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 10 of 108 results for "Marco Alberti" clear search
An agent-based model for assessing strategies of adaptation to climate and tourism demand changes in an alpine destination
Stefano Balbi Marco Alberti | Published Monday, February 14, 2011 | Last modified Saturday, April 27, 2013The model is then used for assessing three hypothetical and contrasted infrastructure-oriented adaptation strategies for the winter tourism industry, that have been previously discussed with local stakeholders, as possible alternatives to the “business-as-usual” situation.
Peer reviewed Garbage can model Excel reconstruction
Smarzhevskiy Ivan | Published Tuesday, August 19, 2014 | Last modified Tuesday, July 30, 2019Reconstruction of the original code M. Cohen, J. March, and J. Olsen garbage can model, realized by means of Microsoft Office Excel 2010
Population dynamics
Marco Janssen | Published Monday, December 31, 2018Simple population dynamics model used in Introduction to Agent-Based Modeling by Marco Janssen. For more information see https://intro2abm.com/
Diffusion of innovations
Marco Janssen | Published Tuesday, January 14, 20203 simple models to illustrate diffusion of innovations.
The models are discussed in Introduction to Agent-Based Modeling by Marco Janssen. For more information see https://intro2abm.com/
Governing the commons
Marco Janssen | Published Tuesday, January 14, 2020 | Last modified Sunday, July 17, 2022Model on the use of shared renewable resources including impact of imitation via success-bias and altruistic punishment.
The model is discussed in Introduction to Agent-Based Modeling by Marco Janssen. For more information see https://intro2abm.com/
Peer reviewed Artificial Anasazi
Marco Janssen | Published Tuesday, September 07, 2010 | Last modified Saturday, April 27, 2013Replication of the well known Artificial Anasazi model that simulates the population dynamics between 800 and 1350 in the Long House Valley in Arizona.
Consumats on a network
Marco Janssen | Published Tuesday, January 14, 2020 | Last modified Tuesday, May 30, 2023Consumer agents make choices which products to choose using the consumat approach. In this approach agents will make choices using deliberation, repetition, imitation or social comparison dependent on the level of need satisfaction and uncertainty.
The model is discussed in Introduction to Agent-Based Modeling by Marco Janssen. For more information see https://intro2abm.com/
Micro-level Adaptation, Macro-level Selection, and the Dynamics of Market Partitioning
Cesar Garcia-Diaz | Published Monday, October 19, 2015 | Last modified Monday, October 19, 2015This model simulates the emergence of a dual market structure from firm-level interaction. Firms are profit-seeking, and demand is represented by a unimodal distribution of consumers along a set of taste positions.
Rangeland and evolution of management styles
Marco Janssen | Published Tuesday, January 14, 2020Provided is a landscape of properties where pastoralists make decisions how much livestock they put on their property and how much to suppress fire from occuring. Rangelands can be grass dominated, or unproductive shrubb dominated. Overgrazing and fire suppresion lead to shrub dominated landscapes. What management strategies evolve, and how is this impacted by policies?
The model is discussed in Introduction to Agent-Based Modeling by Marco Janssen. For more information see https://intro2abm.com/.
Spatial rangeland model
Marco Janssen | Published Tuesday, January 22, 2019 | Last modified Friday, March 04, 2022Spatial explicit model of a rangeland system, based on Australian conditions, where grass, woody shrubs and fire compete fore resources. Overgrazing can cause the system to flip from a healthy state to an unproductive shrub state. With the model one can explore the consequences of different movement rules of the livestock on the resilience of the system.
The model is discussed in Introduction to Agent-Based Modeling by Marco Janssen. For more information see https://intro2abm.com/.
Displaying 10 of 108 results for "Marco Alberti" clear search