EDDY stands for Explainable Dynamic Decision System. It is designed to provide transparent and interpretable decision-making processes in dynamic environments, allowing users to understand the rationale behind decisions made by the system. EDDY also incorporates AI assistant capabilities, enabling it to analyze complex data and provide actionable insights, thereby enhancing decision-making efficiency and effectiveness.

The MIT Orbital Capacity Analysis Tool (MOCAT) is a suite of modeling tools designed to assess and forecast the long-term sustainability of the LEO (Low Earth Orbit) environment. It helps mission designers and regulatory bodies consider the environmental impact of missions.

MOCAT-MC is a tool that allows for detailed simulation of the orbital environment by individually propagating each LEO object and modeling their interactions over time. It provides fine-grained control over the simulation through configurable parameters and allows users to save a wide range of data like satellite information and collision records. It helps in evaluating long-term sustainability and collision risks.

MOCAT-ML is designed to accelerate the prediction of space object density distributions. It uses machine learning to forecast the density distribution of Anthropogenic Space Objects (ASOs) in the orbital phase space. Instead of running computationally expensive full simulations, it can take past data and predict future scenarios.

MOCAT-SSEM takes a coarse-grained modeling approach by representing the entire LEO population using a small number of object categories that interact within discrete altitude shells. Its evolution is governed by differential equations, making it highly efficient. In contrast, MOCAT-MC is a high-fidelity simulation that tracks each object individually.

The Network model for Space Sustainability (NESSY) is designed to study the relationships among different groups of space objects and assess the environmental impact across different orbit regimes. It models the space environment as a dynamic network to understand the global health of space.

NESSY can help in understanding the time evolution of relationships between different types of space objects and derive an understanding of the overall health of the space environment based on the network's properties. It can also be used to predict the effect of launch traffic, collision avoidance maneuvers, and post-mission disposal policies.

The SSSD is a specialized tool for conducting Life Cycle Assessments (LCAs) specifically for the space industry. It helps in assessing the environmental, social, and economic impacts of space activities. Recently, it has been enhanced to integrate considerations of the space environment's health into the overall assessment of space missions, linking Earth-based impacts with orbital sustainability.