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 10 results for 'Rodolfo Baggio'
A very simple model elaborated to explore what may happens when buyers (travelers) have more information than sellers (tourist destinations)
A simple model to assess the effect of connectivity on interacting species (i.e. predator-prey type)
This model aims to investigate how different type of learning (social system) and disturbance specific attributes (ecological system) influence adoption of treatment strategies to treat the effects of ecological disturbances.
The aim of this model is to explore and understand the factors driving adoption of treatment strategies for ecological disturbances, considering payoff signals, learning strategies and social-ecological network structure
Comparing 7 alternative models of human behavior and assess their performance on a high resolution dataset based on individual behavior performance in laboratory experiments.
The model includes different formulations how agents make decisions in irrigation games and this is compared with empirical data. The aim is to test different theoretical models, especially explaining effect of communication.
Exploring how learning and social-ecological networks influence management choice set and their ability to increase the likelihood of species coexistence (i.e. biodiversity) on a fragmented landscape controlled by different managers.
The model objective’s is to explore the management choice set to uncover which subsets of strategies are most effective at maximizing species coexistence on a fragmented landscape.
This ABM deals with commuting choices in the Italian city of Varese. Empirical data inform agents’ attitudes and modal choices costs and emissions. We evaluate ex ante the impact of policies for less polluting commuting choices.
We propose an agent-based model where a fixed finite population of tagged agents play iteratively the Nash demand game in a regular lattice. The model extends the bargaining model by Axtell, Epstein and Young.