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.

IMine is a flexible framework which can be adopt multiple criteria for convergence to solve Influence Minig problems. It can use any diffusion model, as well as resilience to compute the influence of a set of nodes base on the use case.
The code is written and tested on ‘R’ v3.5

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.

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.

What policy measures are effective in redistributing essential resources during crisis situations such as climate change impacts? We model a collective action institution with different rules for designing and organizing it, and make our analysis specific to various societal contexts.

Our model captures a generic societal context of unequal vulnerability and climate change impact in a stylized form. We represent a community of people who harvest and consume an essential resource to maintain their well-being. However, their ability to harvest the resource is not equal; people are characterized by a ‘resource access’ attribute whose values are uniformly distributed from 0 to 1 in the population. A person’s resource access value determines the amount of resource units they are able to harvest, and therefore the welfare levels they are able to attain. People travel to the centralized resource region and derive well-being or welfare, represented as an energy gain, by harvesting and consuming resource units.

The community is subject to a climate change impact event that occurs with a certain periodicity and over a certain duration. The capacity of resource units to regenerate diminishes during the impact events. Unequal capacities to access the essential resource results in unequal vulnerability among people with regards to their ability to maintain a sufficient welfare level, especially during impact events.

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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.