Programmable Human Behaviour Through Evidence-Based Agent Coordination
A comprehensive research and development project exploring how AI agents and intelligent document staging can ethically prime and nudge human behavior toward self-chosen goals.
This research describes powerful behavior influence techniques.
✅ Beneficence: Use ONLY to genuinely benefit users, NOT for manipulation or profit at user expense ✅ Informed Consent: Obtain explicit, informed consent for any behavior change attempts ✅ Autonomy: Respect user autonomy, provide easy opt-out, preserve choice ✅ Transparency: Be honest about intentions and methods when asked ✅ Non-Maleficence: Do no harm, including subtle psychological harm ✅ Reversibility: Allow users to undo any system suggestions or changes
🚫 Commercial manipulation without informed consent 🚫 Exploitation of vulnerabilities or cognitive limitations 🚫 Dark patterns, deception, or hidden persuasion 🚫 Coercion or removal of meaningful choice 🚫 Use on populations unable to consent (children without guardian approval, etc.) 🚫 Any application that prioritizes system/company benefit over user welfare
Misuse of these techniques is unethical and may be illegal in your jurisdiction.
This research is published to:
- Enable defensive awareness (recognize manipulation)
- Establish ethical standards for the field
- Support beneficial applications (education, therapy, personal development)
- Advance scientific understanding
If you observe misuse, please report to: [Contact information TBD]
This work is released under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International with additional ethical use requirements (see LICENSE.md).
Academic/Research Use: Encouraged with proper attribution Therapeutic/Educational Use: Encouraged with informed consent Commercial Use: Requires explicit permission and ethical review
Create a behaviour engineering system that amplifies human agency through:
- Evidence-based priming and nudging techniques
- Multi-agent coordination and consistency
- Intelligent information architecture
- Transparent, ethical influence
- Continuous measurement and adaptation
Core Principle: Technology should enhance human flourishing, not diminish autonomy.
behaviour-lab/
├── README.md # This file
├── docs/
│ └── system-design-comprehensive.md # Complete system architecture
├── research/
│ ├── priming-foundation.md # Priming: theory, mechanisms, evidence
│ ├── nudging-techniques-catalog.md # Complete nudging techniques library
│ ├── human-vs-agent-priming.md # Critical comparison + integration
│ └── narrative-priming-identity.md # AI narratives & identity (philosophical foundations)
├── src/
│ └── priming_analyzer.py # Core analysis tool (Python)
├── experiments/ # Experimental designs (TBD)
├── data/ # Measurement data (TBD)
└── docs/ # Additional documentation
1. Priming Research (priming-foundation.md)
Key Findings:
- ✅ Cognitive priming is REAL: Perceptual, semantic, and repetition priming have robust evidence
- ❌ Social priming is DEAD: Failed replication, only 2 JESP studies in 2024
- ⏱️ Time scales matter: Effects range from milliseconds (perceptual) to months (repetition)
- 🧠 Neural mechanisms: fMRI and EEG studies show representational sharpening, spreading activation
Evidence Tiers:
- HIGH (⭐⭐⭐⭐⭐): Repetition priming (d=0.8, 95% replication rate)
- HIGH (⭐⭐⭐⭐): Semantic priming (d=0.6, 90% replication rate, short-term)
- HIGH (⭐⭐⭐⭐): Perceptual priming (d=0.7, 92% replication rate)
- MODERATE (⭐⭐⭐): Conceptual priming (d=0.4, 70% replication rate)
Critical Insight: Only use priming with neural mechanism support and successful replications.
2. Nudging Techniques (nudging-techniques-catalog.md)
Most Effective Techniques:
| Technique | Evidence | Effect Size | Duration | Use Cases |
|---|---|---|---|---|
| Defaults | ⭐⭐⭐⭐⭐ | Large (0.8) | Long | Opt-in/out decisions |
| Framing | ⭐⭐⭐⭐ | Medium (0.6) | Short | Messaging, risk communication |
| Anchoring | ⭐⭐⭐⭐ | Large (0.7) | Medium | Pricing, negotiations |
| Social Proof | ⭐⭐⭐⭐ | Medium (0.5) | Medium | Behavior normalization |
| Commitment | ⭐⭐⭐⭐ | Medium (0.6) | Long | Goal achievement |
| Simplification | ⭐⭐⭐⭐ | Large (0.7) | Long | Process design |
| Feedback | ⭐⭐⭐⭐ | Medium (0.4) | Ongoing | Habit formation |
Quick Reference Frameworks:
- MINDSPACE: Messenger, Incentives, Norms, Defaults, Salience, Priming, Affect, Commitments, Ego
- EAST: Easy, Attractive, Social, Timely
3. Human vs. Agent Priming (human-vs-agent-priming.md)
Key Discoveries:
- Neurobiological priming with unconscious effects
- Limited working memory (7±2 items)
- Exponential decay functions
- Embodied, emotional, social
- Attention-based "priming" via context
- Vast context windows (4K-200K+ tokens)
- No decay (within context)
- Extremely sensitive to:
- Instruction phrasing (40% accuracy swings)
- Example ordering (>40% differences)
- Format changes (76 accuracy points)
Agent outputs can prime humans through:
- Semantic content (word associations)
- Visual formatting (perceptual priming)
- Conceptual frameworks (mental models)
- Behavioral demonstrations (observational learning)
- Sequential staging (cascade priming)
Critical Asymmetry: Agents are MORE sensitive to surface features than humans, but LESS sensitive to embodied/emotional cues.
┌──────────────────────────────────────────┐
│ HUMAN OPERATOR(S) │
│ Perception → Cognition → Decision │
└─────────┬────────────────────┬───────────┘
│ Priming │ Feedback
↓ ↑
┌─────────────────────────────────────────┐
│ BEHAVIOUR ENGINEERING LAYER │
│ │
│ • Document Staging Engine │
│ • Multi-Agent Orchestrator │
│ • Priming Choreography │
│ • Nudge Execution │
│ • Measurement & Adaptation │
│ • User Model (cognitive profile) │
└─────────┬───────────────────┬───────────┘
│ │
↓ ↑
┌─────────────────────────────────────────┐
│ AGENT ECOSYSTEM │
│ Research │ Planning │ Execution │
│ Review │ Teaching │ Support │
└─────────────────────────────────────────┘
1. User Modeling
- Cognitive style profiling
- Priming susceptibility estimation
- Learning rate tracking
- Behavioral history analysis
2. Document Staging
- Semantic chunking (respects working memory limits)
- Temporal staging (spacing effects)
- Progressive disclosure
- Adaptive complexity
- Multi-modal presentation
3. Multi-Agent Orchestration
- Specialized agent roles (research, planning, execution, review, teaching, support)
- Consistency enforcement across agents
- Collaborative problem-solving
- Behavioral modeling through agent interactions
4. Priming Choreography
- Multi-stage priming sequences
- Cross-modal priming (visual + semantic + behavioral)
- Semantic network activation
- Adaptive intensity control
- Repetition with variation (spacing effect)
5. Nudge Execution
- Evidence-based nudge library
- Context-appropriate selection
- Combination effects
- A/B testing framework
6. Measurement & Adaptation
- Real-time cognitive load estimation
- Behavioral outcome tracking
- Longitudinal persistence measurement
- Predictive modeling
- Continuous adaptation
Run the core priming analyzer to see the knowledge base in action:
cd ~/Documents/GitHub/behaviour-lab
python3 src/priming_analyzer.pyOutput includes:
- Knowledge base summary (4 priming effects)
- Detailed effect profiles with confidence scores
- Paradigm recommendations for specific use cases
- Temporal dynamics analysis (effect strength over time)
Example output:
Direct Repetition Priming (repetition)
Evidence: HIGH (confidence: 0.95)
Effect size: d=0.80
Replications: 95/100
Mechanism: Neuronal Sharpening & Response Learning
RT Advantage: ~50ms
- Spaced repetition for long-term retention
- Progressive disclosure for complex topics
- Multi-agent teaching with consistency
- Behavioral modeling of problem-solving
- Identity priming before behavior priming
- Environmental design guided by agents
- Commitment devices with social accountability
- Feedback loops for reinforcement
- Framing optimization for risk communication
- Default selection for optimal choices
- Anchoring for reference point establishment
- Simplification to reduce cognitive load
- Implementation intentions (if-then planning)
- Salience for important information
- Feedback on progress and patterns
- Agent demonstrations of best practices
- Semantic priming before complex concepts
- Document staging for optimal comprehension
- Cognitive load management during information delivery
- Multi-agent support for different work phases
- Beneficence: System must genuinely benefit user
- Autonomy: User retains final authority
- Transparency: Explainable intentions
- Non-maleficence: Do no harm (including subtle psychological harm)
- Justice: Fair treatment, no exploitation
- Privacy: Minimal data, strong protection
- Reversibility: User can undo any suggestion
- Consent: Informed consent for behavior change attempts
✅ Ethical Evaluation: Every intervention evaluated against principles ✅ Consent Management: Explicit, informed consent with periodic refresh ✅ Continuous Monitoring: Real-time detection of ethical violations ✅ Dark Pattern Detection: Automatically flags manipulative design ✅ User Override: User can reject, query, or reverse any intervention ✅ Audit Trail: Complete log of all interventions and outcomes
🚩 System benefits more than user 🚩 Deceptive framing or hidden manipulation 🚩 Exploitation of vulnerabilities 🚩 Bypasses conscious awareness (unless user-consented research) 🚩 Difficult opt-out or reversal 🚩 Based on discredited science
- Comprehensive research foundation (3 major documents)
- Evidence-based priming taxonomy
- Complete nudging techniques catalog
- Human vs. agent priming analysis
- System architecture design
- Ethical framework specification
- Priming analyzer prototype (Python)
- Implementation roadmap
- Multi-persona research review (NEXT STEP)
- MVP implementation (Phase 1)
- Initial user pilot study
- Measurement validation
- Multi-agent coordination implementation
- Document staging engine
- User modeling system
- Production deployment
- Research publication
python3 -m pip install numpy scipy networkx matplotlib pandas-
Explore the research:
cd ~/Documents/GitHub/behaviour-lab/research cat priming-foundation.md | head -100
-
Run the priming analyzer:
python3 src/priming_analyzer.py
-
Review the system design:
open docs/system-design-comprehensive.md
-
Understand the roadmap:
- Phase 1 (Months 1-2): Foundation
- Phase 2 (Months 3-4): Multi-agent coordination
- Phase 3 (Months 5-6): Advanced features
- Phase 4 (Months 7-12): Deployment & iteration
This project is currently in the research and design phase. Contributions welcome in:
- Research review: Validate findings against latest literature
- Ethical analysis: Strengthen ethical framework
- Implementation: Build core components
- Experimentation: Design and run studies
- Documentation: Improve clarity and accessibility
Next Priority: Multi-Persona Research Review
Before implementing, we need diverse expert perspectives to validate research and identify blind spots.
| Document | Purpose | Length | Status |
|---|---|---|---|
| priming-foundation.md | Priming theory, evidence, mechanisms | 25 pages | ✅ Complete |
| nudging-techniques-catalog.md | All nudging methods with evidence | 30 pages | ✅ Complete |
| human-vs-agent-priming.md | Comparative analysis + integration | 35 pages | ✅ Complete |
| system-design-comprehensive.md | Complete system architecture | 50+ pages | ✅ Complete |
Total Documentation: ~140 pages of research-backed design
- PMC: Nonconscious Social Behavior
- Replicability Index: Priming Research Past its Prime
- Nature: Cognitive Priming and Training
- PMC: Neural Basis of Repetition Priming
- Wikipedia: Nudge Theory
- PMC: Nudging Progress and Future Directions
- The Decision Lab: Choice Architecture
- Behavioral Economics: Nudge
- Lil'Log: Prompt Engineering
- arXiv: LLM Alignment Survey
- OpenAI: Instruction Following
- PMC: Unleashing Prompt Engineering
Near-term (2025-2026):
- Working MVP with single-user effectiveness
- Published research on agent-human priming
- Open-source core components
- Small-scale pilots (n=100)
Mid-term (2026-2028):
- Multi-agent coordination at scale
- Personalized priming schedules
- Cross-cultural adaptation
- Large-scale deployments (n=10,000+)
Long-term (2028+):
- Seamless human-AI collaborative cognition
- Evidence-based behavioral health tools
- Educational transformation
- Enhanced human agency and autonomy
Ultimate Goal: A world where technology genuinely amplifies human capacity while respecting and enhancing individual autonomy.
✅ Research-based behavior engineering ✅ Transparent, consensual influence ✅ User-goal aligned interventions ✅ Evidence-tier classification ✅ Continuous ethical monitoring
❌ Manipulation for commercial gain ❌ Hidden persuasion techniques ❌ Exploitation of vulnerabilities ❌ Social engineering attacks ❌ Dark patterns or deception
Nudging ≠ Manipulation
- Nudging preserves choice and autonomy
- Manipulation restricts options or deceives
Priming ≠ Mind Control
- Priming facilitates processing
- Effects are subtle and context-dependent
- User always retains agency
Behavior Engineering ≠ Coercion
- Engineering creates environments that support goals
- Coercion removes choice
Project Lead: Behaviour Lab Research Team
Repository: ~/Documents/GitHub/behaviour-lab/
Status: Research & Design Phase
Next Milestone: Multi-Persona Review
🤝 Research Partners: Psychology, HCI, AI ethics experts 🤝 Implementation: Software engineers, UX designers 🤝 Ethical Review: Ethicists, privacy advocates 🤝 User Studies: Research participants, pilot users
License to be determined - likely open-source with ethical use restrictions
Core principles:
- Open research and findings
- Ethical use requirements
- No manipulation for profit
- User welfare priority
Research Foundations:
- Daniel Kahneman, Amos Tversky (heuristics & biases)
- Richard Thaler, Cass Sunstein (nudge theory)
- John Bargh (priming research - and subsequent replication crisis lessons)
- UK Behavioural Insights Team (practical applications)
- Replication Index (critical evaluation)
This work stands on the shoulders of giants and learns from both their successes and failures.
Last Updated: 2025-12-02 Version: 1.0.0 Next Review: After multi-persona review
- Multi-Persona Research Review ← YOU ARE HERE
- Human sign-off on research and design
- Begin Phase 1 implementation (MVP)
- Initial user pilot study
- Iterate based on evidence
Ready to transform human-AI interaction through evidence-based behavioral engineering.