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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this agent-based model explores the dynamics of volunteer participation in urban community gardens, by combining behavioral theory and institutional theory
The impacts of income inequality can be seen everywhere, regardless of the country or the level of economic development. According to the literature review, income inequality has negative impacts in economic, social, and political variables. Notwithstanding of how well or not countries have done in reducing income inequality, none have been able to reduce it to a Gini Coefficient level of 0.2 or less.
This is the promise that a novel approach called Counterbalance Economics (CBE) provides without the need of increased taxes.
Based on the simulation, introducing the CBE into the Australian, UK, US, Swiss or German economies would result in an overall GDP increase of under 1% however, the level of inequality would be reduced from an average of 0.33 down to an average of 0.08. A detailed explanation of how to use the model, software, and data dependencies along with all other requirements have been included as part of the info tab in the model.
This model presents an autonomous, two-lane driving environment with a single lane-closure that can be toggled. The four driving scenarios - two baseline cases (based on the real-world) and two experimental setups - are as follows:
A generalized organizational agent- based model (ABM) containing both formal organizational hierarchy and informal social networks simulates organizational processes that occur over both formal network ties and informal networks.
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.
This is a variant of the PaleoscapeABM model available here written by Wren and Janssen. In this variant, we give projectile weapons to hunter and document where they discard them over time. Discard rate and location are influenced by probabilities of hitting/missing the prey, probabilities of damaging the weapon, and probabilities of carrying back embedded projectile armatures to the habitation camp with the body carcass.
The Netlogo model is a conceptualization of the Moria refugee camp, capturing the household demographics of refugees in the camp, a theoretical friendship network based on values, and an abstraction of their daily activities. The model then simulates how Covid-19 could spread through the camp if one refugee is exposed to the virus, utilizing transmission probabilities and the stages of disease progression of Covid-19 from susceptible to exposed to asymptomatic / symptomatic to mild / severe to recovered from literature. The model also incorporates various interventions - PPE, lockdown, isolation of symptomatic refugees - to analyze how they could mitigate the spread of the virus through the camp.
This code is for an agent-based model of collective problem solving in which agents with different behavior strategies, explore the NK landscape while they communicate with their peers agents. This model is based on the famous work of Lazer, D., & Friedman, A. (2007), The network structure of exploration and exploitation.
Netlogo model that shows how the cooling process determines the quality of a solution in simulated annealing using Metropolis algorithm.
The simulation is a variant of the “ToRealSim OD variants - base v2.7” base model, which is based on the standard DW opinion dynamics model (but with the differences that rather than one agent per tick randomly influencing another, all agents randomly influence one other per tick - this seems to make no difference to the outcomes other than to scale simulation time). Influence can be made one-way by turning off the two-way? switch
Various additional variations and sources of noise are possible to test robustness of outcomes to these (compared to DW model).
In this version agent opinions change following the empirical data collected in some experiments (Takács et al 2016).
Such an algorithm leaves no role for the uncertainties in other OD models. [Indeed the data from (Takács et al 2016) indicates that there can be influence even when opinion differences are large - which violates a core assumption of these]. However to allow better comparison with other such models there is a with-un? switch which allows uncertainties to come into play. If this is on, then influence (according to above algorithm) is only calculated if the opinion difference is less than the uncertainty. If an agent is influenced uncertainties are modified in the same way as standard DW models.
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