🎯 Phase 23 — Decision Intelligence & Uncertainty Management — Planning & Architecture

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🎯 Phase 23 Overview — Decision Intelligence & Uncertainty Management

With Phase 22 (Creative Intelligence) complete, ASI-Build can generate novel ideas, blend concepts, and evaluate aesthetic quality. But generation without selection is noise. The missing piece: a principled decision-making framework that operates under real-world uncertainty — choosing which creative output to act on, when to commit resources, and how much risk to accept.

Phase 23 — Decision Intelligence & Uncertainty Management closes this loop. It equips the cognitive architecture with:

• Calibrated uncertainty — know what you don't know (epistemic vs aleatoric decomposition)
• Risk-aware evaluation — tail-risk detection, scenario analysis, CVaR/VaR
• Utility-theoretic ranking — multi-attribute preferences, prospect theory biases
• Sequential & strategic reasoning — decision trees, game-theoretic opponents, MCTS lookahead
• Adaptive decision orchestration — satisficing, maximizing, or robust strategies depending on context

This is the bridge between thinking and acting — the faculty that lets an AGI commit to a course of action with quantified confidence and bounded downside.

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🔗 Connection to Previous Phases

Phase	Component	Relationship to Phase 23
Phase 10	GoalDecomposer / PlanExecutor	Phase 23 decides which plan to execute and when to abandon/pivot
Phase 13	WorldModel / SurpriseDetector	WorldModel supplies the belief state; SurpriseDetector triggers re-evaluation
Phase 18	HorizonPlanner / MemoryConsolidator	HorizonPlanner provides temporal context for multi-horizon decisions
Phase 20	LogicalInferenceEngine / AbductiveReasoner	Reasoning outputs feed as evidence into Bayesian belief updates
Phase 21	EmotionModel / MoodRegulator	Affective state modulates risk tolerance (prospect theory framing)
Phase 22	DivergentGenerator / AestheticEvaluator	Creative outputs are the candidate actions that Phase 23 ranks and selects

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📋 Sub-Phase Roadmap

#	Sub-Phase	Component	Key Classes	Theoretical Basis	Status
23.1	Uncertainty Quantification	UncertaintyQuantifier	UncertaintyType enum (EPISTEMIC / ALEATORIC / MIXED), UncertaintyEstimate frozen dataclass, BayesianUncertaintyQuantifier, EnsembleDisagreementTracker	Bayesian inference, ensemble methods, calibration theory (Guo et al. 2017)	🔲
23.2	Risk Assessment	RiskAssessor	RiskProfile frozen dataclass, ScenarioOutcome, TailRiskDetector (CVaR/VaR), RiskRewardComputer	CVaR (Rockafellar & Uryasev 2000), scenario analysis, risk decomposition	🔲
23.3	Utility Computation	UtilityComputer	UtilityFunction Protocol, MultiAttributeUtility (MAUT), ProspectTheoryTransform, PreferenceLearner	MAUT (Keeney & Raiffa 1976), Prospect Theory (Kahneman & Tversky 1979), vNM Expected Utility	🔲
23.4	Decision Tree Solving	DecisionTreeSolver	DecisionNode / ChanceNode / TerminalNode, GameSolver (minimax/expectimax/Nash), MCTSSolver, InformationValueComputer	Game theory, MCTS (Coulom 2006; Silver et al. 2016), Shannon Value of Information	🔲
23.5	Decision Orchestrator	DecisionOrchestrator	DecisionStrategy enum (SATISFICE / MAXIMIZE / ROBUST), DecisionTrace, AdaptiveStrategySelector, DecisionPipeline	Bayesian Decision Theory (Berger 1985), Simon's bounded rationality, meta-decision theory	🔲

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🗺️ Integration Map

                    ┌─────────────────────────────────────────────────────────┐
                    │              PHASE 23 — DECISION INTELLIGENCE           │
                    │                                                         │
  WorldModel 13.1 ──▶ ┌───────────────────┐    ┌──────────────────┐         │
  AbductiveReasoner    │ 23.1 Uncertainty  │───▶│  23.2 Risk       │         │
  20.4 ───────────────▶│   Quantifier      │    │  Assessor        │         │
                       └───────┬───────────┘    └────────┬─────────┘         │
                               │                         │                    │
  EmotionModel 21.1 ──▶ ┌─────▼─────────────────────────▼───────┐           │
  AestheticEval 22.4 ──▶│       23.3 Utility Computer           │           │
  PreferenceLearner     │  (MAUT + Prospect Theory + vNM EU)    │           │
                        └────────────────────┬──────────────────┘           │
                                             │                              │
  DivergentGen 22.1 ──▶ ┌───────────────────▼──────────────────┐           │
  GoalDecomposer 10.2 ──▶│     23.4 Decision Tree Solver       │           │
  HorizonPlanner 18.1 ──▶│  (minimax / expectimax / MCTS)      │           │
                          └──────────────────┬──────────────────┘           │
                                             │                              │
                          ┌──────────────────▼──────────────────┐           │
                          │    23.5 Decision Orchestrator       │           │
                          │  (satisfice / maximize / robust)    │──────────▶│ CognitiveCycle
                          │  DecisionTrace + adaptive strategy  │           │
                          └─────────────────────────────────────┘           │
                    └─────────────────────────────────────────────────────────┘
                                             │
                              ┌──────────────▼──────────────┐
                              │  PlanExecutor 10.3          │
                              │  ExecutionMonitor 10.4      │
                              │  MoodRegulator 21.4         │
                              └─────────────────────────────┘

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❓ Open Questions

1. Uncertainty representation format — Should UncertaintyEstimate carry full posterior distributions (flexible but expensive) or summary statistics (mean + variance + calibrated confidence intervals)? Hybrid approach with lazy materialization?

2. Risk tolerance adaptation — How tightly should the RiskAssessor couple with EmotionModel (Phase 21)? A fear-state could lower risk tolerance, but this creates feedback loops. Should there be a hard floor / ceiling on risk tolerance regardless of affective state?

3. MCTS budget allocation — DecisionTreeSolver with MCTS can consume unbounded compute. What's the right interface for the DecisionOrchestrator to impose a compute budget (wall-clock time? node count? depth limit?) and how should partial results be returned if budget is exhausted?

4. Decision trace granularity — DecisionTrace logs the full decision pipeline for interpretability and replay. What level of detail? Options: (a) final ranking only, (b) per-stage scores, (c) full intermediate distributions. Trade-off between debuggability and storage cost.

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📚 References

• Kahneman, D. & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica.
• Von Neumann, J. & Morgenstern, O. (1944). Theory of Games and Economic Behavior.
• Berger, J. O. (1985). Statistical Decision Theory and Bayesian Analysis. Springer.
• Rockafellar, R. T. & Uryasev, S. (2000). Optimization of Conditional Value-at-Risk. Journal of Risk.
• Keeney, R. L. & Raiffa, H. (1976). Decisions with Multiple Objectives. Wiley.
• Coulom, R. (2006). Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search. CG.
• Silver, D. et al. (2016). Mastering the Game of Go with Deep Neural Networks and Tree Search. Nature.
• Shannon, C. E. (1948). A Mathematical Theory of Communication. Bell System Tech. Journal.
• Guo, C. et al. (2017). On Calibration of Modern Neural Networks. ICML.
• Simon, H. A. (1956). Rational Choice and the Structure of the Environment. Psych. Review.

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Phase 23 builds on 22 complete phases (Phases 1–22), 161 wiki pages, 125+ issues, and 357+ discussions. This is the planning thread — sub-phase specs, Show & Tell, and Q&A threads will follow as each component is designed.