Robust Decision Programming extends Decision Programming with robust optimization. We recommend becoming familiar with Decision Programming and its documentation, especially Influence Diagram and Decision Model sections, as we build upon its terminology and concepts in Robust Decision Programming. For an overview of different robust optimization models, we recommend the paper Recent advances in robust optimization [1]. As a general reference for fundamental mathematical concepts and notation such as propositional logic and sets, we recommend the book Logic and Proof [2]. For model building using convex optimization, we recommend MOSEK Modeling Cookbook [3].

We can add robustness to Decision Programming either by making Path Utilities robust against uncertainty in the Consequences on Value Nodes or by making Path Probabilities robust against uncertainty in the Probabilities on Chance Nodes. We focus on the latter approach to provide decision-makers tools to protect against uncertainty when estimating probabilities, which are difficult to know accurately in practice. We refer to the approach as distributional robustness.

Distributional Robustness

Distributional robustness represents models that are robust against a pre-defined amount of uncertainty in the Probabilities. Instead of defining a single probability distribution, distributional robustness defines a set of probability distributions around the given distribution known as the uncertainty set. We can form the uncertainty set by allowing decision-makers to define an upper and lower bound to each probability instead of an individual value and to define an uncertainty radius which limits the possible distributions to ones that are within the given Wasserstein distance from the original probability distribution.

Then, we can approach distributional robustness by optimizing the Best Worst-Case Expected Value over a model with uncertainty sets. We have formulated the best-worst case in two steps. In the first step, the objective is to maximize the minimum expected value over the uncertainty sets. In the second step, we maximize the expected value with Probabilities fixed to the minimizing ones from the first step.

We can define an Uncertainty Set for each probability distribution in a Chance node to form robust chance node. Then, by applying the Best Worst-Case Expected Value objective to a Decision Model with robust chance nodes form the Distributionally Robust Decision Model. The main challenge along with correctness is to create a tractable model. The computational complexity of these models depends on the number of robust chance nodes and how many States each node has. We can denote the set of robust chance nodes as a subset of chance nodes, formally $\hat{C}⊆C.$ Each robust chance node $i∈\hat{C}$ creates a number of path probability variables that is bounded by the factorial of the number states, formally $O(|S_i|!).$ The total number of path probability variables, which are linear variables and constraints, grows to the product of the factorials

\[O\big(∏_{i∈\hat{C}} |S_i|!\big).\]

However, the constant in factorial bound is less than one and in certain special cases, the bound is polynomial, which makes the distributionally robust model tractable when the number of states is reasonably small. Due to the complexity, we restrict our Distributionally Robust Decision Model to only one robust chance node, that is $|\hat{C}|=1.$

Generalizing the model and finding tractable formulations for models with multiple robust chance nodes or a larger number of chance states is left for future research.