The University of Arizona and Biosphere 2 have agreed to support a research project, hosted on-site at Biosphere 2, Oracle, Arizona, in order to monitor and record light, temperature, relative humidity, carbon dioxide, and biomass. The goal? —capture the non-linear functions inherent in plant growth and provide a ground-truth data set for the SIMOC agent-based model.

The abstract reads as follows …

Mathematical models of complex systems can provide baseline assumptions about the real-world. While Environmental Control and Life Support System (ECLSS) can be modeled as linear, static, and deterministic, deployed systems do not often behave as modeled for the full duration of a mission. Models of bioregenerative systems are considerably more complex and readily identified as probabilistic. Non-linear models are typically built upon differential equations and/or computer software applications designed specifically for simulation of particular real-world systems.

An agent-based model (ABM) employs the actions and interactions of individual and collective, autonomous agents such that their behavior, when allowed to unfold over a specified time, may exhibit non-linear, dynamic, and probabilistic behavior. Used extensively in finance, biology, ecology, and social sciences ABMs are a proven alternative to more traditional systems of modeling.

SIMOC (a scalable, interactive model of an off-world community) is a Python-based ABM developed as an Interplanetary Initiative pilot project at Arizona State University. In collaboration with the Biosphere 2, SIMOC is employed to model a semi-closed BLSS built upon the NASA funded Prototype Lunar Greenhouse.

SIMOC’s web-based agent library editor enables rapid design of new agents to match real-world systems. The configuration wizard and interactive dashboard provides a graphical interface with ABM readouts and a full command-line, back-end data capture for analytical and machine learning post processing.

This publication sees the results of the first application of this novel approach to modeling a real-world
BLSS in which continuous temperature, relative humidity, luminosity, and carbon dioxide data are
collected for the full duration of the experiment and then compared to the output of the ABM that has
generated the same four parameters for the same duration.