decision-canon/DecisionCanon.md

Decision Canon v0.1

1. Purpose

The Decision Canon provides a coherent conceptual system for describing decisions, decision processes, and decision-governed ecosystems.

It intentionally separates:

1. Decision situation — why a decision exists.
2. Decision modelling — how the relevant world is represented.
3. Decision analysis — how alternatives are evaluated.
4. Decision taking — how an authorized commitment occurs.
5. Decision execution — what is done because of the commitment.
6. Decision observation — what happens afterwards.
7. Decision learning — what should change in beliefs, models, or rules.
8. Decision governance — who may decide and under which constraints.
9. Decision evolution — how policies and constitutional rules reshape future decisions.

2. Unified lifecycle

Situation
   ↓
Decision need
   ↓
Frame
   ↓
Alternatives / interventions
   ↓
Evaluation
   ↓
Commitment
   ↓
Effect / execution
   ↓
Observation
   ↓
Learning

For policy-bearing decisions:

Decision
   ↓
Policy / Rule / Constraint / Delegation
   ↓
Changes future decision spaces
   ↓
Future decisions
   ↓
Observed ecosystem evolution
   ↓
Policy review
   ↓
Revised decision

3. Decision kinds

Initial decision kinds:

• choice
• bet
• experiment
• commitment
• delegation
• allocation
• authorization
• exception
• policy
• constitutional

These may overlap.

Choice

Select among alternatives.

Bet

Accept unresolved uncertainty for expected benefit.

Experiment

Act primarily to acquire information.

Commitment

Reduce future optionality.

Delegation

Transfer or constrain decision authority.

Allocation

Assign scarce resources.

Authorization

Permit or prohibit an action.

Exception

Override an existing rule within bounded scope.

Policy

Create reusable rules that govern classes of future decisions.

Constitutional

Change the rules by which authority, policy, or governance may themselves change.

4. Decision state

Suggested lifecycle:

noticed
→ framed
→ open
→ recommended
→ committed
→ effective
→ observed
→ reviewed
→ closed

Side states:

• deferred
• delegated
• cancelled
• expired
• superseded
• revoked

The key boundary is committed.

Before commitment, the system is considering possibilities.

After commitment, the system has adopted an intention.

5. Decision formality levels

D0 — Ephemeral

Unrecorded or transient choice.

D1 — Recorded

Captures question, choice, and reason.

D2 — Structured

Adds alternatives, owner, context, and constraints.

D3 — Analyzed

Adds evidence, values, uncertainty, and explicit analysis.

D4 — Governed

Adds authority, approvals, delegation, escalation, and auditability.

D5 — Executable

Adds machine-readable rules, conditions, triggers, and actions.

D6 — Policy

Creates reusable rules that constrain future decisions.

D7 — Constitutional

Changes authority structures, policy-creation rules, or foundational ecosystem constraints.

These are not maturity rankings in the sense that D7 is always better than D1.

They indicate the degree and kind of formalization appropriate to the decision.

6. Decision method families

The canon should allow multiple methods rather than prescribe one.

Informal / human

• intuition
• expert judgment
• heuristic
• checklist
• recognition-primed decision

Comparative

• pairwise comparison
• weighted scoring
• MCDA / MAUT
• AHP
• outranking

Probabilistic

• expected utility
• Bayesian decision analysis
• value of information
• decision trees
• influence diagrams

Optimization

• linear programming
• mixed-integer programming
• constraint programming
• stochastic optimization
• robust optimization

Sequential / adaptive

• dynamic programming
• MDP
• POMDP
• optimal control
• reinforcement learning

Causal

• structural causal models
• intervention analysis
• counterfactual analysis

Strategic

• game theory
• bargaining
• mechanism design
• scenario analysis

Complexity-oriented

• probing
• real options
• safe-to-fail experiments
• robust decision making

Collective

• vote
• consent
• consensus
• deliberation
• committee decision

Machine-assisted

• rule engine
• DMN
• optimization service
• recommendation model
• AI-assisted synthesis
• autonomous policy selection

7. Decision authority

Authority should be represented independently from capability.

Canonical modalities:

can      technically capable
may      authorized
should   normatively preferred
will     committed
did      observed
must     mandatory

This permits distinctions such as:

actor: autonomous-deployment-agent
can: true
may: false

or:

actor: tenant-operator
may: true
scope: tenant

8. Decision criticality

The canon should support a measure of required rigor.

A conceptual function:

RequiredRigor =
    Stakes
  × Irreversibility
  × Uncertainty
  × DurationOfEffect
  × ScopeOfImpact
  × AuthorityDepth

This is not yet intended as a literal equation.

Its purpose is to formalize the principle that more consequential decisions deserve more explicit modelling.

9. Policy effect primitives

Initial policy primitives:

• permit
• prohibit
• require
• constrain
• default
• prefer
• incentivize
• delegate
• escalate
• observe
• review
• expire

These primitives should be sufficient to express a large portion of operational governance.

10. Decision relationships

Canonical graph relationships:

• derived-from
• implements
• governed-by
• delegated-by
• authorized-by
• triggered-by
• depends-on
• challenges
• reviews
• supersedes
• revokes
• exception-to
• resulted-in
• learned-from

11. Decision lineage

Decision lineage is the ability to explain an observed action by traversing its decision and authority history:

Observed action
    ↑
Execution
    ↑
Decision
    ↑
Delegated authority
    ↑
Policy
    ↑
Policy decision
    ↑
Constitutional decision

The inverse traversal should answer:

What downstream decisions and actions did this policy enable, prohibit, or shape?

12. Decision quality

Decision quality and outcome quality must remain distinct.

Ex ante

Was the decision reasonable given:

• frame,
• evidence,
• beliefs,
• alternatives,
• values,
• uncertainty,
• available analysis,
• and authority?

Ex post

What actually happened?

The difference between expectation and outcome produces learning.

13. Canonical deep structure

A useful abstract representation is:

D = (F, K, A, V, M, G, C, E, O, L)

where:

• F — Frame
• K — Knowledge
• A — Alternatives
• V — Values
• M — Method
• G — Governance
• C — Commitment
• E — Effect / Execution
• O — Observation
• L — Learning

A lightweight decision may instantiate only:

(F, A, C)

while a constitutional decision may instantiate the entire structure.

14. Central invariant

A Decision Object records an authorized commitment made from a particular frame, knowledge state, and value system, together with its intended effects and sufficient lineage to observe and learn from what follows.