CoMSES Net maintains cyberinfrastructure to foster FAIR data principles for access to and (re)use of computational models. Model authors can publish their model code in the Computational Model Library with documentation, metadata, and data dependencies and support these FAIR data principles as well as best practices for software citation. Model authors can also request that their model code be peer reviewed to receive a DOI. All users of models published in the library must cite model authors when they use and benefit from their code.
CoMSES Net also maintains a curated database of over 7500 publications of agent-based and individual based models with additional metadata on availability of code and bibliometric information on the landscape of ABM/IBM publications that we welcome you to explore.
This model is programmed in Python 3.6. We model how different consensus protocols and trade network topologies affect the performance of a blockchain system. The model consists of multiple trader and miner agents (Trader.py and Tx.py), and one system agent (System.py). We investigated three consensus protocols, namely proof-of-work (PoW), proof-of-stake (PoS), and delegated proof-of-stake (DPoS). We also examined three common trade network topologies: random, small-world, and scale-free. To reproduce our results, you may need to create some databases using, e.g., MySQL; or read and write some CSV files as model configurations.
The purpose of this model is explore how “friend-of-friend” link recommendations, which are commonly used on social networking sites, impact online social network structure. Specifically, this model generates online social networks, by connecting individuals based upon varying proportions of a) connections from the real world and b) link recommendations. Links formed by recommendation mimic mutual connection, or friend-of-friend algorithms. Generated networks can then be analyzed, by the included scripts, to assess the influence that different proportions of link recommendations have on network properties, specifically: clustering, modularity, path length, eccentricity, diameter, and degree distribution.
Contains python3 code to replicate the opinion dynamics model from our (so far unpublished) JASSS sumbission “A Balance Model of Opinion Hyperpolarization”. The main function is run_model(), which returns a dictionary object containing various outcome metrics.
FNNR-ABM is an agent-based model that simulates human activity, Guizhou snub-nosed monkey movement, and GTGP-enrolled land parcel conversion in the Fanjingshan National Nature Reserve in Guizhou, China.
1. Install Python and set environmental path variables.
2. Install the mesa, matplotlib (optional), and pyshp (optional) Python libraries.
3. Configure fnnr_config_file.py.
We present an agent-based model for the sharing economy, in the short-time accommodations market, where peers participating as suppliers and demanders follow simple decision rules about sharing market participation, according to their heterogeneous characteristics. We consider the sharing economy mainly as a peer-to-peer market where the access is preferred to ownership, excluding professional agents using sharing platforms as Airbnb to promote their business.
This is code repository for the paper “Homophily as a process generating social networks: insights from Social Distance Attachment model”.
It provides all information, code and data necessary to replicate all the simulations and analyses presented in the paper.
This document contains the overall instruction as well as description of the content of the repository.
Details regarding particular stages are documented within source files as comments.
This is a series of simulations of binary group decisions and the outcomes applied to a generalized version of Price’s Equation for system fitness.
Agent-Based Computational Model of the cryptocurrency Bitcoin with a realistic market and transaction system. Bitcoin’s transaction limit (i.e. block size) and Bitcoin generation can be calibrated and optimized for wealth and network’s hashing power by the Non-Dominated Sorted Genetic Algorithm - II.