Influence Diagrams for Optimal Decision Making in Early Science Space Mission Concept Development

Concept design maturation is an initial phase in the engineering design process that revolves around decision making. In the context of science space mission concept development, decision trees play a crucial role. The branches in decision trees are interdependent, meaning that the probability of future outcomes depends on past decisions. However, there are limited resources available for exploring these different trade space options, such as time, money, and workforce. Therefore, the main challenge is to efficiently conduct this trade space exploration and generate a wide range of mission ideas and alternatives from which new programs/projects can be selected. This is the purpose of the NASA Pre-Phase A project life cycle phase.To better understand the concept of decision trees, it's important to clarify certain terms. A decision tree is a specific type of graph, which is a collection of objects where some objects have relationships with each other. In a directed graph, these objects are called vertices and are connected by directed edges or arcs. A directed acyclic graph is a directed graph that contains no cycles, meaning there are no loops formed when following the directions of the edges.One approach to reaching optimal decisions is by employing an influence diagram. An influence diagram is a concise graphical and mathematical representation of a decision scenario. By utilizing the structure of a directed acyclic graph, influence diagrams provide a visual and intuitive way to represent the relationships between variables, decisions, and outcomes, facilitating analysis and decision-making under uncertainty. Influence diagrams are particularly useful for modeling decision problems that involve uncertainty and complex dependencies. They can represent decision problems involving multiple decision-makers, allowing for a more comprehensive analysis of interactions and dependencies between different decision-makers. Influence diagrams can also handle decision problems with multiple objectives, enabling decision-makers to consider trade-offs between different criteria and incorporate subjective preferences. Influence diagrams extend the concept of a Bayesian network and allow for modeling and solving both probabilistic inference problems and decision-making problems based on the maximum expected utility criterion.This paper will explore the development of influence diagrams and their application in science space mission concept development at the Jet Propulsion Laboratory.