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.
The model aims to illustrate how Earned Value Management (EVM) provides an approach to measure a project’s performance by comparing its actual progress against the planned one, allowing it to evaluate trends to formulate forecasts. The instance performs a project execution and calculates the EVM performance indexes according to a Performance Measurement Baseline (PMB), which integrates the description of the work to do (scope), the deadlines for its execution (schedule), and the calculation of its costs and the resources required for its implementation (cost).
Specifically, we are addressing the following questions: How does the risk of execution delay or advance impact cost and schedule performance? How do the players’ number or individual work capacity impact cost and schedule estimations to finish? Regardless of why workers cause delays or produce overruns in their assignments, does EVM assess delivery performance and help make objective decisions?
To consider our model realistic enough for its purpose, we use the following patterns: The model addresses classic problems of Project Management (PM). It plays the typical task board where workers are assigned to complete a task backlog in project performance. Workers could delay or advance in the task execution, and we calculate the performance using the PMI-recommended Earned Value.
This model is designed to show the effects of personality types and student organizations have on ones chance to making friendships in a university setting. As known from psychology studies, those that are extroverted have an easier chance making friendships in comparison to those that are introverted.
Once every tick a pair of students (nodes) will be randomly selected they will then have the chance to either be come friends or not (create an edge or not) based on their personality type (you are able to change what the effect of each personality is) and whether or not they are in the same club (you can change this value) then the model triggers the next tick cycle to begin.
In recent years we have seen multiple incidents with a large number of people injured and killed by one or more armed attackers. Since this type of violence is difficult to predict, detecting threats as early as possible allows to generate early warnings and reduce response time. In this context, any tool to check and compare different action protocols can be a further step in the direction of saving lives. Our proposal combines features from continuous and discrete models to obtain the best of both worlds in order to simulate large and crowded spaces where complex behavior individuals interact. With this proposal we aim to provide a tool for testing different security protocols under several emergency scenarios, where spaces, hazards, and population can be customized. Finally, we use a proof of concept implementation of this model to test specific security protocols under emergency situations for real spaces. Specifically, we test how providing some users of a university college with an app that informs about the type and characteristics of the ongoing hazard, affects in the safety performance.
This model implements a classic scenario used in Reinforcement Learning problem, the “Cliff Walking Problem”. Consider the gridworld shown below (SUTTON; BARTO, 2018). This is a standard undiscounted, episodic task, with start and goal states, and the usual actions causing movement up, down, right, and left. Reward is -1 on all transitions except those into the region marked “The Cliff.” Stepping into this region incurs a reward of -100 and sends the agent instantly back to the start (SUTTON; BARTO, 2018).
The problem is solved in this model using the Q-Learning algorithm. The algorithm is implemented with the support of the NetLogo Q-Learning Extension
This is a re-implementation of a the NetLogo model Maze (ROOP, 2006).
This re-implementation makes use of the Q-Learning NetLogo Extension to implement the Q-Learning, which is done only with NetLogo native code in the original implementation.
Patagonia PSMED is an agent-based model designed to study a simple case of Evolution of Ethnic Differentiation. It replicates how can hunter-gatherer societies evolve and built cultural identities as a consequence of the way they interacted.
This model illustrates actor interaction in the construction sector, according to information gathered in NL. It offers a simple frame to represent diverse interests, interdependencies and effects on the number of built sustainable houses.