This model aims to simulate Competition and Displacement of Online Interpersonal Communication Platforms process from a bottom-up angle. Individual interpersonal communication platform adoption and abandonment serve as the micro-foundation of the simulation model. The evolution mode of platform user online communication network determines how present platform users adjust their communication relationships as well as how new users join that network. This evolution mode together with innovations proposed by individual interpersonal communication platforms would also have impacts on the platform competition and displacement process and result by influencing individual platform adoption and abandonment behaviors. Three scenes were designed to simulate some common competition situations occurred in the past and current time, that two homogeneous interpersonal communication platforms competed with each other when this kind of platforms first came into the public eye, that a late entrant platform with a major innovation competed with the leading incumbent platform during the following days, as well as that both the leading incumbent and the late entrant continued to propose many small innovations to compete in recent days, respectively.
Initial parameters are as follows: n(Nmax in the paper), denotes the final node number of the online communication network node. mi (m in the paper), denotes the initial degree of those initial network nodes and new added nodes. pc(Pc in the paper), denotes the proportion of links to be removed and added in each epoch. pst(Pv in the paper), denotes the proportion of nodes with a viscosity to some platforms. comeintime(Ti in the paper), denotes the epoch when Platform 2 joins the market. pit(Pi in the paper), denotes the proportion of nodes adopting Platform 2 immediately at epoch comeintime(Ti). ct(Ct in the paper), denotes the Innovation Effective Period length. In Scene 2, There is only one major platform proposed by Platform 2, and ct describes that length. However, in Scene 3, Platform 2 and 1 will propose innovations alternately. And so, we set ct=10000 in simulation program, and every jtt epochs, we alter the innovation proposer from one platform to the other. Hence in this scene, jtt actually denotes the Innovation Effective Period length instead of ct.

MOOvPOPsurveillance was developed as a tool for wildlife agencies to guide collection and analysis of disease surveillance data that relies on non-probabilistic methods like harvest-based sampling.

MicroAnts 2.5 is a general-purpose agent-based model designed as a flexible workhorse for simulating ecological and evolutionary dynamics in artificial populations, as well as, potentially, the emergence of political institutions and economic regimes. It builds on and extends Stephen Wright’s original MicroAnts 2.0 by introducing configurable predators, inequality tracking, and other options.
Ant agents are of two tyes/casts and controlled by 16-bit chromosomes encoding traits such as vision, movement, mating thresholds, sensing, and combat strength. Predators (anteaters) operate in static, random, or targeted predatory modes. Ants reproduce, mutate, cooperate, fight, and die based on their traits and interactions. Environmental pressures (poison and predators) and social dynamics (sharing, mating, combat) drive emergent behavior across red and black ant populations.
The model supports insertion of custom agents at runtime, configurable mutation/inversion rates, and exports detailed statistics, including inequality metrics (e.g., Gini coefficients), trait frequencies, predator kills, and lineage data. Intended for rapid testing and educational experimentation, MicroAnts 2.5 serves as a modular base for more complex ecological and social simulations.

The Episim framework builds upon the established transportation simulation MATSim and is capable of tracking agents’ movements within a network and thus computing infection chains. Several characteristics of the virus and the environment can be parametred, whilst the infection dynamics is computed based upon a compartment model. The spread of the virus can be mitigated by restricting the agents’ activity in certain places.

Local 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/

In an associated paper which focuses on analyzing the structure of several egocentric networks of collective awareness platforms for sustainable innovation (CAPS), this model is developed. It answers the question whether the network structure is determinative for the sustainability of the created awareness. Based on a thorough literature review a model is developed to explain and operationalize the concept of sustainability of a social network in terms of importance, effectiveness and robustness. By developing this agent-based model, the expected outcomes after the dissolution of the CAPS are predicted and compared with the results of a network with the same participants but with different ties. Twitter data from different CAPS is collected and used to feed the simulation. The results show that the structure of the network is of key importance for its sustainability. With this knowledge and the ability to simulate the results after network changes have taken place, CAPS can assess the sustainability of their legacy and actively steer towards a longer lasting potential for social innovation. The retrieved knowledge urges organizations like the European Commission to adopt a more blended approach focusing not only on solving societal issues but on building a community to sustain the initiated development.

Industrial clustering patterns are the result of an entrepreneurial process where spinoffs inherit the ideas and attributes of their parent firms. This computational model maps these patterns using abstract methodologies.

Agent based approach to the class of the Integrated Assessment Models. An agent-based model (ABM) that focuses on the energy sector and climate relevant facts in a detailed way while being complemented with consumer goods, labour and capital markets to a minimal necessary extent.

Models land-use, perception, and biocultural interactions between two forager populations.

Model of shifting cultivation. All parameters can be controlled by the user or the model can be run in adaptive mode, in which agents innovate and select parameters.