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.
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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 for the diffusion of innovations with multiple characteristics and price-premiums
The aim of the model is to define when researcher’s assumptions of dependence or independence of cases in multiple case study research affect the results — hence, the understanding of these cases.
In this agent-based model, agents decide to adopt a new product according to a utility function that depends on two kinds of social influences. First, there is a local influence exerted on an agent by her closest neighbors that have already adopted, and also by herself if she feels the product suits her personal needs. Second, there is a global influence which leads agents to adopt when they become aware of emerging trends happening in the system. For this, we endow agents with a reflexive capacity that allows them to recognize a trend, even if they can not perceive a significant change in their neighborhood.
Results reveal the appearance of slowdown periods along the adoption rate curve, in contrast with the classic stylized bell-shaped behavior. Results also show that network structure plays an important role in the effect of reflexivity: while some structures (e.g., scale-free networks) may amplify it, others (e.g., small-world structure) weaken such an effect.
3 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/
Studies on word-of-mouth identify two behaviors leading to transmission of information between individuals: proactive transmission of information, and information seeking. Individuals who are aware might be curious of it and start seeking for information; they might find around them the expertise held by another individual. Field studies indicate individuals do not adopt an innovation if they don’t hold the corresponding expertise. This model describes this information seeking behavior, and enables the exploration of the dynamics which emerges out of it.
This model simulate product diffusion on different social network structures.
This model is a market game for evaluating the effectiveness of the UK government’s 2008-2010 policy on promoting smart metering in the UK retail electricity market. We break down the policy into four
This models simulates innovation diffusion curves and it tests the effects of the degree and the direction of social influences. This model replicates, extends and departs from classical percolation models.
This is model that simulates how multiple kinds of peer effects shape the diffusion of innovations through different types of social relationships.
A model of attitudinal dynamics based on the cognitive mechanism of emotional coherence. The code is written in Java. For initialization an additional dataset is required.