Uncertainty is the environment in which our decision models frequently operate. What drives real decisions under real uncertainty? Adam DeJans Jr. unpacks why most “optimization” efforts fail under real-world uncertainty in the 5-part mini-series:

• The Illusion of Deterministic Optimization
• Why Uncertainty Deserves a First-Class Seat
• Plans to Policies
• Building and Evaluating Policies
• Models & Operational Systems

Optimization under uncertainty needs to be embedded within your business as a living system. Its success is measured not by solver convergence or benchmark accuracy, but by decisions that consistently align operational realities with financial objectives under real-world volatility.

Adam’s recommended infrastructure includes:

• Data ingestion → signal extraction → belief updates → policy execution, creating a continuous flow from raw data to action.
• Feedback loops to measure decision outcomes and improve policies systematically over time.
• Ownership: ensuring teams are accountable for system performance in production, not just offline model metrics or slide-deck KPIs.

The same is true not only for optimization models but for rules-based decision models as well. Here is an example of a feedback loop when Machine Learning is used for business rules adjustments:

Click on the image to see a more extended architecture