curiositech Naturalistic Decision Making for High-Stakes Domains
DECISION POINTS
Primary Decision Tree: Recognize-and-Act vs Deliberate-and-Compare
Is the situation pattern-matchable to prior experience?
├── YES: Generate one plausible action
│ ├── Mental simulation passes → ACT immediately
│ └── Simulation fails → Modify action OR generate next best candidate
└── NO: Is this genuine novelty or pattern library failure?
├── Genuine novelty → Deliberate carefully, document for future patterns
└── Pattern gap → Flag training need, proceed with explicit uncertainty
Time Pressure Assessment
Time available vs. cost of error:
├── High time pressure + Low error cost → Act on current best read
├── High time pressure + High error cost → Quick situation assessment, then act
├── Low time pressure + Any error cost → Full situation assessment required
└── Conflicting cues present → ALWAYS invest in situation clarification first
Expertise Level Calibration
Agent expertise level assessment:
├── Novice/Advanced Beginner → Strict rule following, no improvisation
├── Competent → Rules apply, exceptions require explicit justification (DANGER ZONE)
└── Proficient/Expert → Pattern recognition reliable, intuition is valid data
Post-Decision Failure Diagnosis
Decision failed - where was the breakdown?
├── Wrong situation read → Failure in SITUATION ASSESSMENT
│ └── Review cue interpretation, not action selection
├── Right read, wrong action → Failure in ACTION SELECTION
│ └── Review option generation or mental simulation
├── Acted before understanding → PREMATURE CLOSURE
│ └── Novice/Competent behavior pattern
└── Analyzed past action window → DECISION PARALYSIS
└── Missing satisficing rules
FAILURE MODES
1. Tunnel Vision Syndrome
Symptom: Agent fixates on initial interpretation despite contradicting evidence Root Cause: Premature closure - commitment to first plausible story without verification Detection Rule: If agent ignores 3+ cues that contradict primary hypothesis, tunnel vision active Fix: Force hypothesis generation of 2+ alternative explanations before action
2. Option Enumeration Paralysis
Symptom: Agent generates exhaustive option lists under time pressure Root Cause: Novice pattern mistaken for rigor - treating optimization as always superior to satisficing Detection Rule: If agent spends >30% of available time on option generation vs. situation assessment, paralysis mode active Fix: Implement explicit satisficing thresholds and stop-search rules
3. Competent Stage Overconfidence
Symptom: Agent breaks rules without situational justification Root Cause: Enough experience to see exceptions, insufficient models to know when rule-breaking is safe Detection Rule: If agent deviates from protocols but cannot articulate situation-specific reasoning, overconfidence active Fix: Require explicit situational model before any rule deviation
4. Action-First Assessment
Symptom: Agent jumps to response selection before understanding what's happening Root Cause: Confusing speed of action with speed of assessment Detection Rule: If agent selects response before completing "what/why/what-next" situation model, action-first active Fix: Mandatory situation assessment checkpoint before action consideration
5. Expertise Misattribution
Symptom: System fails because expert self-reports were taken at face value Root Cause: Assuming experts can accurately describe their own decision processes Detection Rule: If knowledge base built from direct interviews without structured elicitation, misattribution likely Fix: Use CDM (Critical Decision Method) structured retrospective interviews
WORKED EXAMPLES
Example 1: Ambush Scenario - Expert vs Novice Decision Trees
Setup: Officer approaches vehicle during traffic stop. Driver's hands suddenly drop below window line.
Novice Decision Tree:
- Notices hand movement (single cue)
- Recalls training rule: "Hands not visible = potential threat"
- Draws weapon immediately
- Miss: No assessment of totality - time of day, passenger behavior, driver's verbal responses
- Outcome: Escalation of non-threatening situation
Expert Decision Tree:
- Notices hand movement (primary cue)
- Situation Assessment Phase:
- Passenger behavior (calm vs. agitated)
- Driver verbal responses (compliant vs. evasive)
- Environmental context (lighting, escape routes, backup proximity)
- Vehicle contents visible
- Pattern Recognition: "Nervous compliance" vs. "Pre-attack indicators"
- Mental Simulation: "If I draw now, what happens next?"
- Action: Verbal command + tactical positioning (vs. immediate weapon draw)
- Continuous Assessment: Ready to escalate but hasn't committed to single interpretation
Key Difference: Expert invests cognitive resources in situation model before action selection. Novice applies rule to single cue.
Example 2: Hostage Situation - Satisficing vs. Optimization
Setup: Armed subject holding hostage, demands negotiation. SWAT has clear shot opportunity.
Optimization Approach (Failure Mode):
- Enumerate all possible interventions
- Analyze probability matrices for each
- Seek additional intelligence to improve analysis
- Time Cost: 45+ minutes of analysis
- Outcome: Subject escalates during delay, situation deteriorates
Expert Satisficing Approach:
- Quick Assessment: Subject behavior pattern (calm/agitated), hostage condition, containment status
- First Viable Option: Negotiation attempt with sniper backup positioned
- Stop-Search Rule: "If negotiation maintains status quo for 10 minutes and subject shows compliance indicators, continue. If escalation indicators appear, transition to tactical solution"
- Mental Simulation: "This approach handles the 80% probability scenarios adequately"
- Time Cost: 5 minutes to implementation
- Outcome: Faster stabilization, preserved options
Trade-off Recognition: Expert accepts "good enough" solution quickly rather than pursuing optimal solution slowly. The risk of delay exceeded the benefit of analysis perfection.
QUALITY GATES
Situation assessment is complete when:
- Primary story can be articulated: "What is happening here?"
- Key cues supporting story have been identified (minimum 3)
- Alternative explanations considered: "What else could this be?"
- Prediction generated: "What happens next if story is correct?"
- Contradicting evidence actively sought: "What would prove me wrong?"
- Time/stakes calibration: "How much certainty does this situation require?"
- Expert intuition acknowledged: "Does something feel off?"
Action selection is complete when:
- Response matches situation assessment (not generic protocol)
- Mental simulation of consequences performed
- Fallback options identified if primary response fails
Decision implementation is complete when:
- Monitoring plan active for disconfirming cues
- Escalation/de-escalation triggers defined
- Success/failure criteria specified
NOT-FOR Boundaries
Do NOT use this skill for:
- Routine procedural decisions → Use standard operating procedures instead
- Technical/engineering problems → Use systematic analysis frameworks instead
- Novel situations with unlimited time → Use deliberate problem-solving methods instead
- Decisions where failure cost is catastrophic → Use formal risk analysis instead
When to delegate:
- For pure optimization problems → Use operations research methods
- For complex system design → Use systems engineering approaches
- For policy development → Use structured policy analysis frameworks
- For training design → Use instructional design methodologies
This skill is specifically for:
- Time-pressured decisions with incomplete information
- Replicating expert judgment under uncertainty
- Diagnosing decision failures in dynamic environments
- Building agent architectures for high-stakes domains