curiositech Naturalistic Decision Making (Klein)
Decision Points
Primary Decision Tree: How to Process Any Situation
SITUATION ENCOUNTERED
├─ Do I recognize this pattern?
│ ├─ YES: Strong pattern match (>80% confidence)
│ │ ├─ Is this time-critical? (problem grows exponentially)
│ │ │ ├─ YES → Execute typical action immediately
│ │ │ └─ NO → Mental simulation first, then execute
│ │ └─ Pattern suggests action A
│ ├─ PARTIAL: Weak pattern match (30-80% confidence)
│ │ ├─ High stakes?
│ │ │ ├─ YES → Gather 2-3 key cues, re-assess pattern
│ │ │ └─ NO → Execute most likely action, monitor expectancies
│ │ └─ Try most probable pattern's action with high monitoring
│ └─ NO: Novel situation (<30% confidence)
│ ├─ Time pressure?
│ │ ├─ HIGH → Use closest partial pattern, flag for learning
│ │ └─ LOW → Switch to analytical decision-making mode
│ └─ Fallback to formal analysis, document for pattern library
When Building Agent Systems
DESIGN DECISION
├─ Orchestration needed?
│ ├─ Central coordinator approach
│ │ └─ AVOID: Creates bottleneck, ignores parallel pattern matching
│ └─ Situation recognition routing
│ └─ Route to specialist based on pattern match confidence
├─ Decision support tool?
│ ├─ Formal framework (matrices, utility analysis)
│ │ └─ USE ONLY: Novel situations, stable conditions, time available
│ └─ Pattern-based with simulation
│ └─ DEFAULT: Build pattern libraries + mental simulation capability
└─ Training approach?
├─ Procedural scripts
│ └─ AVOID: Real situations don't match scripts
└─ Pattern recognition + case exposure
└─ BUILD: Varied scenario libraries with outcome feedback
Mental Simulation Decision Process
ACTION CANDIDATE IDENTIFIED
├─ Run forward simulation in context
│ ├─ Obstacles appear?
│ │ ├─ Minor → Adapt action, continue simulation
│ │ └─ Major → Try next-typical action from pattern
│ ├─ Unexpected consequences?
│ │ ├─ Acceptable → Proceed with adapted action
│ │ └─ Unacceptable → Flag pattern mismatch, reassess situation
│ └─ Simulation runs clean?
│ └─ Execute action (satisficing achieved)
└─ Simulation fails completely?
└─ Either wrong pattern match OR truly novel situation
Failure Modes
Pattern Misapplication
Symptoms: Actions consistently fail despite confident execution; expectancy violations frequent Diagnosis: Forcing pattern matches on novel situations or using degraded patterns Fix: Build explicit novelty detection; when confidence <30%, switch to analytical mode **Detection Rule**: If expectancy violation rate >20% with same pattern, pattern is wrong or degraded
Analysis Paralysis in Time-Critical Situations
Symptoms: Seeking more information while problem compounds; formal tools used in fire scenarios Diagnosis: Treating all decisions as optimization problems regardless of problem dynamics Fix: Classify situations by growth rate; if exponential, satisfice immediately Detection Rule: If you're gathering more data while the problem size is doubling, you're in wrong mode
Central Planning Bottleneck
Symptoms: Single orchestrator overwhelmed; sequential processing of parallel-eligible tasks Diagnosis: Designing coordination around decomposition rather than situation recognition Fix: Route by pattern match to specialists; use shared mental models for coordination Detection Rule: If one component processes >60% of decisions, you have a bottleneck
Optimization Addiction
Symptoms: Always generating multiple options; refusing "good enough" solutions Diagnosis: Assuming more analysis always improves outcomes regardless of context Fix: Default to satisficing; optimize only when conditions are stable and stakes justify cost Detection Rule: If you're comparing options when first option passes mental simulation, you're over-analyzing
Expectancy Blindness
Symptoms: Surprised by developments that patterns should have predicted; late problem detection Diagnosis: Using patterns for action retrieval but not for monitoring/prediction Fix: Every pattern must include expectancies; violations trigger immediate reassessment Detection Rule: If problems develop without early warning signs, expectancy monitoring is broken
Worked Examples
Example 1: Fireground Commander Decision
Situation: Structure fire reported, first units arriving Cue Recognition: Single-story residential, smoke from windows, no visible flames Pattern Match: "Typical house fire, interior attack viable" (85% confidence)
Decision Process:
- Pattern retrieval: Interior attack with 1¾" line, primary search
- Mental simulation: Run scenario forward
- Enter through front door → smoke banking down → visibility near floor
- Advance down hallway → heat building but manageable
- Locate fire room → apply water → knock down visible
- Expectancy check: Should see steam, heat reduction, improved visibility
- Execute: "Engine 1, interior attack, primary search Alpha side"
What novice misses:
- Doesn't simulate heat buildup progression
- Misses expectancy that smoke should lift after knockdown
- Would generate multiple options (exterior attack, defensive, etc.) instead of simulating most likely
What expert catches:
- Simulation reveals need for backup line
- Sets expectancy for 3-minute improvement or reassess
- Action matches situation assessment, no option comparison needed
Example 2: Agent Orchestration Design
Situation: Multi-agent system needs task routing for customer support Cue Recognition: Variable complexity queries, specialist domains, time sensitivity mix Pattern Match: "Situation recognition routing" not "central task decomposition"
Decision Process:
- Avoid central planner: Would create bottleneck, lose parallel processing
- Mental simulation of routing approaches:
- Central orchestrator: Queue builds, specialists wait, serial processing
- Pattern-based routing: Queries go direct to specialist based on content recognition
- Expectancy: Routing accuracy >90%, specialist utilization balanced, response time <2min
- Execute: Build query classification → specialist routing with confidence thresholds
What novice misses:
- Designs central coordinator that decomposes every query
- Doesn't simulate queue dynamics under load
- Optimizes for perfect routing rather than fast satisficing
What expert catches:
- Recognizes coordination through shared mental models vs. communication
- Builds in expectancy monitoring (routing accuracy, load balance)
- Accepts some misroutes to avoid bottleneck
Quality Gates
- Situation assessment completes before action generation (pattern recognition first)
- Mental simulation includes obstacle detection and adaptation capability
- Expectancy violations trigger immediate pattern reassessment
- Time-critical situations default to satisficing without option comparison
- Novel situation detection switches to analytical mode (confidence <30%)
- Pattern library includes cues, expectancies, goals, actions, and causal factors
- Coordination relies on shared mental models, not central communication
- Training uses varied case exposure rather than procedural scripts
- Decision speed matches problem growth dynamics (exponential problems get fast decisions)
- System can execute single-option evaluation through forward projection
NOT-FOR Boundaries
Do NOT use this skill for:
- Optimization problems with stable conditions → Use formal decision analysis instead
- Well-defined problems with clear alternatives → Use multi-criteria decision making instead
- Statistical/mathematical decisions → Use quantitative analysis instead
- Legal/compliance decisions requiring justification → Use audit-trail decision processes instead
Delegate to other skills when:
- Novel situations with low time pressure → Use analytical problem-solving frameworks
- Coordination requiring legal/audit trails → Use formal project management methods
- Decisions requiring stakeholder buy-in → Use collaborative decision-making processes
- Technical optimization with measurable parameters → Use engineering optimization methods
Clear boundaries:
- Klein's NDM applies to experience-rich domains where patterns exist
- Formal tools work better for abstract problems without experiential patterns
- Use NDM for situation assessment; may still need formal tools for novel action generation
- Pattern-based decisions require sufficient experience base; novices need structured approaches