decision-canon/INTENT.md

INTENT — decision-canon

Mission

Establish a coherent, extensible Decision Canon that provides:

1. a shared language for thinking and talking about decisions,
2. a unified Decision Object capable of representing decisions across radically different levels of rigor and consequence,
3. a taxonomy of decision kinds, methods, authority modes, and effects,
4. a progressive formalization model that keeps simple decisions simple,
5. a machine-processable foundation for governed human, agentic, and hybrid decision systems,
6. a way to model policies and constitutional decisions as decisions whose effects shape future decision spaces,
7. and a closed-loop learning model connecting decisions to execution, outcomes, retrospectives, and policy evolution.

Problem

Decision-related disciplines are fragmented.

Relevant bodies of work include:

• normative decision theory,
• Bayesian decision analysis,
• causal inference,
• multi-criteria decision analysis,
• operations research,
• robust optimization,
• game theory,
• behavioral decision theory,
• naturalistic decision making,
• complexity-oriented decision making,
• organizational decision rights,
• business rules and DMN,
• workflow systems,
• control theory,
• reinforcement learning,
• policy design,
• and governance.

Each contributes useful abstractions, but no single practical object model spans the whole lifecycle:

situation
→ framing
→ evidence
→ alternatives
→ values
→ analysis
→ authority
→ commitment
→ execution
→ observation
→ learning
→ policy evolution

The canon shall explore that missing connective layer.

Core hypothesis

A Decision Object can serve as a first-class unit of agency.

It should record an authorized commitment made from a particular frame, knowledge state, and value system, together with intended effects and sufficient lineage to observe and learn from what follows.

The object must support progressive formalization:

informal choice
→ recorded decision
→ structured decision
→ analyzed decision
→ governed decision
→ executable decision
→ policy decision
→ constitutional decision

Key design principles

One semantic object, progressive formality

A trivial choice must not require heavyweight structure.

A consequential policy decision must support deep analysis, explicit authority, evidence, machine-readable effects, and retrospective learning.

Explicit incompleteness

Unknown, estimated, assumed, inferred, observed, and decided must remain distinguishable.

Decision quality is not outcome quality

The decision record must preserve what was knowable at decision time so that outcomes do not rewrite history.

Capability and authority are independent

The canon must distinguish:

can
may
should
will
did

Policies are decision effects

A policy decision changes the rules under which future decisions are made.

Constitutional decisions alter decision-making itself

Constitutional decisions may change authority structures, policy creation rules, fundamental constraints, or mechanisms for delegation and escalation.

Decisions form a graph

The canon should support explicit decision lineage such as:

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

Intended uses

The canon should eventually support:

• architecture decision records,
• product and investment decisions,
• security decisions,
• autonomous-agent delegation,
• policy modelling,
• governance systems,
• approval and escalation systems,
• incident decision reconstruction,
• decision retrospectives,
• machine-executable decision services,
• organizational decision intelligence,
• and simulation of ecosystem evolution under different policies.

Non-goals

The canon is not intended to:

• replace every existing decision-analysis method,
• impose quantitative analysis on all decisions,
• force all decisions into one universal workflow,
• collapse values, evidence, uncertainty, and authority into one scalar score,
• or treat policy as merely another business rule.

Long-term ambition

Create a practical and formal foundation for adaptive socio-technical decision systems in which humans, agents, services, and institutions can make, delegate, execute, review, and evolve decisions while preserving authority lineage and epistemic context.