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.
Displaying 10 of 31 results foraging clear search
Modeling the emergence of migratory corridors and foraging hotspots of the green sea turtle
Volker Grimm Stephanie Kramer-Schadt Jérôme Bourjea Mayeul Dalleau | Published Friday, April 05, 2019 | Last modified Tuesday, September 17, 2019The model represents migration of the green sea turtle, Chelonia mydas, between foraging and breeding sites in the Southwest Indian Ocean. The purpose of the model is to investigate the impact of local environmental conditions, including the quality of foraging sites and ocean currents, on emerging migratory corridors and reproductive output and to thereby identify conservation priority sites.
Corresponding article to found here: https://onlinelibrary.wiley.com/doi/epdf/10.1002/ece3.5552
RaMDry - Rangeland Model in Drylands
Pascal Fust Eva Schlecht | Published Friday, January 05, 2018 | Last modified Friday, April 01, 2022RaMDry allows to study the dynamic use of forage ressources by herbivores in semi-arid savanna with an emphasis on effects of change of climate and management. Seasonal dynamics affects the amount and the nutritional values of the available forage.
Environmental knowledge inhibits hominin dispersal
Colin Wren | Published Thursday, November 09, 2017Local scale mobility, namely foraging, leads to global population dispersal. Agents acquire information about their environment in two ways, one individual and one social. See also http://www.openabm.org/model/3846/
The Opportunistic Acquisition Model of Stone Tool Raw Material Procurement
Marco Janssen Simen Oestmo Haley Cawthra | Published Friday, April 21, 2017 | Last modified Sunday, March 10, 2019The 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.
Peer reviewed The Effect of Spatial Clustering on Stone Raw Material Procurement
Marco Janssen Simen Oestmo Curtis W Marean | Published Friday, April 21, 2017This 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).
Resource distribution effects on optimal foraging theory
Marco Janssen Kim Hill | Published Friday, January 27, 2017The original Ache model is used to explore different distributions of resources on the landscape and it’s effect on optimal strategies of the camps on hunting and camp movement.
An agent-based approach to weighted decision making in the spatially and temporally variable South African Paleoscape
Colin Wren | Published Thursday, December 29, 2016This model simulates a foraging system based on Middle Stone Age plant and shellfish foraging in South Africa.
Central-place forager mobility and cultural diversity
Luke Premo | Published Wednesday, May 18, 2016This spatially explicit agent-based model addresses how effective foraging radius (r_e) affects the effective size–and thus the equilibrium cultural diversity–of a structured population composed of central-place foraging groups.
Hedonic and Eudaimonic Well-being Based Reward for Intrinsic Motivated Reinforcement Learning Agents
Yue Gao Shimon Edelman | Published Monday, March 21, 2016The code contains four experiments for well-being based IMRL reward features.
How to not get stuck – an ant model showing how negative feedback due to crowding maintains flexibility in ant foraging
Tomer Czaczkes | Published Thursday, December 17, 2015Positive feedback can lead to “trapping” in local optima. Adding a simple negative feedback effect, based on ant behaviour, prevents this trapping
Displaying 10 of 31 results foraging clear search