BSM-Behavioral Strategy Integration Ontology
This ontology maps the relationships between the Behavioral State Model and the Behavioral Strategy framework, providing a computational model for integration.
Visual Ontology
graph TB
subgraph "Behavioral State Model"
BSM[Behavioral State]
BSM --> P[Personality]
BSM --> PE[Perception]
BSM --> E[Emotions]
BSM --> A[Abilities]
BSM --> SS[Social Status]
BSM --> M[Motivations]
BSM --> SE[Social Environment]
BSM --> PHE[Physical Environment]
ID[Identity] --> P
ID --> PE
ID --> E
ID --> A
ID --> SS
ID --> M
ENV[Environment] --> SE
ENV --> PHE
end
subgraph "Behavioral Strategy Framework"
BS[Behavioral Strategy]
BS --> PMF[Problem Market Fit]
BS --> BMF[Behavior Market Fit]
BS --> SMF[Solution Market Fit]
BS --> PMFB[Product Market Fit]
DRIVE[DRIVE Framework]
DRIVE --> D[Define]
DRIVE --> R[Research]
DRIVE --> I[Integrate]
DRIVE --> V[Verify]
DRIVE --> EN[Enhance]
end
subgraph "Integration Points"
PMF -.-> PE
PMF -.-> M
PMF -.-> E
BMF -.-> A
BMF -.-> P
BMF -.-> SE
BMF -.-> PHE
SMF -.-> A
SMF -.-> PE
SMF -.-> M
D -.-> BSM
R -.-> ID
I -.-> ENV
V -.-> BSM
EN -.-> BSM
end
style BSM fill:#f9f,stroke:#333,stroke-width:4px
style BS fill:#bbf,stroke:#333,stroke-width:4px
Computational Ontology
RDF/OWL Representation
@prefix bsm: <https://behavioralstrategy.com/ontology/bsm#> .
@prefix bs: <https://behavioralstrategy.com/ontology/bs#> .
@prefix owl: <http://www.w3.org/2002/07/owl#> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
# Behavioral State Model Classes
bsm:BehavioralState a owl:Class ;
rdfs:label "Behavioral State" ;
rdfs:comment "A complete behavioral state at a moment in time" .
bsm:IdentityComponent a owl:Class ;
rdfs:label "Identity Component" ;
rdfs:subClassOf bsm:StateComponent .
bsm:EnvironmentComponent a owl:Class ;
rdfs:label "Environment Component" ;
rdfs:subClassOf bsm:StateComponent .
# BSM Components
bsm:Personality a owl:Class ;
rdfs:subClassOf bsm:IdentityComponent ;
bs:influencesFit bs:BehaviorMarketFit .
bsm:Perception a owl:Class ;
rdfs:subClassOf bsm:IdentityComponent ;
bs:influencesFit bs:ProblemMarketFit, bs:SolutionMarketFit .
bsm:Emotions a owl:Class ;
rdfs:subClassOf bsm:IdentityComponent ;
bs:influencesFit bs:ProblemMarketFit .
bsm:Abilities a owl:Class ;
rdfs:subClassOf bsm:IdentityComponent ;
bs:influencesFit bs:BehaviorMarketFit, bs:SolutionMarketFit .
bsm:SocialStatus a owl:Class ;
rdfs:subClassOf bsm:IdentityComponent ;
bs:influencesFit bs:BehaviorMarketFit .
bsm:Motivations a owl:Class ;
rdfs:subClassOf bsm:IdentityComponent ;
bs:influencesFit bs:ProblemMarketFit, bs:SolutionMarketFit .
bsm:SocialEnvironment a owl:Class ;
rdfs:subClassOf bsm:EnvironmentComponent ;
bs:influencesFit bs:BehaviorMarketFit .
bsm:PhysicalEnvironment a owl:Class ;
rdfs:subClassOf bsm:EnvironmentComponent ;
bs:influencesFit bs:BehaviorMarketFit .
# Behavioral Strategy Classes
bs:BehavioralStrategy a owl:Class ;
rdfs:label "Behavioral Strategy" .
bs:FourFitHierarchy a owl:Class ;
rdfs:subClassOf bs:BehavioralStrategy .
bs:ProblemMarketFit a owl:Class ;
rdfs:subClassOf bs:FourFitHierarchy ;
bsm:requiredComponents bsm:Perception, bsm:Motivations, bsm:Emotions .
bs:BehaviorMarketFit a owl:Class ;
rdfs:subClassOf bs:FourFitHierarchy ;
bsm:requiredComponents bsm:Abilities, bsm:Personality,
bsm:SocialEnvironment, bsm:PhysicalEnvironment .
bs:SolutionMarketFit a owl:Class ;
rdfs:subClassOf bs:FourFitHierarchy ;
bsm:requiredComponents bsm:Abilities, bsm:Perception, bsm:Motivations .
# Properties
bsm:hasComponent a owl:ObjectProperty ;
rdfs:domain bsm:BehavioralState ;
rdfs:range bsm:StateComponent .
bsm:componentScore a owl:DatatypeProperty ;
rdfs:domain bsm:StateComponent ;
rdfs:range xsd:decimal .
bs:influencesFit a owl:ObjectProperty ;
rdfs:domain bsm:StateComponent ;
rdfs:range bs:FourFitHierarchy .
JSON-LD Implementation
{
"@context": {
"bsm": "https://behavioralstrategy.com/ontology/bsm#",
"bs": "https://behavioralstrategy.com/ontology/bs#",
"componentScore": {
"@id": "bsm:componentScore",
"@type": "@id"
},
"influencesFit": {
"@id": "bs:influencesFit",
"@type": "@id"
}
},
"@graph": [
{
"@id": "bsm:BehavioralState",
"@type": "Class",
"label": "Behavioral State",
"hasComponents": [
"bsm:Personality",
"bsm:Perception",
"bsm:Emotions",
"bsm:Abilities",
"bsm:SocialStatus",
"bsm:Motivations",
"bsm:SocialEnvironment",
"bsm:PhysicalEnvironment"
]
},
{
"@id": "bs:BehaviorMarketFit",
"@type": "Class",
"requiredBSMComponents": [
{
"component": "bsm:Abilities",
"minScore": 6,
"weight": 0.25
},
{
"component": "bsm:Personality",
"minScore": 5,
"weight": 0.20
},
{
"component": "bsm:SocialEnvironment",
"minScore": 4,
"weight": 0.15
},
{
"component": "bsm:PhysicalEnvironment",
"minScore": 4,
"weight": 0.15
}
]
}
]
}
Mapping Functions
BSM to Problem Market Fit
def map_bsm_to_pmf(behavioral_state: BehavioralState) -> Dict[str, float]:
"""
Map BSM components to Problem Market Fit assessment
"""
return {
'problem_perception': behavioral_state.perception,
'problem_urgency': (behavioral_state.emotions * 0.5 +
behavioral_state.motivations * 0.5),
'solution_seeking': behavioral_state.motivations,
'pmf_score': calculate_pmf_from_bsm(behavioral_state)
}
def calculate_pmf_from_bsm(state: BehavioralState) -> float:
"""
Calculate Problem Market Fit score from BSM components
PMF = f(Perception, Emotions, Motivations)
"""
weights = {
'perception': 0.40, # Problem awareness and beliefs
'emotions': 0.30, # Emotional urgency
'motivations': 0.30 # Drive to find solution
}
score = (
state.perception * weights['perception'] +
state.emotions * weights['emotions'] +
state.motivations * weights['motivations']
)
return min(10, score)
BSM to Behavior Market Fit
def map_bsm_to_bmf(behavioral_state: BehavioralState,
target_behavior: str) -> Dict[str, float]:
"""
Map BSM components to Behavior Market Fit assessment
"""
behavior_requirements = get_behavior_requirements(target_behavior)
component_fits = {
'ability_fit': (behavioral_state.abilities /
behavior_requirements['ability_needed']),
'personality_fit': calculate_personality_behavior_fit(
behavioral_state.personality,
behavior_requirements['personality_profile']
),
'environment_fit': (
behavioral_state.social_environment * 0.5 +
behavioral_state.physical_environment * 0.5
) / behavior_requirements['environment_support_needed'],
'motivation_behavior_alignment': calculate_motivation_alignment(
behavioral_state.motivations,
behavior_requirements['reward_structure']
)
}
# Apply minimum component rule from BSM
min_fit = min(component_fits.values())
avg_fit = sum(component_fits.values()) / len(component_fits)
return {
'bmf_score': avg_fit * 10,
'minimum_component_fit': min_fit,
'component_breakdown': component_fits,
'will_perform': min_fit > 0.5 # BSM rule: all components must be sufficient
}
BSM to Solution Market Fit
def map_bsm_to_smf(behavioral_state: BehavioralState,
solution_features: Dict) -> Dict[str, float]:
"""
Map BSM components to Solution Market Fit assessment
"""
return {
'ability_solution_match': evaluate_solution_ability_match(
behavioral_state.abilities,
solution_features['complexity']
),
'perception_solution_alignment': evaluate_perception_alignment(
behavioral_state.perception,
solution_features['value_proposition']
),
'motivation_solution_fit': evaluate_motivation_fit(
behavioral_state.motivations,
solution_features['reward_mechanisms']
),
'smf_score': calculate_smf_from_bsm(behavioral_state, solution_features)
}
Integration Algorithms
Unified Scoring System
class BSMBehavioralStrategyScorer:
"""
Unified scoring system integrating BSM with Behavioral Strategy
"""
def __init__(self):
self.component_mappings = self._initialize_mappings()
def _initialize_mappings(self) -> Dict:
"""Define how BSM components map to BS concepts"""
return {
'problem_market_fit': {
'perception': 0.4,
'emotions': 0.3,
'motivations': 0.3
},
'behavior_market_fit': {
'personality': 0.15,
'perception': 0.10,
'emotions': 0.10,
'abilities': 0.20,
'social_status': 0.10,
'motivations': 0.15,
'social_environment': 0.10,
'physical_environment': 0.10
},
'solution_market_fit': {
'perception': 0.25,
'abilities': 0.35,
'motivations': 0.25,
'physical_environment': 0.15
},
'product_market_fit': {
# All components contribute to product-market fit
'personality': 0.125,
'perception': 0.125,
'emotions': 0.125,
'abilities': 0.125,
'social_status': 0.125,
'motivations': 0.125,
'social_environment': 0.125,
'physical_environment': 0.125
}
}
def calculate_unified_score(
self,
behavioral_state: BehavioralState,
target_behavior: str,
solution_features: Optional[Dict] = None
) -> Dict:
"""
Calculate all four fits using BSM components
"""
state_vector = behavioral_state.to_vector()
components = [
'personality', 'perception', 'emotions', 'abilities',
'social_status', 'motivations', 'social_environment',
'physical_environment'
]
state_dict = dict(zip(components, state_vector))
# Calculate each fit
pmf = self._calculate_fit('problem_market_fit', state_dict)
bmf = self._calculate_fit('behavior_market_fit', state_dict)
smf = self._calculate_fit('solution_market_fit', state_dict) if solution_features else None
pmf_behavioral = self._calculate_fit('product_market_fit', state_dict)
# Apply BSM minimum component rule
min_component = behavioral_state.minimum_component
return {
'problem_market_fit': pmf,
'behavior_market_fit': bmf,
'solution_market_fit': smf,
'product_market_fit': pmf_behavioral,
'limiting_component': min_component[0],
'limiting_score': min_component[1],
'overall_viability': self._calculate_overall_viability(
pmf, bmf, smf, min_component[1]
)
}
def _calculate_fit(self, fit_type: str, state_dict: Dict) -> float:
"""Calculate specific fit score from BSM components"""
mappings = self.component_mappings[fit_type]
score = sum(
state_dict[comp] * weight
for comp, weight in mappings.items()
)
return score
def _calculate_overall_viability(
self,
pmf: float,
bmf: float,
smf: Optional[float],
min_component: float
) -> str:
"""Determine overall strategic viability"""
if min_component < 3:
return "BLOCKED" # BSM rule: insufficient component
elif pmf < 5:
return "NO_PROBLEM_FIT"
elif bmf < 5:
return "NO_BEHAVIOR_FIT"
elif smf and smf < 5:
return "NO_SOLUTION_FIT"
elif pmf >= 7 and bmf >= 7:
return "HIGH_VIABILITY"
else:
return "MODERATE_VIABILITY"
DRIVE Framework Integration
BSM-Enhanced DRIVE Implementation
class BSMDriveFramework:
"""DRIVE Framework enhanced with BSM insights"""
def define_with_bsm(self, user_segments: List[Dict]) -> Dict:
"""Define phase using BSM profiling"""
segment_states = []
for segment in user_segments:
# Create behavioral state profile
state_profile = self._create_segment_state_profile(segment)
segment_states.append({
'segment': segment,
'behavioral_state': state_profile,
'problem_recognition_capacity': self._assess_problem_capacity(state_profile)
})
return {
'target_segments': self._prioritize_segments(segment_states),
'behavioral_requirements': self._extract_requirements(segment_states)
}
def research_with_bsm(self, target_segments: List[Dict]) -> Dict:
"""Research phase using BSM components"""
research_plan = {
'personality_research': {
'methods': ['psychographic surveys', 'value assessments'],
'metrics': ['trait distributions', 'value alignments']
},
'perception_research': {
'methods': ['belief mapping', 'mental model interviews'],
'metrics': ['belief strengths', 'perception gaps']
},
'emotion_research': {
'methods': ['emotion journey mapping', 'trigger analysis'],
'metrics': ['emotional states', 'emotion-behavior correlations']
},
'ability_research': {
'methods': ['skill assessments', 'capability testing'],
'metrics': ['ability distributions', 'skill gaps']
},
'social_research': {
'methods': ['social network analysis', 'status mapping'],
'metrics': ['social influences', 'status impacts']
},
'motivation_research': {
'methods': ['incentive testing', 'motivation profiling'],
'metrics': ['motivation types', 'reward sensitivities']
},
'environment_research': {
'methods': ['context mapping', 'environment audits'],
'metrics': ['environmental barriers', 'contextual enablers']
}
}
return research_plan
def integrate_with_bsm(
self,
behavioral_states: List[BehavioralState],
target_behavior: str
) -> Dict:
"""Integrate phase using BSM state modification"""
integration_strategies = []
for state in behavioral_states:
# Identify components needing modification
gaps = self._identify_component_gaps(state, target_behavior)
# Design interventions for each gap
for component, gap in gaps.items():
strategy = {
'component': component,
'current_value': getattr(state, component),
'target_value': gap['target'],
'intervention': self._design_component_intervention(
component, gap
)
}
integration_strategies.append(strategy)
return {
'intervention_plan': integration_strategies,
'implementation_sequence': self._sequence_interventions(integration_strategies)
}
def verify_with_bsm(
self,
initial_states: List[BehavioralState],
current_states: List[BehavioralState],
target_behavior: str
) -> Dict:
"""Verify phase using BSM state tracking"""
verification_results = {
'state_changes': [],
'behavior_adoption': [],
'component_improvements': {}
}
for initial, current in zip(initial_states, current_states):
# Track component changes
changes = {
comp: getattr(current, comp) - getattr(initial, comp)
for comp in ['personality', 'perception', 'emotions',
'abilities', 'social_status', 'motivations',
'social_environment', 'physical_environment']
}
verification_results['state_changes'].append(changes)
# Verify behavior likelihood improvement
initial_likelihood = self._calculate_behavior_likelihood(
initial, target_behavior
)
current_likelihood = self._calculate_behavior_likelihood(
current, target_behavior
)
verification_results['behavior_adoption'].append({
'initial': initial_likelihood,
'current': current_likelihood,
'improvement': current_likelihood - initial_likelihood
})
return verification_results
def enhance_with_bsm(
self,
behavioral_states: List[BehavioralState],
performance_data: Dict
) -> Dict:
"""Enhance phase using BSM optimization"""
enhancement_recommendations = []
# Analyze which components most influence success
component_impacts = self._analyze_component_impacts(
behavioral_states,
performance_data
)
# Generate enhancement strategies
for component, impact in component_impacts.items():
if impact['influence'] > 0.7: # High influence component
recommendation = {
'component': component,
'current_impact': impact['influence'],
'optimization_potential': impact['potential'],
'enhancement_strategies': self._generate_enhancements(
component, impact
)
}
enhancement_recommendations.append(recommendation)
return {
'priority_enhancements': enhancement_recommendations[:3],
'expected_improvements': self._project_improvements(
enhancement_recommendations
)
}
Practical Applications
Healthcare Example
# Using BSM to improve medication adherence
patient_state = BehavioralState(
timestamp=datetime.now(),
user_id="patient_123",
personality=5, # Moderate health consciousness
perception=3, # Low belief in medication importance
emotions=7, # Anxious about health
abilities=8, # Capable of taking medication
social_status=6, # Family supportive
motivations=4, # Low immediate reward perception
social_environment=7, # Doctor recommends
physical_environment=9 # Easy access to pharmacy
)
# Apply integrated scoring
scorer = BSMBehavioralStrategyScorer()
results = scorer.calculate_unified_score(
patient_state,
"daily_medication_adherence"
)
print(f"PMF: {results['problem_market_fit']:.1f}") # 4.9 - Moderate
print(f"BMF: {results['behavior_market_fit']:.1f}") # 5.7 - Moderate
print(f"Limiting factor: {results['limiting_component']}") # perception
print(f"Viability: {results['overall_viability']}") # NO_PROBLEM_FIT
# Intervention recommendation: Focus on perception component
# - Education about medication importance
# - Show clear health outcome data
# - Connect with success stories
E-commerce Example
# Using BSM for purchase behavior prediction
shopper_state = BehavioralState(
timestamp=datetime.now(),
user_id="shopper_456",
personality=8, # Loves shopping
perception=7, # Believes in product value
emotions=6, # Positive mood
abilities=9, # High purchasing power
social_status=7, # Status conscious
motivations=8, # Motivated by rewards
social_environment=6, # Peer influence moderate
physical_environment=10 # Easy online access
)
# Predict purchase likelihood
predictor = BSMRealTimePredictor()
prediction = predictor.predict_behavior_likelihood(
"shopper_456",
"premium_purchase",
domain="ecommerce"
)
print(f"Purchase likelihood: {prediction['likelihood']:.1%}") # 76%
print(f"Will perform: {prediction['will_perform']}") # True
print(f"Confidence: {prediction['confidence']:.1%}") # 82%
API Reference
GraphQL Schema
type BehavioralState {
id: ID!
userId: String!
timestamp: DateTime!
personality: Float!
perception: Float!
emotions: Float!
abilities: Float!
socialStatus: Float!
motivations: Float!
socialEnvironment: Float!
physicalEnvironment: Float!
identityScore: Float!
environmentScore: Float!
minimumComponent: ComponentScore!
}
type ComponentScore {
component: String!
score: Float!
}
type BehavioralStrategyAssessment {
problemMarketFit: Float!
behaviorMarketFit: Float!
solutionMarketFit: Float
productMarketFit: Float
limitingComponent: String!
overallViability: Viability!
}
enum Viability {
BLOCKED
NO_PROBLEM_FIT
NO_BEHAVIOR_FIT
NO_SOLUTION_FIT
MODERATE_VIABILITY
HIGH_VIABILITY
}
type Query {
getBehavioralState(userId: String!): BehavioralState
assessBehavioralStrategy(
userId: String!
targetBehavior: String!
solutionFeatures: JSON
): BehavioralStrategyAssessment
}
type Mutation {
updateBehavioralState(
userId: String!
component: String!
value: Float!
): BehavioralState
}
Next Steps
- Implement BSM scoring in Behavior Prediction API
- Use BSM components in BMF Scoring Algorithm
- Apply BSM to Real-Time Predictor
- Review BSM Integration Guide
Licensing: This ontology specification is protected under the Behavioral Strategy Specification License (BSSL). Commercial use requires explicit licensing. See full licensing terms for details.
Attribution: This ontology integrates Jason Hreha’s Behavioral State Model with the Behavioral Strategy framework.