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.

This is an agent-based model of a simple insurance market with two types of agents: customers and insurers. Insurers set premium quotes for each customer according to an estimation of their underlying risk based on past claims data. Customers either renew existing contracts or else select the cheapest quote from a subset of insurers. Insurers then estimate their resulting capital requirement based on a 99.5% VaR of their aggregate loss distributions. These estimates demonstrate an under-estimation bias due to the winner’s curse effect.

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.

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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The development and popularisation of new energy vehicles have become a global consensus. The shortage and unreasonable layout of electric vehicle charging infrastructure (EVCI) have severely restricted the development of electric vehicles. In the literature, many methods can be used to optimise the layout of charging stations (CSs) for producing good layout designs. However, more realistic evaluation and validation should be used to assess and validate these layout options. This study suggested an agent-based simulation (ABS) model to evaluate the layout designs of EVCI and simulate the driving and charging behaviours of electric taxis (ETs). In the case study of Shenzhen, China, GPS trajectory data were used to extract the temporal and spatial patterns of ETs, which were then used to calibrate and validate the actions of ETs in the simulation. The ABS model was developed in a GIS context of an urban road network with travelling speeds of 24 h to account for the effects of traffic conditions. After the high-resolution simulation, evaluation results of the performance of EVCI and the behaviours of ETs can be provided in detail and in summary. Sensitivity analysis demonstrates the accuracy of simulation implementation and aids in understanding the effect of model parameters on system performance. Maximising the time satisfaction of ET users and reducing the workload variance of EVCI were the two goals of a multiobjective layout optimisation technique based on the Pareto frontier. The location plans for the new CS based on Pareto analysis can significantly enhance both metrics through simulation evaluation.

The Communication-Based Model of Perceived Descriptive Norm Dynamics in Digital Networks (COMM-PDND) is an agent-based model specifically created to examine the dynamics of perceived descriptive norms in the context of digital network structures. The model, developed as part of a master’s thesis titled “The Dynamics of Perceived Descriptive Norms in Digital Network Publics: An Agent-Based Simulation,” emphasizes the critical role of communication processes in norm formation. It focuses on the role of communicative interactions in shaping perceived descriptive norms.

The COMM-PDND is tuned to explore the effects of normative deviance in digital social networks. It provides functionalities for manipulating agents according to their network position, and has a versatile set of customizable parameters, making it adaptable to a wide range of research contexts.

The model simulates the diffusion of four low-carbon energy technologies among households: photovoltaic (PV) solar panels, electric vehicles (EVs), heat pumps, and home batteries. We model household decision making as the decision marking of one person, the agent. The agent decides whether to adopt these technologies. Hereby, the model can be used to study co-adoption behaviour, thereby going beyond traditional diffusion models that focus on the adop-tion of single technologies. The combination of these technologies is of particular interest be-cause (1) using the energy generated by PV solar panels for EVs and heat pumps can reduce emissions associated with transport and heating, respectively, and (2) EVs, heat pumps, and home batteries can help to integrate PV solar panels in local electricity grids by offering flexible demand (EVs and heat pumps) and energy storage (home batteries and EVs), thereby reducing grid impacts and associated upgrading costs.

The purpose of the model is to represent realistic adoption and co-adoption behaviour. This is achieved by grounding the decision model on the risks-as-feelings model (Loewenstein et al., 2001), theory from environmental and social psychology, and empirically informing agent be-haviour by survey-data among 1469 people in the Swiss region Romandie.

The model can be used to construct scenarios for the diffusion of the four low-carbon energy technologies depending on different contexts, and as a virtual experimentation environment for ex ante evaluation of policy interventions to stimulate adoption and co-adoption.

This model is linked to the paper “The Epistemic Role of Diversity in Juries: An Agent-Based Model”. There are many version of this model, but the current version focuses on the role of diversity in whether juries reach correct verdicts. Using this agent-based model, we argue that diversity can play at least four importantly different roles in affecting jury verdicts. (1) Where different subgroups have access to different information, equal representation can strengthen epistemic jury success. (2) If one subgroup has access to particularly strong evidence, epistemic success may demand participation by that group. (3) Diversity can also reduce the redundancy of the information on which a jury focuses, which can have a positive impact. (4) Finally, and most surprisingly, we show that limiting communication between diverse groups in juries can favor epistemic success as well.

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.

The model generates disaggregated traffic flows of pedestrians, simulating their daily mobility behaviour represented as probabilistic rules. Various parameters of physical infrastructure and travel behaviour can be altered and tested. This allows predicting potential shifts in traffic dynamics in a simulated setting. Moreover, assumptions in decision-making processes are general for mid-sized cities and can be applied to similar areas.

Together with the model files, there is the ODD protocol with the detailed description of model’s structure. Check the associated publication for results and evaluation of the model.

Installation
Download GAMA-platform (GAMA1.8.2 with JDK version) from https://gama-platform.github.io/. The platform requires a minimum of 4 GB of RAM.
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