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.
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This software simulates cars and bicycles as traffic participants while crossing different crossroad designs such as roundabouts, protected crossroads and standard crossroads. It is written in Netlogo 6.2 and aims to identify safety characteristics of these layouts using agent-based modeling. Participants track the line of sight to each other and print them as an output alongside with the adjacent destination, used layout, count of collisions/cars/bicycles and time.
Detailed information can be found within the info tab of the program itself.
This spatially explicit agent-based model addresses how effective foraging radius (r_e) affects the effective size–and thus the equilibrium cultural diversity–of a structured population composed of central-place foraging groups.
This model examines the potential impact of market collapse on the economy and demography of fishing households in the Logone Floodplain, Cameroon.
MASTOC-LLM extends the classic Multi-Agent System Tragedy of the Commons (MASTOC) model by replacing hard-coded behavioral rules with autonomous decision-making powered by large language models (LLMs). Three heterogeneous agents manage herds of cows on a shared grassland commons. Each tick, an agent receives a structured prompt describing current resource levels, its own herd size, peer behavior, and — optionally — a rolling memory of recent rounds and messages from neighboring agents. The LLM returns a stocking decision (add, remove, or hold cows) together with a natural-language rationale and, when communication is enabled, a short message to broadcast to peers.
The model is designed to test whether LLM agents spontaneously develop Ostrom-style common-pool resource governance (mutual monitoring, graduated sanctions, graduated rule revision) or instead fall into identifiable failure modes. Preliminary experiments with Claude Haiku 4.5, GPT-5.4-mini, and DeepSeek R1:32b have revealed four recurring collapse patterns — Cooperative Paralysis, Defection Cascade, Overshoot-Panic, and Hybrid Architecture Failure — whose onset timing is sensitive to memory length, inter-agent communication, and the post-training alignment approach of the underlying model.
MASTOC-LLM is intended as a laboratory for generative agent-based modelling (GABM) methodology: it provides a clean, well-understood commons baseline against which LLM behavioral hypotheses can be systematically tested and compared across models, parameter sweeps, and alignment regimes.
A curious aspect of the Covid-19 pandemic is the clustering of outbreaks. Evidence suggests that 80\% of people who contract the virus are infected by only 19% of infected individuals, and that the majority of infected individuals faile to infect another person. Thus, the dispersion of a contagion, $k$, may be of more use in understanding the spread of Covid-19 than the reproduction number, R0.
The Virus Transmission with Super-spreaders model, written in NetLogo, is an adaptation of the canonical Virus Transmission on a Network model and allows the exploration of various mitigation protocols such as testing and quarantines with both homogenous transmission and heterogenous transmission.
The model consists of a population of individuals arranged in a network, where both population and network degree are tunable. At the start of the simulation, a subset of the population is initially infected. As the model runs, infected individuals will infect neighboring susceptible individuals according to either homogenous or heterogenous transmission, where heterogenous transmission models super-spreaders. In this case, k is described as the percentage of super-spreaders in the population and the differing transmission rates for super-spreaders and non super-spreaders. Infected individuals either recover, at which point they become resistant to infection, or die. Testing regimes cause discovered infected individuals to quarantine for a period of time.
A global model of the 1918-19 Influenza Pandemic. It can be run to match history or explore counterfactual questions about the influence of World War I on the dynamics of the epidemic. Explores two theories of the location of the initial infection.
Captures interplay between fixed ethnic markers and culturally evolved tags in the evolution of cooperation and ethnocentrism. Agents evolve cultural tags, behavioural game strategies and in-group definitions. Ethnic markers are fixed.
In this Repast model the ‘Consumat’ cognitive framework is applied to an ABM of the Dutch car market. Different policy scenarios can be selected or created to examine their effect on the diffusion of EVs.
A simple model that aims to demonstrate the influence of agri-environmental payments on land-use patterns in a virtual landscape. The landscape consists of grassland (which can be managed extensively or intensively) and a river. Agri-environmental payments are provided for extensive management of grassland. Additionally, there are boni for (a) extensive grassland in proximity of the river; and (b) clusters (“agglomerations”) of extensive grassland. The farmers, who own randomly distributed grassland patches, make decisions either on the basis of simple income maximization or they maximize only up to an income threshold beyond which they seize making changes in management. The resulting landscape pattern is evaluated by means of three simple models for (a) agricultural yield, (b) habitat/biodiversity and (c) water quality. The latter two correspond to the two boni. The model has been developed within a small project called Aligning Agent-Based Modelling with Multi-Objective Land-Use Allocation (ALABAMA).
This ABM re-implements and extends the simulation model of peer review described in Squazzoni & Gandelli (Squazzoni & Gandelli, 2013 - doi:10.18564/jasss.2128) (hereafter: ‘SG’). The SG model was originally developed for NetLogo and is also available in CoMSES at this link.
The purpose of the original SG model was to explore how different author and reviewer strategies would impact the outcome of a journal peer review system on an array of dimensions including peer review efficacy, efficiency and equality. In SG, reviewer evaluation consists of a continuous variable in the range [0,1], and this evaluation scale is the same for all reviewers. Our present extension to the SG model allows to explore the consequences of two more realistic assumptions on reviewer evaluation: (1) that the evaluation scale is discrete (e.g. like in a Likert scale); (2) that there may be differences among their interpretation of the grades of the evaluation scale (i.e. that the grade language is heterogeneous).
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