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 allows for the investigation of the effect spatial clustering of raw material sources has on the outcome of the neutral model of stone raw material procurement by Brantingham (2003).
The Opportunistic Acquisition Model (OAM) posits that the archaeological lithic raw material frequencies are due to opportunistic encounters with sources while randomly walking in an environment.
We built a model using R,polr package, to assess 55 published case studies from developing countries to determine what factors influence the level of compliance of local communities with protected area regulations.
Genetic algorithms try to solve a computational problem following some principles of organic evolution. This model has educational purposes; it can give us an answer to the simple arithmetic problem on how to find the highest natural number composed by a given number of digits. We approach the task using a genetic algorithm, where the candidate solutions to the problem are represented by agents, that in logo programming environment are usually known as “turtles”.
A stylized scale model to codesign with villagers an agent-based model of bushmeat hunting in the periphery of Korup National Park (Cameroon)
Industrial clustering patterns are the result of an entrepreneurial process where spinoffs inherit the ideas and attributes of their parent firms. This computational model maps these patterns using abstract methodologies.
Due to the large extent of the Harz National Park, an accurate measurement of visitor numbers and their spatiotemporal distribution is not feasible. This model demonstrates the possibility to simulate the streams of visitors with ABM methodology.
Knowledge Based Economy (KBE) is an artificial economy where firms placed in geographical space develop original knowledge, imitate one another and eventually recombine pieces of knowledge. In KBE, consumer value arises from the capability of certain pieces of knowledge to bridge between existing items (e.g., Steve Jobs illustrated the first smartphone explaining that you could make a call with it, but also listen to music and navigate the Internet). Since KBE includes a mechanism for the generation of value, it works without utility functions and does not need to model market exchanges.
This is the electronic companion to the paper “Modelling Electricity Consumption in Office Buildings: An Agent Based Approach”
Agents are linked in a social-network and make decisions on which of 2 types of behavior to adopt. We explore consequences of different information feedback and providing targeted feedback to individuals.
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