| license | Apache-2.0 |
| name | ndm-decision-models |
| description | Models of naturalistic decision-making including recognition-primed decisions, sensemaking, and mental simulation |
| category | Cognitive Science & Decision Making |
| tags | ["ndm","decision-models","recognition-primed","expertise","heuristics"] |
Naturalistic Decision Making Models for Agent Systems
Decision Points
Primary Branch: Time Pressure + Domain Familiarity → Decision Mode
Agent faces decision task
├── High time pressure (< 5 seconds to decide)
│ ├── Domain is familiar/trained → Use RPD mode
│ │ ├── Clear situation pattern? → Generate action, simulate, execute
│ │ └── Unclear pattern? → Generate best guess action, short simulation, act
│ └── Domain is novel/untrained → Use constrained analytical mode
│ ├── Can identify 2-3 viable options quickly? → Compare those only
│ └── Cannot quickly identify options? → Escalate to human/expert
└── Low time pressure (> 30 seconds to decide)
├── High stakes + reversible decision → Use RPD with extended simulation
├── High stakes + irreversible decision → Use analytical mode with expert review
└── Low stakes → Use RPD mode regardless of domain familiarity
Secondary Branch: Action Validation During Execution
Agent generates action via RPD
├── Mental simulation passes cleanly → Execute immediately
├── Mental simulation shows minor issues → Modify action, simulate again
├── Mental simulation shows major failure → Generate different action
└── Cannot simulate (insufficient domain model) → Revert to analytical mode
Tertiary Branch: Multi-Agent Coordination
Multiple agents must coordinate
├── Agents have shared situation model → Proceed with individual RPD
├── Agents disagree on situation assessment → Stop, build shared model first
└── Situation model unclear → Designate lead agent for situation assessment
Failure Modes
1. Analysis Paralysis in Pattern-Recognizable Situations
Detection Rule: Agent spends >10 seconds comparing obvious alternatives when first option would work.
Symptoms: Over-enumeration of options, probability calculations for clear cases, delayed response to time-critical situations.
Fix: Check if situation matches trained patterns. If yes, force RPD mode; generate first workable action and execute after brief simulation.
2. Recognition Bias in Novel Domains
Detection Rule: Agent confidently executes actions in unfamiliar domains without simulation or verification.
Symptoms: Fast decisions in areas outside training data, no uncertainty signaling when domain shifts, pattern matching to superficially similar but structurally different situations.
Fix: Add domain boundary detection. When domain novelty detected, require analytical mode or human consultation.
3. Simulation Bypass Under Pressure
Detection Rule: Agent executes first generated action without mental simulation when time pressure increases.
Symptoms: Higher error rates under time pressure, no modification of initially generated actions, inability to catch obvious flaws in plan.
Fix: Implement minimum simulation requirement even under extreme time pressure. Better to act 2 seconds later with simulation than immediately without.
4. Situation Model Lock-in
Detection Rule: Agent maintains initial situation assessment despite contradictory evidence emerging during execution.
Symptoms: Continued execution of failing plan, ignoring feedback that invalidates situation model, escalating commitment to wrong diagnosis.
Fix: Build expectancy violation monitoring. Force situation reassessment when 2+ predictions fail to materialize.
5. Decision Support Tool Override
Detection Rule: Agent ignores or works around decision support tools that require analytical processing.
Symptoms: Consistent bypass of formal decision frameworks, resistance to using probability estimation tools, degraded performance when tools are mandatory.
Fix: Redesign tools to support situation assessment and pattern recognition rather than option comparison.
Worked Examples
Example 1: Emergency Response Agent - RPD Success
Scenario: Building fire alarm triggers emergency response agent. Sensors show: smoke detector C-wing, temperature spike, no water flow alerts, 14:30 weekday.
Agent's RPD Process:
- Recognition: Pattern matches to "office fire, business hours, sprinkler system intact"
- Action Generation: Evacuate C-wing, dispatch fire crew, prepare building-wide evacuation
- Mental Simulation: C-wing evacuation takes ~3 minutes, fire crew arrival ~7 minutes, if fire spreads beyond C-wing need full evacuation. Simulation passes.
- Execution: Issues C-wing evacuation, dispatches crew
What Novice Would Miss: Would spend time calculating probability fire spreads, comparing evacuation vs. wait-and-see options, analyzing sensor readings. By recognition, expert agent knows: office fire + working sprinklers = evacuate zone first, full building only if escalation.
Outcome: Fire contained to origin room. Total evacuation time: 4 minutes. Analytical approach would have taken 8-12 minutes just for decision.
Example 2: Trading Agent - Recognition Failure
Scenario: Market volatility spike during Asian session. Agent sees pattern similar to "flash crash" from training data: rapid 2% drop in 5 minutes, high volume, news feed shows "regulatory concern."
Agent's Flawed RPD:
- Recognition: Matches to "flash crash recovery" pattern
- Action Generation: Buy the dip, expecting rapid rebound
- Mental Simulation: Predicts 1-2% recovery within 30 minutes
- Execution: Takes large long position
What Expert Would Catch: "Regulatory concern" during Asian session is structurally different from technical flash crashes. Domain shift not recognized. Should have triggered analytical mode or expert consultation.
Outcome: Further 3% drop as regulatory news proves substantial. Loss: $2.3M.
Fix Applied: Added domain boundary detection for "regulatory news" keyword that forces analytical mode regardless of price pattern recognition.
Example 3: Multi-Agent Coordination - Situation Model Disagreement
Scenario: Software deployment agents preparing production release. Agent A sees "standard deployment" pattern, Agent B sees "high-risk deployment" pattern from same signals: 47 code changes, 3 database migrations, 2 new external dependencies, Friday 4PM release window.
Decision Process:
- Detect Disagreement: Agents generate different actions (A: proceed normally, B: delay to Monday)
- Stop Individual RPD: Both agents halt action generation
- Build Shared Model: Agent A weights "only 47 changes, tested migrations"; Agent B weights "Friday release + external deps"
- Resolution: Shared assessment: "Standard scope but risky timing"
- Coordinated Action: Proceed with deployment but extend monitoring window and prepare rapid rollback
What Would Fail: If agents proceeded with individual RPD, would get coordination failure. Agent A deploys while Agent B holds back monitoring resources.
Quality Gates
Agent deployment readiness checklist:
NOT-FOR Boundaries
This skill should NOT be used for:
- Novel problem domains: Use analytical-reasoning instead for completely unfamiliar situations
- Single high-stakes irreversible decisions: Use formal-decision-analysis for "bet the company" choices
- Mathematical optimization problems: Use optimization-algorithms for resource allocation, scheduling, routing
- Regulatory compliance decisions: Use rule-based-systems for legal/compliance requirements where process traceability is mandatory
- Creative/generative tasks: Use design-thinking or creative-problem-solving for open-ended innovation
- Research and discovery: Use scientific-method for hypothesis testing and knowledge creation
Delegate instead:
- For mathematical problems with clear optimization criteria → optimization-algorithms
- For creative ideation → design-thinking
- For regulatory compliance → rule-based-systems
- For novel domains with no training data → analytical-reasoning
- For high-stakes irreversible decisions → formal-decision-analysis
