacial Maximum contain large quantities of European rabbit (Oryctolagus cuniculus) remains with an anthropic origin. Ethnographic and historic studies report that rabbits may be mass-collected through warren-based harvesting involving the collaborative participation of several persons.

We propose and implement an Agent-Based Model grounded in the Optimal Foraging Theory and the Diet Breadth Model to examine how different warren-based hunting strategies influence the resulting human diets.

Particularly, this model is developed to test the following hypothesis: What if an age and/or gender-based division of labor was adopted, in which adult men focus on large prey hunting, and women, elders and children exploit warrens?

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The western honey bee Apis mellifera is the most important pollinator in the world. The biggest threat to managed honey bees is the ectoparasitic mite Varroa destructor and the viruses DWV (Deformed Wing Virus) and APV (Acute Paralysis Virus) it transmits. Untreated honey bee colonies are expected to die within one to three years. This led to the development of strategies for beekeepers to control the Varroa mite in honey bee colonies and ensure the health and survival of their bee colonies, so called Good Beekeeping Practice. The aim of the extension of BEEHAVE was to represent the Good Beekeeping Practice of Varroa control in Germany. The relevant measures within the Varroa control strategies are drone brood removal as a Varroa trap and the treatment of bee colonies with organic acaricides (formic and oxalic acid) to kill the mites. This extension improves BEEHAVE and builds a bridge between beekeepers in practice and in the modelling world. It vastly contributes to the future use of BEEHAVE in beekeeping education in Germany.

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.

A model of the emergence of intersectional life course inequalities through transitions in the workplace. It explores LGBTQ citizens’ career outcomes and trajectories in relation to several mediating factors: (i) workplace discrimination; (ii) social capital; (iii) policy interventions (i.e., workplace equality, diversity, and inclusion policies); (iv) and LGBTQ employees’ behaviours in response to discrimination (i.e., moving workplaces and/or different strategies for managing the visibility of their identity).

The purpose of this agent-based model is to explore the emergent phenomena associated with scientific publication, including quantity and quality, from different academic types based on their publication strategies.