This is a simple model replicating Hardin’s Tragedy of the Commons using reactive agents that have psychological behavioral and social preferences.

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 was developed to study the combination of electric vehicles (EVs) and intermitten renewable energy sources. The model presents an EV fleet in a fictional area, divided into a residential area, an office area and commercial area. The area has renewable energy sources: wind and PV solar panels. The agents can be encouraged to charge their electric vehicles at times of renewable energy surplus by introducing different policy interventions. Other interesting variables in the model are the installed renewable energy sources, EV fleet composition and available charging infrastructure. Where possible, use emperical data as input for our model. We expand upon previous models by incorporating environmental self-identity and range anxiety as agent variables.

This is an implementation of an agent based model for the evolution of ethnocentrism. While based off a model published by Hammond and Axelrod (2006), the code has been modified to allow for a more fine-grained analysis of evolutionary dynamics.

AgentEx aims to advance understanding of group processes for sustainable management of a common pool resource (CPR). By supporting the development and test explanations of cooperation and sustainable exploitation.

The model generates disaggregated traffic flows of pedestrians, simulating their daily mobility behaviour represented as probabilistic rules. Various parameters of physical infrastructure and travel behaviour can be altered and tested. This allows predicting potential shifts in traffic dynamics in a simulated setting. Moreover, assumptions in decision-making processes are general for mid-sized cities and can be applied to similar areas.

Together with the model files, there is the ODD protocol with the detailed description of model’s structure. Check the associated publication for results and evaluation of the model.

Installation
Download GAMA-platform (GAMA1.8.2 with JDK version) from https://gama-platform.github.io/. The platform requires a minimum of 4 GB of RAM.
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The Nice Musical Chairs (NMC) model represent the competition for space between groups of stakeholders of farming and herding activities in the arid Afro-Eurasia.

Aroused public opinion has led to public debates on social responsibility issues in food supply chains. This model based op opinion dynamics and the linkages between involved actors simulates the public debate leading to the transitions.

This Agent-Based model intends to explore the conditions for the emergence and change of land use patterns in Central Asian oases and similar contexts.

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.