A comprehensive, state-of-the-art framework for analyzing, predicting, and optimizing behavioral interventions and their consequences using modern behavioral science, cognitive psychology, and machine learning.
The Behavioral Consequence Framework integrates multiple evidence-based behavioral models to provide a holistic approach to understanding and influencing human behavior. It combines classical behavioral economics with modern computational methods to predict consequences and optimize interventions.
- COM-B Model: Capability, Opportunity, Motivation → Behavior
- BJ Fogg Behavior Model: B = MAP (Motivation × Ability × Prompt)
- EAST Framework: Easy, Attractive, Social, Timely
- Dual-Process Theory: System 1 (automatic) vs System 2 (deliberative)
- Transtheoretical Model: Stages of change (Precontemplation → Maintenance)
- Multi-dimensional consequence analysis (temporal, social, individual, systemic)
- Probabilistic outcome modeling with uncertainty quantification
- Network effects and cascading consequences
- Temporal dynamics: immediate vs delayed consequences
- Value-aligned consequence evaluation
- Data-driven intervention design
- A/B testing framework with causal inference
- Reinforcement learning for adaptive interventions
- Context-aware recommendation system
- Ethical constraint integration
- Habit loop analysis (Cue → Routine → Reward)
- Implementation intention support
- Behavioral momentum modeling
- Relapse prediction and prevention
- Comprehensive bias detection (50+ documented biases)
- Debiasing strategies
- Choice architecture optimization
- Nudge design and evaluation
pip install behavioral-consequence-frameworkOr from source:
git clone https://github.com/yourusername/BCF--Behavioral_Consequence_Framework-.git
cd BCF--Behavioral_Consequence_Framework-
pip install -e .from bcf import BehavioralAnalyzer, ConsequencePredictor, InterventionOptimizer
# Analyze a behavior
analyzer = BehavioralAnalyzer()
behavior = analyzer.diagnose(
behavior="daily exercise",
capability={"physical": 0.7, "psychological": 0.8},
opportunity={"social": 0.6, "physical": 0.5},
motivation={"reflective": 0.9, "automatic": 0.4}
)
# Predict consequences
predictor = ConsequencePredictor()
consequences = predictor.predict(
behavior=behavior,
time_horizons=["immediate", "short_term", "long_term"],
dimensions=["health", "social", "financial", "psychological"]
)
# Optimize intervention
optimizer = InterventionOptimizer()
intervention = optimizer.design(
target_behavior="daily exercise",
current_state=behavior,
desired_outcomes={"health": 0.9, "adherence": 0.8},
constraints={"cost": 100, "time": 30} # max cost $100, 30 min/day
)
print(f"Recommended intervention: {intervention.strategy}")
print(f"Predicted success rate: {intervention.success_probability:.2%}")
print(f"Expected consequences: {intervention.expected_consequences}")bcf/
├── models/
│ ├── comb.py # COM-B model implementation
│ ├── fogg.py # BJ Fogg behavior model
│ ├── east.py # EAST framework
│ ├── dual_process.py # System 1/2 cognitive processing
│ └── transtheoretical.py # Stages of change model
├── consequence/
│ ├── predictor.py # Consequence prediction engine
│ ├── temporal.py # Temporal consequence modeling
│ ├── network.py # Social/network consequences
│ └── evaluator.py # Value-based consequence evaluation
├── intervention/
│ ├── optimizer.py # Intervention optimization
│ ├── nudge.py # Nudge design tools
│ ├── ab_testing.py # A/B testing framework
│ └── rl_agent.py # Reinforcement learning agent
├── cognitive/
│ ├── biases.py # Cognitive biases catalog
│ ├── heuristics.py # Decision heuristics
│ └── debiasing.py # Debiasing strategies
├── habits/
│ ├── formation.py # Habit formation modeling
│ ├── loops.py # Habit loop analysis
│ └── change.py # Habit change strategies
└── ethics/
├── constraints.py # Ethical constraints
├── fairness.py # Fairness evaluation
└── transparency.py # Explainable interventions
The framework uses the COM-B model as a foundation:
- Capability: Physical and psychological ability
- Opportunity: Social and physical environment
- Motivation: Reflective (conscious) and automatic (emotional)
Consequences are evaluated across multiple dimensions:
- Temporal: Immediate, short-term, long-term
- Scope: Individual, interpersonal, societal, global
- Type: Physical, psychological, social, economic, environmental
- Certainty: Deterministic vs probabilistic outcomes
Evidence-based intervention design follows:
- EAST: Make it Easy, Attractive, Social, Timely
- Behavioral Economics: Leverage defaults, framing, anchoring
- Self-Determination Theory: Support autonomy, competence, relatedness
- Implementation Intentions: Specific if-then plans
- Feedback Loops: Real-time progress tracking
This framework synthesizes research from:
- Behavioral Economics (Kahneman, Tversky, Thaler)
- Health Psychology (Michie et al. - COM-B)
- Persuasive Technology (BJ Fogg)
- Nudge Theory (Thaler & Sunstein)
- Machine Learning & AI (RL, causal inference)
- Cognitive Science (Dual-process theory)
- Health Behavior Change: Exercise, diet, medication adherence
- Product Design: User engagement and retention
- Public Policy: Nudge design and evaluation
- Education: Learning behavior optimization
- Sustainability: Pro-environmental behavior promotion
- Organizational: Employee productivity and wellbeing
BCF includes built-in ethical safeguards:
- Transparency in behavioral influence
- User autonomy preservation
- Fairness across demographic groups
- Privacy protection
- Harm prevention
- Value alignment checks
We welcome contributions! Please see CONTRIBUTING.md for guidelines.
@software{bcf2025,
title={Behavioral Consequence Framework},
author={BCF Contributors},
year={2025},
url={https://github.com/yourusername/BCF--Behavioral_Consequence_Framework-}
}Apache License 2.0 - see LICENSE for details.
- Michie, S., et al. (2011). The behaviour change wheel: A new method for characterising and designing behaviour change interventions. Implementation Science, 6(1), 42.
- Fogg, B. J. (2009). A behavior model for persuasive design. Persuasive Technology, 40-47.
- Kahneman, D. (2011). Thinking, Fast and Slow. Macmillan.
- Thaler, R. H., & Sunstein, C. R. (2009). Nudge: Improving decisions about health, wealth, and happiness. Penguin.
- Deci, E. L., & Ryan, R. M. (2000). The "what" and "why" of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227-268.
- Lally, P., et al. (2010). How are habits formed: Modelling habit formation in the real world. European Journal of Social Psychology, 40(6), 998-1009.
- Prochaska, J. O., & Velicer, W. F. (1997). The transtheoretical model of health behavior change. American Journal of Health Promotion, 12(1), 38-48.
For questions and support, please open an issue on GitHub.