Our mission is to help computational modelers at all levels engage in the establishment and adoption of community standards and good practices for developing and sharing computational models. Model authors can freely publish their model source code in the Computational Model Library alongside narrative documentation, open science metadata, and other emerging open science norms that facilitate software citation, reproducibility, interoperability, and reuse. Model authors can also request peer review of their computational models to receive a DOI.
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 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 additional 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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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.
C++ and Netlogo models presented in G. Bravo (2011), “Agents’ beliefs and the evolution of institutions for common-pool resource management”. Rationality and Society 23(1).
LUXE is a land-use change model featuring different levels of land market implementation. It integrates utility measures, budget constraints, competitive bidding, and market interactions to model land-use change in exurban environment.
This agent-based model simulates a stoplight parrotfish population in a heavily-fished Caribbean coral reef. The model allows for the simulation of various fishing regulations and observation of population and catch outcomes. It was built using the structure and equations from several previously published models, including the work of Bozec et al. (2016) and Alonzo and Mangel (2004 and 2005). The initial model conditions are parameterized to population and fishing data collected in Buen Hombre, Dominican Republic by Tyler Pavlowich.
The Bronze Age Collapse model (BACO model) is written using free NetLogo software v.6.0.3. The purpose of using the BACO model is to develop a tool to identify and analyse the main factors that made the Late Bronze Age and Early Iron Age socio-ecological system resilient or vulnerable in the face of the environmental aridity recorded in the Aegean. The model explores the relationship between dependent and independent variables. Independent variables are: a) inter-annual rainfall variability for the Late Bronze Age and Early Iron Age in the eastern Mediterranean, b) intensity of raiding, c) percentage of marine, agricultural and other calorie sources included in the diet, d) soil erosion processes, e) farming assets, and d) storage capacity. Dependent variables are: a) human pressure for land, b) settlement patterns, c) number of commercial exchanges, d) demographic behaviour, and e) number of migrations.
This is a coupled conceptual model of agricultural land decision-making and incentivisation and species metacommunities.
We compare three model estimates for the time and treatment requirements to eliminate HCV among HIV-positive MSM in Victoria, Australia: a compartmental model; an ABM parametrized by surveillance data; and an ABM with a more heterogeneous population.
A dynmaic microsimulation model to project the UK population over time
Demographic microsimulation model used in speed tests against LIAM 2.
This is an agent-based model with two types of agents: customers and insurers. Insurers are price-takers who choose how much to spend on their service quality, and customers evaluate insurers based on premium, brand preference, and their perceived service quality. Customers are also connected in a small-world network and may share their opinions with their network.
The ABM contains two types of agents: insurers and customers. These act within the environment of a motor insurance market. At each simulation, the model undergoes the following steps:
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