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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This model is used to investigate the role of opinion leader. More specifically: the influence of ‘innovative behavior’, ‘weigth of normative influence’, ‘better product judgment’, ‘number of opinion
The CONSERVAT model evaluates the effect of social influence among farmers in the Lake Naivasha basin (Kenya) on the spatiotemporal diffusion pattern of soil conservation effort levels and the resulting reduction in lake sedimentation.
This is a social trust model for investigating the social relationships and social networks in the real world and in social media.
The Inspection Model represents a basic food safety system where inspectors, consumers and stores interact. The purpose of the model is to provide insight into an optimal level of inspectors in a food system by comparing three search strategies.
The Inspection Model represents a basic food safety system where inspectors, consumers and stores interact. The purpose of the model is to provide insight into an optimal level of inspectors in a food system by comparing three search strategies.
The Inspection Model represents a basic food safety system where inspectors, consumers and stores interact. The purpose of the model is to provide insight into an optimal level of inspectors in a food system by comparing three search strategies.
The Friendship Field model aims at modelling friendship formation based on three factors: Extraversion, Resemblance and Status, where social interaction is motivated by the Social Battery. Social Battery is one’s energy and motivation to engage in social contact. Since social contact is crucial for friendship formation, the model included Social Battery to affect social interactions. To our best knowledge, Social Battery is a yet unintroduced concept in research while it is a dynamic factor influencing the social interaction besides one’s characteristics. Extraverts’ Social Batteries charge while interacting and exhaust while being alone. Introverts’ Social Batteries charge while being alone and exhaust while interacting. The aim of the model is to illustrate the concept of Social Battery. Moreover, the Friendship Field shows patterns regarding Extraversion, Resemblance and Status including the mere-exposure effect and friendship by similarity. For the implementation of Status, Kemper’s status-power theory is used. The concept of Social Battery is also linked to Kemper’s theory on the organism as reference group. By running the model for a year (3 interactions moments per day), the friendship dynamics over time can be studied.
We presented the model at the Social Simulation Conference 2022.
Flibs’NLogo implements in NetLogo modelling environment, a genetic algorithm whose purpose is evolving a perfect predictor from a pool of digital creatures constituted by finite automata or flibs (finite living blobs) that are the agents of the model. The project is based on the structure described by Alexander K. Dewdney in “Exploring the field of genetic algorithms in a primordial computer sea full of flibs” from the vintage Scientific American column “Computer Recreations”
As Dewdney summarized: “Flibs […] attempt to predict changes in their environment. In the primordial computer soup, during each generation, the best predictor crosses chromosomes with a randomly selected flib. Increasingly accurate predictors evolve until a perfect one emerges. A flib […] has a finite number of states, and for each signal it receives (a 0 or a 1) it sends a signal and enters a new state. The signal sent by a flib during each cycle of operation is its prediction of the next signal to be received from the environment”
The FishCensus model simulates underwater visual census methods, where a diver estimates the abundance of fish. A separate model is used to shape species behaviours and save them to a file that can be shared and used by the counting model.
The model demonstrates how non-instantaneous sampling techniques produce bias by overestimating the number of counted animals, when they move relative to the person counting them.
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