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.
An agent-based model of echo chamber formation employing a Bayesian Source Credibility cognitive architecture limiting interactions to a single cascade.
This is an opinion dynamics model which extends the model found in (Martins 2009). The previous model had an unshared uncertainty assumption in agent-to-agent interaction this model relaxes that assumption. The model only supports a fully connect network where every agent has an equal likelihood of interacting with every other agent at any given time step. The model is highly modular so different social network paradigm can easier be implemented.
Micro-targeted vs stochastic political campaigning agent-based model simulation. Written by Toby D. Pilditch (University of Oxford, University College London), in collaboration with Jens K. Madsen (University of Oxford, London School of Economics)
The purpose of the model is to explore the various impacts on voting intention among a population sample, when both stochastic (traditional) and Micto-targeted campaigns (MTCs) are in play. There are several stages of the model: initialization (setup), campaigning (active running protocols) and vote-casting (end of simulation). The campaigning stage consists of update cycles in which “voters” are targeted and “persuaded” - updating their beliefs in the campaign candidate / policies.
This model implements a Bayesian belief revision model that contrasts an ideal agent in possesion of true likelihoods, an agent using a fixed estimate of trusting its source of information, and an agent updating its trust estimate.
We expose RA agent-based model of the opinion and tolerance dynamics in artificial societies. The formal mathematical model is based on the ideas of Social Influence, Social Judgment, and Social Identity theories.
This adaptation of the Relative Agreement model of opinion dynamics (Deffuant et al. 2002) extends the Meadows and Cliff (2012) implementation of this model in a manner that explores the effect of the network structure among the agents.
This model extends the bounded confidence model of Deffuant and Weisbuch. It introduces online contexts in which a person can deliver his or her opinion to several other persons. There are 2 additional parameters accessibility and connectivity.
We compare the effect of four activation regimes by measuring the appropriate opinion clustering statistics and also the number of emergent extremists.
We investigate the interplay of homophily, differentiation, and in-group cooperation mechanisms on the formation of opinion clusters and emergence of radical opinions.