Governance¶
Forecasting systems do not operate in isolation. They inform decisions that affect operations, cost, service levels, and risk. As a result, forecasting systems require governance—not only over models and data, but over how evaluation results are interpreted and how decisions are made.
In Electric Barometer, governance refers to the explicit structures, policies, and controls that ensure forecasting-driven decisions are transparent, reproducible, and aligned with organizational intent.
Why governance is necessary¶
In many forecasting workflows, decisions emerge implicitly:
- Metrics are chosen without documenting why.
- Models are selected because they score highest on a leaderboard.
- Tradeoffs are resolved informally or inconsistently.
- Decisions cannot be easily explained after the fact.
These patterns create fragility. When outcomes are questioned, it becomes difficult to reconstruct what assumptions were made, which alternatives were considered, and why a particular choice prevailed.
Governance exists to prevent this ambiguity by making decision logic explicit and reviewable.
Governance is not bureaucracy¶
Governance is often misunderstood as an administrative burden. In practice, it serves a different purpose.
Effective governance:
- Clarifies responsibility and intent
- Reduces reliance on implicit judgment
- Improves consistency across time and teams
- Enables faster iteration by removing ambiguity
Rather than slowing decision-making, well-designed governance accelerates it by establishing clear rules and expectations.
The scope of governance in forecasting systems¶
In the context of Electric Barometer, governance applies to several layers:
- Evaluation criteria — what is measured and why
- Cost assumptions — how different outcomes are valued
- Decision policies — how evaluation results are acted upon
- Tie-breaking rules — how ambiguity is resolved
- Change control — how updates to metrics or policies are introduced
Governance does not prescribe a single “correct” choice at any of these layers. Instead, it ensures that choices are deliberate, documented, and reproducible.
Explicit policy as a governance mechanism¶
At the heart of governance is policy.
Policy defines how evaluation outputs are translated into decisions. It encodes preferences, priorities, and risk posture that cannot be inferred from data alone.
Examples of policy decisions include:
- Preferring conservative forecasts under high uncertainty
- Penalizing certain types of error more heavily than others
- Favoring stability over marginal performance gains
- Requiring additional evidence before adopting a new model
By treating these as explicit policy rather than implicit behavior, Electric Barometer enables governance without embedding assumptions deep in code or metrics.
Governance and reproducibility¶
A governed system must be reproducible.
Reproducibility means that:
- The same inputs and evaluation criteria yield the same measurements
- The same policies applied to those measurements yield the same decisions
- Decisions can be traced back to both data and policy
Without reproducibility, governance collapses into anecdote. Electric Barometer emphasizes reproducible artifacts and structured outputs as a foundation for governed decision-making.
Auditing and explainability¶
Governance enables explanation after the fact.
When decisions are questioned—by operators, stakeholders, or leadership—a governed system can answer:
- What alternatives were evaluated?
- What criteria were applied?
- What tradeoffs were considered?
- What policy rules influenced the outcome?
This capability is essential in operational environments where decisions carry financial, reputational, or safety implications.
Governance across time¶
Forecasting systems evolve. Data changes, priorities shift, and constraints emerge.
Governance ensures that:
- Changes to metrics or policies are intentional
- Historical decisions remain interpretable under the rules that existed at the time
- Policy evolution does not silently invalidate past evaluations
By separating evaluation logic from decision policy, Electric Barometer allows governance to evolve without destabilizing the system.
How Electric Barometer supports governance¶
Electric Barometer is designed to make governance practical rather than aspirational.
The framework:
- Separates evaluation from decisioning
- Encourages explicit policy definition
- Produces structured, inspectable evaluation outputs
- Supports reproducible selection and tuning workflows
- Treats governance as a system property, not an external process
Governance is not an optional overlay. It is a core requirement for any forecasting system intended to support real-world operational decisions.
Electric Barometer is built with that requirement in mind.