A flexible framework for Agent-Based Models (ABM), the ‘epiworldR’ package provides methods for prototyping disease outbreaks and transmission models using a ‘C++’ backend, making it very fast. It supports multiple epidemiological models, including the Susceptible-Infected-Susceptible (SIS), Susceptible-Infected-Removed (SIR), Susceptible-Exposed-Infected-Removed (SEIR), and others, involving arbitrary mitigation policies and multiple-disease models. Users can specify infectiousness/susceptibility rates as a function of agents’ features, providing great complexity for the model dynamics. Furthermore, ‘epiworldR’ is ideal for simulation studies featuring large populations.

Swidden Farming is designed to explore the dynamics of agricultural land management strategies.

Agent-based models of organizational search have long investigated how exploitative and exploratory behaviors shape and affect performance on complex landscapes. To explore this further, we build a series of models where agents have different levels of expertise and cognitive capabilities, so they must rely on each other’s knowledge to navigate the landscape. Model A investigates performance results for efficient and inefficient networks. Building on Model B, it adds individual-level cognitive diversity and interaction based on knowledge similarity. Model C then explores the performance implications of coordination spaces. Results show that totally connected networks outperform both hierarchical and clustered network structures when there are clear signals to detect neighbor performance. However, this pattern is reversed when agents must rely on experiential search and follow a path-dependent exploration pattern.

Clostridioides Difficile Infection (CDI) stands out as a critical healthcare-associated infection with global implications. Effectively understanding the mechanisms of infection dissemination within healthcare units and hospitals is imperative to implement targeted containment measures. In this study, we address the limitations of prior research by Sulyok et al., where they delineated two distinct categories of surfaces as high-touch and low-touch fomites, and subsequently evaluated the viral spread contribution of each surface utilizing mathematical modeling and Ordinary Differential Equations (ODE). Acknowledging the indispensable role of spatial features and heterogeneity in the modeling of hospital and healthcare settings, we employ agent-based modeling to capture new insights. By incorporating spatial considerations and heterogeneous patients, we explore the impact of high-touch and low-touch surfaces on contamination transmission between patients. Furthermore, the study encompasses a comprehensive assessment of various cleaning protocols, with differing intervals and detergent cleaning efficacies, in order to identify the most optimal cleaning strategy and the most important factor amidst the array of alternatives.

This package implements a simplified artificial agent-based demographic model of the UK. Individuals of an initial population are subject to ageing, deaths, births, divorces and marriages. A specific case-study simulation is progressed with a user-defined simulation fixed step size on a hourly, daily, weekly, monthly basis or even an arbitrary user-defined clock rate. While the model can serve as a base model to be adjusted to realistic large-scale socio-economics, pandemics or social interactions-based studies mainly within a demographic context, the main purpose of the model is to explore and exploit capabilities of the state-of-the-art Agents.jl Julia package as well as other ecosystem of Julia packages like GlobalSensitivity.jl. Code includes examples for evaluating global sensitivity analysis using Morris and Sobol methods and local sensitivity analysis using OFAT and OAT methods. Multi-threaded parallelization is enabled for improved runtime performance.

Presented here is a socioeconomic agent-based model (ABM) to examine the Hollywood labor system as a network within a simulated movie labor market based on preferential attachment and compare the findings with 50 co-production ego networks during the 2015 movie year. Using the ABM, I test the role slight individual preference for racial and ethnic similarity within one’s own network at the microlevel and find that it is insufficient to explain the phenomena of racial and ethnic underrepresentation at the macrolevel. The ABM also includes the ability to test alternative explanations, such as overt opportunity loss as a possible explanation.

Within the archeological record for Bronze Age Chinese culture, there continues to be a gap in our understanding of the sudden rise of the Erlitou State from the previous late Longshan chiefdoms. In order to examine this period, I developed and used an agent-based model (ABM) to explore possible socio-politically relevant hypotheses for the gap between the demise of the late Longshan cultures and rise of the first state level society in East Asia. I tested land use strategy making and collective action in response to drought and flooding scenarios, the two plausible environmental hazards at that time. The model results show cases of emergent behavior where an increase in social complexity could have been experienced if a catastrophic event occurred while the population was sufficiently prepared for a different catastrophe, suggesting a plausible lead for future research into determining the life of the time period.

The ABM published here was originally developed in 2016 and its results published in the Proceedings of the 2017 Winter Simulation Conference.

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.

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
…

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.