This model is an agent-based simulation written in Python 2.7, which simulates the cost of social care in an ageing UK population. The simulation incorporates processes of population change which affect the demand for and supply of social care, including health status, partnership formation, fertility and mortality. Fertility and mortality rates are drawn from UK population data, then projected forward to 2050 using the methods developed by Lee and Carter 1992.

The model demonstrates that rising life expectancy combined with lower birthrates leads to growing social care costs across the population. More surprisingly, the model shows that the oft-proposed intervention of raising the retirement age has limited utility; some reductions in costs are attained initially, but these reductions taper off beyond age 70. Subsequent work has enhanced and extended this model by adding more detail to agent behaviours and familial relationships.

The version of the model provided here produces outputs in a format compatible with the GEM-SA uncertainty quantification software by Kennedy and O’Hagan. This allows sensitivity analyses to be performed using Gaussian Process Emulation.

This agent-based simulation model for group interaction is rooted in social psychological theory. The
model integrates affect control theory with networked interaction structures and sequential behavior protocols as they are often encountered in task groups. By expressing status hierarchy through network structure we build a bridge between expectation states theory and affect control theory, and are able to reproduce central results from the expectation states research program in sociological social psychology. Furthermore, we demonstrate how the model can be applied to analyze specialized task groups or sub-cultural domains by combining it with empirical data sources. As an example, we simulate groups of open-source software developers and analyze how cultural expectations influence the occupancy of high status positions in these groups.

EMMIT is an end-user developed agent-based simulation of malaria transmission. The simulation’s development is a case study demonstrating an approach for non-technical investigators to easily develop useful simulations of complex public health problems. We focused on malaria transmission, a major global public health problem, and insecticide resistance (IR), a major problem affecting malaria control. Insecticides are used to reduce transmission of malaria caused by the Plasmodium parasite that is spread by the Anopheles mosquito. However, the emergence and spread of IR in a mosquito population can diminish the insecticide’s effectiveness. IR results from mutations that produce behavioral changes or biochemical changes (such as detoxification enhancement, target site alterations) in the mosquito population that provide resistance to the insecticide. Evolutionary selection for the IR traits reduces the effectiveness of an insecticide favoring the resistant mosquito population. It has been suggested that biopesticides, and specifically those that are Late Life Acting (LLA), could address this problem. LLA insecticides exploit Plasmodium’s approximate 10-day extrinsic incubation period in the mosquito vector, a delay that limits malaria transmission to older infected mosquitoes. Since the proposed LLA insecticide delays mosquito death until after the exposed mosquito has a chance to produce several broods of offspring, reducing the selective pressure for resistance, it delays IR development and gives the insecticide longer effectivity. Such insecticides are designed to slow the evolution of IR thus maintaining their effectiveness for malaria control. For the IR problem, EMMIT shows that an LLA insecticide could work as intended, but its operational characteristics are critical, primarily the mean-time-to-death after exposure and the associated standard deviation. We also demonstrate the simulation’s extensibility to other malaria control measures, including larval source control and policies to mitigate the spread of IR. The simulation was developed using NetLogo as a case study of a simple but useful approach to public health research.

The Urban Traffic Simulator is an agent-based model developed in the Unity platform. The model allows the user to simulate several autonomous vehicles (AVs) and tune granular parameters such as vehicle downforce, adherence to speed limits, top speed in mph and mass. The model allows researchers to tune these parameters, run the simulator for a given period and export data from the model for analysis (an example is provided in Jupyter Notebook).

The data the model is currently able to output are the following:

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The purpose of the model is to collect information on human decision-making in the context of coalition formation games. The model uses a human-in-the-loop approach, and a single human is involved in each trial. All other agents are controlled by the ABMSCORE algorithm (Vernon-Bido and Collins 2020), which is an extension of the algorithm created by Collins and Frydenlund (2018). The glove game, a standard cooperative game, is used as the model scenario.

The intent of the game is to collection information on the human players behavior and how that compares to the computerized agents behavior. The final coalition structure of the game is compared to an ideal output (the core of the games).

The purpose of the model is to generate coalition structures of different glove games, using a specially designed algorithm. The coalition structures can be are later analyzed by comparing them to core partitions of the game used. Core partitions are coalition structures where no subset of players has an incentive to form a new coalition.

The algorithm used in this model is an advancement of the algorithm found in Collins & Frydenlund (2018). It was used used to generate the results in Vernon-Bido & Collins (2021).

NetLogo model corresponding to the JASSS article “Agent-Based Simulation of West Asian Urban Dynamics: Impact of Refugees”

The agent-based simulation is set to work on information that is either (a) functional, (b) pseudo-functional, (c) dysfunctional, or (d) irrelevant. The idea is that a judgment on whether information falls into one of the four categories is based on the agent and its network. In other words, it is the agents who interprets a particular information as being (a), (b), (c), or (d). It is a decision based on an exchange with co-workers. This makes the judgment a socially-grounded cognitive exercise. The uFUNK 1.0.2 Model is set on an organization where agent-employee work on agent-tasks.

IOP 2.1.2 is an agent-based simulation model designed to explore the relations between (1) employees, (2) tasks and (3) resources in an organizational setting. By comparing alternative cognitive strategies in the use of resources, employees face increasingly demanding waves of tasks that derive by challenges the organization face to adapt to a turbulent environment. The assumption tested by this model is that a successful organizational adaptation, called plastic, is necessarily tied to how employees handle pressure coming from existing and new tasks. By comparing alternative cognitive strategies, connected to ‘docility’ (Simon, 1993; Secchi, 2011) and ‘extended’ cognition (Clark, 2003, Secchi & Cowley, 2018), IOP 2.1.2 is an attempt to indicate which strategy is most suitable and under which scenario.

Juan Castilla-Rho et al. (2015) developed a platform, named FLowLogo, which integrates a 2D, finite-difference solution of the governing equations of groundwater flow with agent-based simulation. We used this model for Rafsanjan Aquifer, which is located in an arid region in Iran. To use FLowLogo for a real case study, one needs to add GIS shapefiles of boundary conditions and modify the code written in NetLogo a little bit. The FlowLogo model used in our research is presented here.