Our mission is to help computational modelers develop, document, and share their computational models in accordance with community standards and good open science and software engineering practices. Model authors can publish their model source code in the Computational Model Library with narrative documentation as well as metadata that supports open science and emerging norms that facilitate software citation, computational reproducibility / frictionless reuse, and interoperability. Model authors can also request private peer review of their computational models. Models that pass peer review receive a DOI once published.
All users of models published in the library must cite model authors when they use and benefit from their code.
Please check out our model publishing tutorial and feel free to contact us if you have any questions or concerns about publishing your model(s) in the Computational Model Library.
We also maintain a curated database of over 7500 publications of agent-based and individual based models with detailed metadata on availability of code and bibliometric information on the landscape of ABM/IBM publications that we welcome you to explore.
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Provided is a landscape of properties where pastoralists make decisions how much livestock they put on their property and how much to suppress fire from occuring. Rangelands can be grass dominated, or unproductive shrubb dominated. Overgrazing and fire suppresion lead to shrub dominated landscapes. What management strategies evolve, and how is this impacted by policies?
The model is discussed in Introduction to Agent-Based Modeling by Marco Janssen. For more information see https://intro2abm.com/.
CHALMS simulates housing and land market interactions between housing consumers, developers, and farmers in a growing ex-urban area.
MOOvPOPsurveillance was developed as a tool for wildlife agencies to guide collection and analysis of disease surveillance data that relies on non-probabilistic methods like harvest-based sampling.
RHEA aims to provide a methodological platform to simulate the aggregated impact of households’ residential location choice and dynamic risk perceptions in response to flooding on urban land markets. It integrates adaptive behaviour into the spatial landscape using behavioural theories and empirical data sources. The platform can be used to assess: how changes in households’ preferences or risk perceptions capitalize in property values, how price dynamics in the housing market affect spatial demographics in hazard-prone urban areas, how structural non-marginal shifts in land markets emerge from the bottom up, and how economic land use systems react to climate change. RHEA allows direct modelling of interactions of many heterogeneous agents in a land market over a heterogeneous spatial landscape. As other ABMs of markets it helps to understand how aggregated patterns and economic indices result from many individual interactions of economic agents.
The model could be used by scientists to explore the impact of climate change and increased flood risk on urban resilience, and the effect of various behavioural assumptions on the choices that people make in response to flood risk. It can be used by policy-makers to explore the aggregated impact of climate adaptation policies aimed at minimizing flood damages and the social costs of flood risk.
A dynamic model of social network formation on single-layer and multiplex networks with structural incentives that vary over time.
The purpose of the model is to study the impact of global food trade on food and nutrition security in countries around the world. It will incorporate three main aspects of trade between countries, including a country’s wealth, geographic location, and its trade relationships with other countries (past and ongoing), and can be used to study food and nutrition security across countries in various scenarios, such as climate change, sustainable intensification, waste reduction and dietary change.
This is a basic Susceptible, Infected, Recovered (SIR) model. This model explores the spread of disease in a space. In particular, it explores how changing assumptions about the number of susceptible people, starting number of infected people, as well as the disease’s infection probability, and average duration of infection. The model shows that the interactions of agents can drastically affect the results of the model.
We used it in our course on COVID-19: https://www.csats.psu.edu/science-of-covid19
Status-power dynamics on a playground, resulting in a status landscape with a gender status gap. Causal: individual (beauty, kindness, power), binary (rough-and-tumble; has-been-nice) or prior popularity (status). Cultural: acceptability of fighting.
NetPlop is a presentation editor built entirely in NetLogo, an agent-based modelling environment. The NetPlop Editor includes a variety of tools to design slide decks, and the Viewer allows these decks to be dis-played to an enraptured audience. A key feature of NetPlop is the ability to embed agent-based models. NetPlop was developed for SIGBOVIK 2021.
Using Sierra Leone as a test case, the purpose of the model is to explore the role of geography in a resource-driven war. An ABM is integrated with geographic information systems (GIS) for this purpose.
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