Our mission is to help computational modelers develop, document, and share their computational models in accordance with community standards and good open science and software engineering practices. Model authors can publish their model source code in the Computational Model Library with narrative documentation as well as metadata that supports open science and emerging norms that facilitate software citation, computational reproducibility / frictionless reuse, and interoperability. Model authors can also request private peer review of their computational models. Models that pass peer review receive a DOI once published.
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Please check out our model publishing tutorial and feel free to contact us if you have any questions or concerns about publishing your model(s) in the Computational Model Library.
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This is a simulation model of communication between two groups of managers in the course of project implementation. The “world” of the model is a space of interaction between project participants, each of which belongs either to a group of work performers or to a group of customers. Information about the progress of the project is publicly available and represents the deviation Earned value (EV) from the planned project value (cost baseline).
The key elements of the model are 1) persons belonging to a group of customers or performers, 2) agents that are communication acts. The life cycle of persons is equal to the time of the simulation experiment, the life cycle of the communication act is 3 periods of model time (for the convenience of visualizing behavior during the experiment). The communication act occurs at a specific point in the model space, the coordinates of which are realized as random variables. During the experiment, persons randomly move in the model space. The communication act involves persons belonging to a group of customers and a group of performers, remote from the place of the communication act at a distance not exceeding the value of the communication radius (MaxCommRadius), while at least one representative from each of the groups must participate in the communication act. If none are found, the communication act is not carried out. The number of potential communication acts per unit of model time is a parameter of the model (CommPerTick).
The managerial sense of the feedback is the stimulating effect of the positive value of the accumulated communication complexity (positive background of the project implementation) on the productivity of the performers. Provided there is favorable communication (“trust”, “mutual understanding”) between the customer and the contractor, it is more likely that project operations will be performed with less lag behind the plan or ahead of it.
The behavior of agents in the world of the model (change of coordinates, visualization of agents’ belonging to a specific communicative act at a given time, etc.) is not informative. Content data are obtained in the form of time series of accumulated communicative complexity, the deviation of the earned value from the planned value, average indicators characterizing communication - the total number of communicative acts and the average number of their participants, etc. These data are displayed on graphs during the simulation experiment.
The control elements of the model allow seven independent values to be varied, which, even with a minimum number of varied values (three: minimum, maximum, optimum), gives 3^7 = 2187 different variants of initial conditions. In this case, the statistical processing of the results requires repeated calculation of the model indicators for each grid node. Thus, the set of varied parameters and the range of their variation is determined by the logic of a particular study and represents a significant narrowing of the full set of initial conditions for which the model allows simulation experiments.
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The model examines the dynamics of herd growth in African pastoral systems. We used it to examine the role of scale (herd size) stochasticity (in mortality, fertility, and offtake) on herd growth.
An empirically validated agent-based model of circular migration
An integrated wildfire and urban growth model for informal settlements.
Fire-WCISG is developed to test the impact of different fire management strategies on the number and type of residents staying in an informal settlement, and the fire-dependent fynbos vegetation that it is next to.
The model uses a hybrid protection motivation theory, place attachment and Turner’s informal residents as described by Christ et al. (2023) (10.1016/j.habitatint.2023.102815), for the agents decision making. Wildland vegetation is based on published understanding of fynbos ecology.
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Logônia is a NetLogo model that simulates the growth response of a fictional plant, Logônia, under different climatic conditions. The model uses climate data from WorldClim 2.1 (Fick & Hijmans, 2017) and demonstrates how to integrate the LogoClim model through the LevelSpace extension.
The model was developed according to the FAIR principles for research software (Barker et al., 2022) and is openly available on the CoMSES Network and GitHub.
This is a multi-patch meta-population ecological model. It intended as a test-bed in which to test the impact of humans with different kinds of social structure.
This model is to match students and schools using real-world student admission mechanisms. The mechanisms in this model are serial dictatorship, deferred acceptance, the Boston mechanism, Chinese Parallel, and the Taipei mechanism.
Agent-Based-Modeling - space colonization
ask me for the .nlogo model
WHAT IS IT?
The goal of this project is to simulate with NetLogo (v6.2) a space colonization of humans, starting from Earth, into the Milky Way.
HOW IT WORKS
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The Carington model is designed to provide insights into the factors affecting informal health care for older adults. It encompasses older adults, caregivers, and factors affecting informal health care. The Carington model includes no submodels.
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.
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