Evaluation vs Decisioning¶
Forecasting systems are often evaluated as if evaluation and decision-making were the same problem. In practice, they are distinct stages with different goals, assumptions, and failure modes. Conflating them leads to misleading conclusions about model quality and suboptimal operational outcomes.
Electric Barometer explicitly separates evaluation from decisioning, treating each as a first-class layer in the forecasting lifecycle.
What evaluation is meant to do¶
Evaluation answers the question:
How does a forecasting system behave relative to a defined objective?
In this context, evaluation is concerned with:
- Comparing alternative forecasts or models
- Measuring performance against explicit criteria
- Understanding tradeoffs and sensitivities
- Producing reproducible, inspectable results
Evaluation operates in a controlled setting. Its purpose is to surface information about system behavior, not to decide what action to take.
What decisioning is meant to do¶
Decisioning answers a different question:
Given the available information, what action should be taken?
Decisioning involves:
- Choosing between competing options
- Applying policy, preference, or risk tolerance
- Resolving tradeoffs that cannot be optimized simultaneously
- Committing to an action that has real-world consequences
Unlike evaluation, decisioning is inherently normative. It reflects priorities, constraints, and organizational objectives that extend beyond technical performance.
Why the distinction matters¶
When evaluation and decisioning are treated as interchangeable, several problems arise:
- Evaluation metrics are implicitly treated as decision rules.
- Hidden assumptions about cost and risk are baked into scores.
- Decisions appear objective when they are actually policy-driven.
- It becomes difficult to explain or audit why a particular choice was made.
A model that performs well under an evaluation metric may still be a poor choice once operational constraints or asymmetric risks are considered. Without a clear boundary between evaluation and decisioning, these discrepancies are easy to miss.
Metrics are not decisions¶
A common failure mode in forecasting systems is allowing a single metric to dictate decisions automatically.
Metrics summarize behavior under specific assumptions. They do not encode:
- Organizational risk tolerance
- Context-specific tradeoffs
- Operational constraints
- Downstream consequences beyond the metric definition
Treating a metric as a decision rule collapses complex judgment into a scalar value, often without realizing what has been assumed or excluded.
Evaluation should inform decisions, not replace them.
Decisioning introduces policy¶
Decisioning requires explicit policy. Policy defines how evaluation results are interpreted and acted upon.
Policy may specify, for example:
- How to trade off different types of error
- When stability is preferred over marginal improvement
- How ties or near-ties are resolved
- When conservative versus aggressive choices are favored
These choices are not inherent properties of the data or the model. They reflect intent. Making them explicit allows decisions to be understood, debated, and revised.
The role of uncertainty and tradeoffs¶
Evaluation often produces ambiguous outcomes:
- Multiple models may perform similarly.
- Improvements in one area may degrade performance elsewhere.
- Performance may vary across segments or time periods.
Decisioning exists to resolve these ambiguities. It acknowledges that not all objectives can be optimized simultaneously and that some choices require prioritization rather than optimization.
Separating evaluation from decisioning prevents false precision and encourages deliberate tradeoff management.
How Electric Barometer separates the two¶
Electric Barometer enforces a clear boundary between evaluation and decisioning:
- Evaluation produces structured, reproducible measurements.
- Decisioning applies explicit policies to those measurements.
- Metrics are treated as inputs, not directives.
- Selection and tuning are governed by declared rules rather than implicit assumptions.
This separation allows organizations to change decision policy without rewriting evaluation logic, and to improve evaluation rigor without prematurely committing to actions.
Why this separation improves governance¶
By disentangling evaluation from decisioning, Electric Barometer improves:
- Transparency — decisions can be traced back to both measurements and policy.
- Reproducibility — the same inputs and rules yield the same outcomes.
- Accountability — policy choices are visible and reviewable.
- Adaptability — policies can evolve as priorities change.
Evaluation tells you what happened. Decisioning determines what you do about it.
Electric Barometer treats both as essential, but distinct, components of a responsible forecasting system.