Stephanie Introne
(Advisor: Prof. Dimitri Mavris)

will propose a doctoral thesis entitled,

A Methodology for Forecasting and Mitigating Risk in Cislunar Mission Planning Using Real Options

On

Friday, October 20 at 8:00 a.m.
Collaborative Visualization Environment (CoVE)
Weber Space Science and Technology Building (SST III)

And

Click here to join the meeting

Abstract

Space mission planning is made extremely difficult by the nature of space itself: it is a hostile environment for human exploration, requiring large budgets and advanced technologies. Space exploration is also a dangerous endeavor; crewed missions have consistently exceeded the risk guidelines set by NASA, and the most stringent safety requirements are set on the launch and landing phases, which are also the phases where an accident is statistically most likely to occur. The focus of future exploration is on creating a foundation on the Moon for the eventual launch of crewed Mars missions, so the operating environment is becoming even more challenging, and with mission budgets tied to public perception and assumed risk, there is an increased need for methodology improvements to reduce programmatic and personal risk.

One opportunity for improved risk management and reduction for space mission planning is through the application of real options analysis. Real options analysis borrows concepts from economics and financial markets to model how the value of a project changes over time and captures deep uncertainty, which mirrors the level of uncertainty in mission planning. The outcome of real options analysis is a decision tree that presents a distribution of possible outcomes depending on the path taken to an end state. A major advantage of real options is the built-in flexibility: there are opportunities at decision nodes to abandon a project, increase investment, or change the structure.

The major limitation of applying this type of analysis is that the scope of the problem is so large that the analyst is quickly presented with a decision tree of millions or billions of nodes, which is nearly intractable to consider or modify. There are also additional complexities associated with how to determine whether a possible outcome is acceptable or appropriate, and what level of granularity is needed to fully analyze possible failure points. The first part of this research will demonstrate a methodology for dimensionality reduction of the decision tree to enable decision-making informed by real options. The second part of this research will formulate the problem into a tabletop exercise, similar to how a military wargaming scenario would be performed, because meaningful results must be informed by the scenario and stakeholder perspectives. This tabletop exercise will also allow for consensus building among several analysts and work towards the derivation of an objective function that cannot be otherwise derived. The expected benefit of applying the proposed methodology to space problems is increased efficiency in determining an appropriate mission, where the solution considers several relevant metrics and is informed by information related to the current scenario and stakeholder interests.

Committee

• Prof. Dimitri Mavris – School of Aerospace Engineering (advisor)
• Dr. Mark Whorton – Georgia Tech Research Institute/School of Aerospace Engineering
• Prof. Glenn Lightsey – School of Aerospace Engineering
• Prof. Mariel Borowitz – Sam Nunn School of International Affairs
• Dr. Michael Balchanos – School of Aerospace Engineering