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ResearchProgram.md

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+# 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:
+
+```text
+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?