Computational Model Library

Studies of colonization processes in past human societies often use a standard population model in which population is represented as a single quantity. Real populations in these processes, however, are structured with internal classes or stages, and classes are sometimes created based on social differentiation. In this present work, information about the colonization of old Providence Island was used to create an agent-based model of the colonization process in a heterogeneous environment for a population with social differentiation. Agents were socially divided into two classes and modeled with dissimilar spatial clustering preferences. The model and simulations assessed the importance of gregarious behavior for colonization processes conducted in heterogeneous environments by socially-differentiated populations. Results suggest that in these conditions, the colonization process starts with an agent cluster in the largest and most suitable area. The spatial distribution of agents maintained a tendency toward randomness as simulation time increased, even when gregariousness values increased. The most conspicuous effects in agent clustering were produced by the initial conditions and behavioral adaptations that increased the agent capacity to access more resources and the likelihood of gregariousness. The approach presented here could be used to analyze past human colonization events or support long-term conceptual design of future human colonization processes with small social formations into unfamiliar and uninhabited environments.

Sugarscape with spice

Marco Janssen | Published Tue Jan 14 17:09:12 2020 | Last modified Fri Sep 18 16:31:42 2020

This is a variation of the Sugarspace model of Axtell and Epstein (1996) with spice and trade of sugar and spice. The model is not an exact replication since we have a somewhat simpler landscape of sugar and spice resources included, as well as a simple reproduction rule where agents with a certain accumulated wealth derive an offspring (if a nearby empty patch is available).
The model is discussed in Introduction to Agent-Based Modeling by Marco Janssen. For more information see https://intro2abm.com/

The model simulates agents in a spatial environment competing for a common resource that grows on patches. The resource is converted to energy, which is needed for performing actions and for surviving.

NetLogo agent-based model to simulate the transmission of COVID-19 in a university dormitory. User can set the number of initial students, buildings, floors, rooms, number of initially infected, and transmission rate. They can also test the effect of masks, sanitizations, elevator allowance, and visits on the effect of the SEIR curve.

The purpose of this model is to explain the post-disaster recovery of households residing in their own single-family homes and to predict households’ recovery decisions from drivers of recovery. Herein, a household’s recovery decision is repair/reconstruction of its damaged house to the pre-disaster condition, waiting without repair/reconstruction, or selling the house (and relocating). Recovery drivers include financial conditions and functionality of the community that is most important to a household. Financial conditions are evaluated by two categories of variables: costs and resources. Costs include repair/reconstruction costs and rent of another property when the primary house is uninhabitable. Resources comprise the money required to cover the costs of repair/reconstruction and to pay the rent (if required). The repair/reconstruction resources include settlement from the National Flood Insurance (NFI), Housing Assistance provided by the Federal Emergency Management Agency (FEMA-HA), disaster loan offered by the Small Business Administration (SBA loan), a share of household liquid assets, and Community Development Block Grant Disaster Recovery (CDBG-DR) fund provided by the Department of Housing and Urban Development (HUD). Further, household income determines the amount of rent that it can afford. Community conditions are assessed for each household based on the restoration of specific anchors. ASNA indexes (Nejat, Moradi, & Ghosh 2019) are used to identify the category of community anchors that is important to a recovery decision of each household. Accordingly, households are indexed into three classes for each of which recovery of infrastructure, neighbors, or community assets matters most. Further, among similar anchors, those anchors are important to a household that are located in its perceived neighborhood area (Moradi, Nejat, Hu, & Ghosh 2020).

Peer reviewed BAM: The Bottom-up Adaptive Macroeconomics Model

Alejandro Platas López | Published Tue Jan 14 17:04:32 2020 | Last modified Sun Jul 26 00:26:21 2020

Overview

Purpose

Modeling an economy with stable macro signals, that works as a benchmark for studying the effects of the agent activities, e.g. extortion, at the service of the elaboration of public policies..

Urban/Rural Adaptive Culture Model

Nick LaBerge | Published Sun Jul 19 20:29:41 2020

Contains python3 code used to replicate the culture model from the JASSS submission: “Modeling Cultural Dissemination and Divergence between Rural and Urban Regions.”

The MML is a hybrid modeling environment that couples an agent-based model of small-holder agropastoral households and a cellular landscape evolution model that simulates changes in erosion/deposition, soils, and vegetation.

Peer reviewed MIOvCWD

Aniruddha Belsare | Published Fri Dec 13 20:24:03 2019

MIOvCWD is a spatially-explicit, agent-based model designed to simulate the spread of chronic wasting disease (CWD) in Michigan’s white-tailed deer populations. CWD is an emerging prion disease of North American cervids (white-tailed deer Odocoileus virginianus, mule deer Odocoileus hemionus, and elk Cervus elaphus) that is being actively managed by wildlife agencies in most states and provinces in North America, including Michigan. MIOvCWD incorporates features like deer population structure, social organization and behavior that are particularly useful to simulate CWD dynamics in regional deer populations.

In the face of the COVID-19 pandemic, public health authorities around the world have experimented, in a short period of time, with various combinations of interventions at different scales. However, as the pandemic continues to progress, there is a growing need for tools and methodologies to quickly analyze the impact of these interventions and answer concrete questions regarding their effectiveness, range and temporality.

COMOKIT, the COVID-19 modeling kit, is such a tool. It is a computer model that allows intervention strategies to be explored in silico before their possible implementation phase. It can take into account important dimensions of policy actions, such as the heterogeneity of individual responses or the spatial aspect of containment strategies.

In COMOKIT, built using the agent-based modeling and simulation platform GAMA, the profiles, activities and interactions of people, person-to-person and environmental transmissions, individual clinical statuses, public health policies and interventions are explicitly represented and they all serve as a basis for describing the dynamics of the epidemic in a detailed and realistic representation of space.

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