Decision-Making Under Uncertainty
Blog: For Practitioners by Practitioners!
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