About the CoMSES Model Library more info
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.
Displaying 7 of 7 results multi-agent simulation clear
Multi-agent modeling and analysis of the knowledge learning of a human-machine hybrid intelligent organization with human-machine trust
Haoxiang Zhang | Published Monday, April 24, 2023Machine learning technologies have changed the paradigm of knowledge discovery in organizations and transformed traditional organizational learning to human-machine hybrid intelligent organizational learning. However, it remains unclear how human-machine trust, which is an important factor that influences human-machine knowledge exchange, affects the effectiveness of human-machine hybrid intelligent organizational learning. To explore this issue, we used multi-agent simulation to construct a knowledge learning model of a human-machine hybrid intelligent organization with human-machine trust.
RAGE RAngeland Grazing Model
Gunnar Dressler Jürgen Groeneveld Carsten M Buchmann Cheng Guo Niklas Hase Jule Thober Karin Frank Birgit Müller | Published Monday, July 17, 2017 | Last modified Friday, October 26, 2018RAGE models a stylized common property grazing system. Agents follow a certain behavioral type. The model allows analyzing how household behavior with respect to a social norm on pasture resting affects long-term social-ecological system dynamics.
Auctionsimulation
Deniz Kayar | Published Wednesday, August 12, 2020This repository the multi-agent simulation software for the paper “Comparison of Competing Market Mechanisms with Reinforcement Learning in a CarPooling Scenario”. It’s a mutlithreaded Javaapplication.
TERRoir level Organic matter Interactions and Recycling model
Myriam Grillot | Published Wednesday, April 19, 2017 | Last modified Wednesday, June 17, 2020The TERROIR agent-based model was built for the multi-level analysis of biomass and nutrient flows within agro-sylvo-pastoral villages in West Africa. It explicitly takes into account both human organization and spatial extension of such flows.
OLIGO
Mason Wright | Published Thursday, October 24, 2013 | Last modified Monday, April 08, 2019A multi-agent model of oligarchy in a spatial election simulation.
A Multi-Agent Simulation Approach to Farmland Auction Markets
James Nolan | Published Wednesday, June 22, 2011 | Last modified Saturday, April 27, 2013This model explores the effects of agent interaction, information feedback, and adaptive learning in repeated auctions for farmland. It gathers information for three types of sealed-bid auctions, and one English auction and compares the auctions on the basis of several measures, including efficiency, price information revelation, and ability to handle repeated bidding and agent learning.
A simulation tool for capability-based team task allocation in emergency-responce environments
Afsaneh Fatemi | Published Wednesday, March 16, 2011 | Last modified Saturday, April 27, 2013Its a multi agent simulation environment, provided using JADE/Java. It gets the number of agents and tasks, then divides the physical environment to some segments, and then runs a greedy capability-based coalition formation and task allocation algorithm to assign tasks to groups of agents and complete the tasks.