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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A model for simulating the evolution of individual’s preferences, incliding adaptive agents “falsifying” -as public opinions- their own preferences. It was builded to describe, explore, experiment and understand how simple heuristics can modulate global opinion dynamics. So far two mechanisms are implemented: a version of Festiguer’s reduction of cognitive disonance, and a version of Goffman’s impression management. In certain social contexts -minority, social rank presure- some models agents can “fake” its public opinion while keeping internally the oposite preference, but after a number of rounds following this falsifying behaviour pattern, a coherence principle can change the real or internal preferences close to that expressed in public.
This project attempts to model how social media platforms recommend a user followers based on their interests, and how those individual interests change as a result of the influences from those they follow/are followed by.
We have three types of users on the platform:
Consumers (🔴), who update their interests based on who they’re following.
Creators (⬛), who update their interests based on who’s following them.
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This is a very simple foraging model used to illustrate the features of Netlogo’s Profiler extension.
A discrete-time stochastic model with state-dependent transmission probabilities and multi-agent simulations focusing on possible risks that could materialize in the final phase of the epidemic.
INOvPOP is designed to simulate population dynamics (abundance, sex-age composition and distribution in the landscape) of white-tailed deer (Odocoileus virginianus) for selected Indiana counties. Updated for netLogo 6.4.0
A “Ger” is a yurt style house used by pastoralists in Mongolia. This model simulates seasonal movements, fission/fusion dynamics, social interaction between households and how these relate to climate impacts.
This is a short NetLogo example demonstrating how to initialize 500 agents with 4 correlated parameters each with random values by doing the necessary calculations in the program “R” and retrieving the results.
This model is a market game for evaluating the effectiveness of the UK government’s 2008-2010 policy on promoting smart metering in the UK retail electricity market. We break down the policy into four
What is it?
This model demonstrates a very simple bidding market where buyers try to acquire a desired item at the best price in a competitive environment
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The purpose of this agent-based model is to simulate the behaviors of small farming households in the Amazon estuary region and evaluate their resilience to external shocks with the presence of several government cash transfer programs.
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