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 aims to examine how different levels of communication noise and superiority bias affect team performance when solving problems collectively. We used a networked agent-based model of collective problem solving in which agents explore the NK landscape for a better solution and communicate with each other regarding their current solutions. We compared the team performance in solving problems collectively at different levels of self-superiority bias when facing simple and complex problems. Additionally, we addressed the effect of different levels of communication noise on the team’s outcome
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.
MOOvPOPsurveillance was developed as a tool for wildlife agencies to guide collection and analysis of disease surveillance data that relies on non-probabilistic methods like harvest-based sampling.
The Agent-Based Wildfire Simulation Environment (ABWiSE) translates the concept of a moving fire front as a set of mobile fire agents that respond to, and interact with, vegetation, wind, and terrain. Presently, the purpose of ABWiSE is to explore how ABM, using simple interactions between agents and a simple atmospheric feedback model, can simulate emergent fire spread patterns.
This is an empirical model described in http://dx.doi.org/10.1016/j.landurbplan.2010.05.001. The objective of the model is to simulate how the decision-making of farmers/agents with different strategies can affect the landscape structure in a region in the Netherlands.
This model (CharRec) creates simulated charcoal records, based on differing natural and anthropogenic patterns of ignitions, charcoal dispersion, and deposition.
Ecosystems are among the most complex structures studied. They comprise elements that seem both stable and contingent. The stability of these systems depends on interactions among their evolutionary history, including the accidents of organisms moving through the landscape and microhabitats of the earth, and the biotic and abiotic conditions in which they occur. When ecosystems are stable, how is that achieved? Here we look at ecosystem stability through a computer simulation model that suggests that it may depend on what constrains the system and how those constraints are structured. Specifically, if the constraints found in an ecological community form a closed loop, that allows particular kinds of feedback may give structure to the ecosystem processes for a period of time. In this simulation model, we look at how evolutionary forces act in such a way these closed constraint loops may form. This may explain some kinds of ecosystem stability. This work will also be valuable to ecological theorists in understanding general ideas of stability in such systems.
The model is designed to analyse the effects of mitigation measures on the European brown hare (Lepus europaeus), which is directly affected by ongoing land use change and has experienced widespread decline throughout Europe since the 1960s. As an input, we use two 4×4 km large model landscapes, which were generated by a landscape generator based on real field sizes and crop proportions and differed in average field size and crop composition. The crops grown annually are evaluated in terms of forage suitability, breeding suitability and crop richness for the hare. Six mitigation scenarios are implemented, defined by a 10 % increase in: (1) mixed silphie, (2) miscanthus, (3) grass-clover ley, (4) alfalfa, (5) set-aside, and (6) general crop richness. The model shows that that both landscape configuration and composition have a significant effect on hare population development, which responds particularly strongly to compositional changes.
Agers and non-agers agent compete over a spatial landscape. When two agents occupy the same grid, who will survive is decided by a random draw where chances of survival are proportional to fitness. Agents have offspring each time step who are born at a distance b from the parent agent and the offpring inherits their genetic fitness plus a random term. Genetic fitness decreases with time, representing environmental change but effective non-inheritable fitness can increase as animals learn and get bigger.
A modelling system to simulate Neanderthal demography and distribution in a reconstructed Western Europe for the late Middle Paleolithic.
Displaying 10 of 83 results landscape clear