The targeted subsidies plan model is based on the economic concept of targeted subsidies.

The targeted subsidies plan model simulates the distribution of subsidies among households in a community over several years. The model assumes that the government allocates a fixed amount of money each year for the purpose of distributing cash subsidies to eligible households. The eligible households are identified by dividing families into 10 groups based on their income, property, and wealth. The subsidy is distributed to the first four groups, with the first group receiving the highest subsidy amount. The model simulates the impact of the subsidy distribution process on the income and property of households in the community over time.

The model simulates a community of 230 households, each with a household income and wealth that follows a power-law distribution. The number of household members is modeled by a normal distribution. The model allocates a fixed amount of money each year for the purpose of distributing cash subsidies among eligible households. The eligible households are identified by dividing families into 10 groups based on their income, property, and wealth. The subsidy is distributed to the first four groups, with the first group receiving the highest subsidy amount.
The model runs for a period of 10 years, with the subsidy distribution process occurring every month. The subsidy received by each household is assumed to be spent, and a small portion may be saved and added to the household’s property. At the end of each year, the grouping of households based on income and assets is redone, and a number of families may be moved from one group to another based on changes in their income and property.
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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.

Background: Establishing a human settlement on Mars is an incredibly complex engineering problem. The inhospitable nature of the Martian environment requires any habitat to be largely self-sustaining. Beyond mining a few basic minerals and water, the colonizers will be dependent on Earth resupply and replenishment of necessities via technological means, i.e., splitting Martian water into oxygen for breathing and hydrogen for fuel. Beyond the technical and engineering challenges, future colonists will also face psychological and human behavior challenges.
Objective: Our goal is to better understand the behavioral and psychological interactions of future Martian colonists through an Agent-Based Modeling (ABM simulation) approach. We seek to identify areas of consideration for planning a colony as well as propose a minimum initial population size required to create a stable colony.
Methods: Accounting for engineering and technological limitations, we draw on research regarding high performing teams in isolated and high stress environments (ex: submarines, Arctic exploration, ISS, war) to include the 4 NASA personality types within the ABM. Interactions between agents with different psychological profiles are modeled at the individual level, while global events such as accidents or delays in Earth resupply affect the colony as a whole.
Results: From our multiple simulations and scenarios (up to 28 Earth years), we found that an initial population of 22 was the minimum required to maintain a viable colony size over the long run. We also found that the Agreeable personality type was the one more likely to survive.
Conclusion We developed a simulation with easy to use GUI to explore various scenarios of human interactions (social, labor, economic, psychological) on a future colony on Mars. We included technological and engineering challenges, but our focus is on the behavioral and psychological effects on the sustainability of the colony on the long run. We find, contrary to other literature, that the minimum number of people with all personality types that can lead to a sustainable settlement is in the tens and not hundreds.

Chicago’s demographic, neighborhood, sex risk behaviors, sexual network data, and HIV prevention and treatment cascade information from 2015 were integrated as input to a new agent-based model (ABM) called the Levers-of-HIV-Model (LHM). This LHM, written in NetLogo, forms patterns of sexual relations among Men who have Sex with Men (MSM) based on static traits (race/ethnicity, and age) and dynamic states (sexual relations and practices) that are found in Chicago. LHM’s five modules simulate and count new infections at the two marker years of 2023 and 2030 for a wide range of distinct scenarios or levers, in which the levels of PrEP and ART linkage to care, retention, and adherence or viral load are increased over time from the 2015 baseline levels.

This model presents the simulation model of a city in the context of overtourism. The study area is the city of Santa Marta in Colombia. The purpose is to illustrate the spatial and temporal distribution of population and tourists in the city. The simulation analyzes emerging patterns that result from the interaction between critical components in the touristic urban system: residents, urban space, touristic sites, and tourists. The model is an Agent-Based Model (ABM) with the GAMA software. Also, it used public input data from statistical centers, geographical information systems, tourist websites, reports, and academic articles. The ABM includes assessing some measures used to address overtourism. This is a field of research with a low level of analysis for destinations with overtourism, but the ABM model allows it. The results indicate that the city has a high risk of overtourism, with spatial and temporal differences in the population distribution, and it illustrates the effects of two management measures of the phenomenon on different scales. Another interesting result is the proposed tourism intensity indicator (OVsm), taking into account that the tourism intensity indicators used by the literature on overtourism have an overestimation of tourism pressures.

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 an extended replication of Abelson’s and Bernstein’s early computer simulation model of community referendum controversies which was originally published in 1963 and often cited, but seldom analysed in detail. This replication is in NetLogo 6.3.0, accompanied with an ODD+D protocol and class and sequence diagrams.

This replication replaces the original scales for attitude position and interest in the referendum issue which were distributed between 0 and 1 with values that are initialised according to a normal distribution with mean 0 and variance 1 to make simulation results easier compatible with scales derived from empirical data collected in surveys such as the European Value Study which often are derived via factor analysis or principal component analysis from the answers to sets of questions.

Another difference is that this model is not only run for Abelson’s and Bernstein’s ten week referendum campaign but for an arbitrary time in order that one can find out whether the distributions of attitude position and interest in the (still one-dimensional) issue stabilise in the long run.

Prior to COVID-19, female academics accounted for 45% of assistant professors, 37% of associate professors, and 21% of full professors in business schools (Morgan et al., 2021). The pandemic arguably widened this gender gap, but little systemic data exists to quantify it. Our study set out to answer two questions: (1) How much will the COVID-19 pandemic have impacted the gender gap in U.S. business school tenured and tenure-track faculty? and (2) How much will institutional policies designed to help faculty members during the pandemic have affected this gender gap? We used agent-based modeling coupled with archival data to develop a simulation of the tenure process in business schools in the U.S. and tested how institutional interventions would affect this gender gap. Our simulations demonstrated that the gender gap in U.S. business schools was on track to close but would need further interventions to reach equality (50% females). In the long-term picture, COVID-19 had a small impact on the gender gap, as did dependent care assistance and tenure extensions (unless only women received tenure extensions). Changing performance evaluation methods to better value teaching and service activities and increasing the proportion of female new hires would help close the gender gap faster.

A simple model is constructed using C# in order to to capture key features of market dynamics, while also producing reasonable results for the individual insurers. A replication of Taylor’s model is also constructed in order to compare results with the new premium setting mechanism. To enable the comparison of the two premium mechanisms, the rest of the model set-up is maintained as in the Taylor model. As in the Taylor example, homogeneous customers represented as a total market exposure which is allocated amongst the insurers.

In each time period, the model undergoes the following steps:
1. Insurers set competitive premiums per exposure unit
2. Losses are generated based on each insurer’s share of the market exposure
3. Accounting results are calculated for each insurer
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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.