decision-canon/ResearchProgram.md

Research Program — decision-canon

1. Research objective

Develop and validate a unified Decision Canon that connects decision theory, practical decision-making methods, governance, executable decision systems, and adaptive policy evolution.

The research should answer:

Can a single progressively formalizable Decision Object represent the important semantics of decisions from simple human choices to constitutional rules governing autonomous ecosystems?

2. Research streams

A. Decision theory foundations

Study and synthesize:

• expected utility theory,
• Bayesian decision theory,
• value of information,
• causal decision theory,
• bounded rationality,
• prospect theory,
• ecological rationality.

Goal:

Identify the smallest set of primitives needed to represent rational, bounded, uncertain, and causally informed decisions.

B. Decision analysis and modelling

Study:

• decision trees,
• influence diagrams,
• MAUT / MCDA,
• AHP,
• ELECTRE / PROMETHEE,
• scenario analysis,
• robust decision making,
• real options.

Goal:

Map analytical methods onto modular Decision Object extensions.

C. Optimization and sequential decisions

Study:

• linear and integer optimization,
• constraint programming,
• stochastic optimization,
• robust optimization,
• MDPs,
• POMDPs,
• control theory,
• reinforcement learning.

Goal:

Determine how generated actions and learned policies fit the same object model as discrete human choices.

D. Strategic and multi-actor decisions

Study:

• game theory,
• bargaining,
• mechanism design,
• social choice,
• multi-agent systems.

Goal:

Represent decisions made in environments containing other adaptive decision makers.

E. Human and naturalistic decision making

Study:

• heuristics,
• expert judgment,
• recognition-primed decisions,
• cognitive biases,
• time-pressured decisions,
• group decision making.

Goal:

Avoid over-formalizing real-world human decisions while preserving enough structure for learning.

F. Organizational governance

Study:

• decision rights,
• delegation,
• RACI / RAPID / DACI,
• four-eyes principles,
• escalation models,
• board and committee governance,
• regulatory accountability.

Goal:

Formalize authority separately from capability and preference.

G. Executable decision systems

Study:

• DMN,
• BPMN integration,
• rule engines,
• policy engines,
• workflow systems,
• access-control policy languages.

Goal:

Create mappings between Decision Objects and executable operational systems.

H. Policy and constitutional modelling

Study:

• public policy design,
• legal rules,
• constitutions,
• institutional economics,
• governance theory,
• Ostrom-style rule systems,
• adaptive policy making.

Goal:

Define policy decisions as decisions that alter future decision spaces, and constitutional decisions as decisions that alter how such rules may change.

I. Decision lineage and observability

Study analogies to:

• data lineage,
• provenance,
• event sourcing,
• audit logs,
• distributed tracing,
• causal graphs.

Goal:

Enable traversal from observed action back to the policy and authority chain that enabled it.

J. AI and autonomous decision systems

Study:

• AI-assisted decision making,
• human-in-the-loop systems,
• decision-focused learning,
• causal reinforcement learning,
• autonomous agents,
• machine delegation,
• AI governance.

Goal:

Define safe and efficient mechanisms for dynamically allocating decision authority between humans and machines.

3. Initial hypotheses

H1 — Progressive formalization works

A shared semantic object can span informal and formal decisions if optional analytical and governance envelopes are used.

H2 — Commitment is the central boundary

The most important semantic transition is not analysis completion but authorized commitment.

H3 — Policy is a decision effect

Policies can be modelled naturally as effects that constrain or enable future decisions.

H4 — Constitutional recursion is representable

Decisions can modify the rules that govern creation and modification of future policies and decision rights.

H5 — Decision lineage is operationally useful

Organizations and agentic ecosystems benefit from being able to trace actions to decisions, delegations, and governing policies.

H6 — Decision quality must remain separate from outcome quality

Preserving decision-time epistemic state improves learning and accountability.

4. Prototype milestones

M0 — Concept seed

Deliver:

• intent,
• initial canon,
• Decision Object,
• schema draft,
• glossary,
• research history.

M1 — Comparative corpus

Represent at least 25 decisions from different domains:

• trivial engineering choice,
• architecture decision,
• hiring decision,
• procurement decision,
• incident-response decision,
• investment decision,
• security exception,
• deployment policy,
• budget allocation,
• public-style policy,
• constitutional governance rule.

Evaluate where the schema becomes awkward.

M2 — Kernel freeze candidate

Identify the minimal mandatory kernel and normative lifecycle.

M3 — Extension model

Separate optional modules for:

• probabilistic analysis,
• causal analysis,
• MCDA,
• optimization,
• governance,
• policy,
• execution,
• learning.

M4 — Decision graph

Implement explicit typed relationships and decision lineage.

M5 — Executable mapping

Prototype conversion or linkage to:

• DMN,
• workflow systems,
• policy engines,
• approval systems.

M6 — Autonomous delegation

Model decision authority for human/agent hybrids, including escalation and bounded autonomy.

M7 — Policy evolution simulation

Use Decision Objects to model an ecosystem where policies influence later decisions and are themselves revised based on observed outcomes.

5. Evaluation criteria

The canon should be judged against:

Expressiveness

Can it model radically different decisions without distortion?

Simplicity

Can simple decisions remain simple?

Composability

Can decisions depend on, authorize, or revise other decisions?

Temporal integrity

Can the system preserve what was known at decision time?

Governance fidelity

Can authority, delegation, approval, and escalation be represented explicitly?

Executability

Can sufficiently formal decisions drive software systems?

Learnability

Can outcomes and retrospectives update later models without corrupting the original record?

Explainability

Can an observed action be traced back through its decision and authority lineage?

6. Research artifacts

Suggested repository evolution:

decision-canon/
├── INTENT.md
├── DecisionCanon.md
├── UnifiedDecisionObject.md
├── DecisionObjectSchemaDraft.md
├── Glossary.md
├── ResearchProgram.md
├── concepts/
├── catalog/
├── compendium/
├── schemas/
├── examples/
├── experiments/
├── fixtures/
├── reviews/
└── history/

7. Immediate next questions

1. What is the irreducible Decision Object kernel?
2. Is commitment always necessary for something to count as a decision?
3. Should policies themselves be Decision Objects, effects, or both?
4. How should competing world models be represented?
5. How should authority provenance be modelled?
6. How should recursive policy changes avoid infinite conceptual nesting?
7. Which existing standards should be reused rather than reinvented?
8. How should uncertainty and confidence be represented without fake precision?
9. How should decisions be versioned and superseded?
10. What should a minimal executable decision format look like?