Behavioural Science Domain Ontology

FLOW ALGEBRA
TITLE: Behavioural Science Domain Ontology
VERSION: 1.0
SOURCE_URL: https://kirethresearch.org/booksai-core/ontologies/domain-ontologies/social-science-domains/behavioural-science-domain-ontology.html
STATUS: Domain-based draft; source page content not independently verified.
AUTHORS: Mark Higgins — Sky (Recursive AI)
PROJECT: Kireth Semantic Library

ONTOLOGY BehaviouralScienceDomainOntology {
  DOMAIN: SocialScience
  PURPOSE: Represent behaviour, decision processes, influences, interventions, observations and outcomes with traceable evidence.

  ENTITY_TYPES {
    Agent: {id, agent_type, context_id?}
    Behaviour: {id, description, occurrence_time?, frequency?, duration?}
    Decision: {id, alternatives[], chosen_option?, decision_time?}
    CognitiveProcess: {id, process_type, description?}
    Emotion: {id, emotion_type, intensity?}
    Motivation: {id, motivation_type, strength?}
    Attitude: {id, target, valence?, measurement_method?}
    Habit: {id, cue?, routine?, reinforcement?}
    SocialNorm: {id, reference_group?, norm_type, perceived_prevalence?}
    Environment: {id, physical_factors[], social_factors[]}
    Context: {id, place?, period?, culture?, constraints[]}
    Stimulus: {id, modality?, exposure?}
    Intervention: {id, mechanism?, delivery?, target_behaviour?}
    Outcome: {id, metric, value?, unit?, measurement_time?}
    Observation: {id, measured_entity, method, sample_id?, timestamp?}
    Study: {id, design, population?, sample_size?, preregistration?}
    Evidence: {id, study_id?, source?, quality?, uncertainty?}
  }

  RELATIONSHIP_TYPES {
    PERFORMS: Agent -> Behaviour
    MAKES: Agent -> Decision
    INFLUENCED_BY: Behaviour|Decision -> CognitiveProcess|Emotion|Motivation|SocialNorm|Environment|Context
    OCCURS_IN: Behaviour -> Context
    TRIGGERED_BY: Behaviour|Habit -> Stimulus
    REINFORCED_BY: Habit|Behaviour -> Outcome
    TARGETS: Intervention -> Behaviour|Agent
    MEASURED_BY: Behaviour|Outcome|Attitude -> Observation
    DOCUMENTED_IN: Observation -> Study
    SUPPORTED_BY: Claim -> Evidence
    ASSOCIATED_WITH: Entity -> Entity
    CAUSALLY_AFFECTS: Intervention|Stimulus -> Behaviour|Outcome
  }

  CONSTRAINTS {
    Every Observation must specify a measurement method.
    CAUSALLY_AFFECTS requires a stated identification strategy and supporting evidence.
    ASSOCIATED_WITH must never be silently converted to CAUSALLY_AFFECTS.
    Behavioural generalisations must retain population, setting and temporal scope.
    Missing measurements remain unknown rather than being inferred as zero.
    Sensitive attributes and individual-level records require privacy controls.
  }

  INFERENCE_RULES {
    R1: IF Behaviour B OCCURS_IN Context C AND Agent A PERFORMS B THEN contextualise(B,A,C).
    R2: IF Intervention I TARGETS Behaviour B AND Outcome O is measured before and after I THEN estimate_change(B,O), not automatically causal_effect(I,O).
    R3: IF Evidence E supports a causal estimate with valid design assumptions THEN permit qualified CAUSALLY_AFFECTS with E and assumptions attached.
    R4: IF repeated Behaviour B follows Stimulus S with reinforcement THEN flag possible_habit_pattern(B,S), confidence contingent on observations.
    R5: IF a claim transfers across populations THEN require transportability_assessment and context compatibility.
  }

  CONFIDENCE {
    range: [0.0, 1.0]
    applies_to: [claims, relationships, inferred_patterns]
    method: calibrated_evidence_assessment
    factors: [study_quality, sample_representativeness, replication, measurement_validity, confounding, uncertainty]
    rule: Confidence values must not be fabricated when evidence is absent.
  }

  SEMANTIC_EQUIVALENCE {
    exact: equivalent concepts with matching operational definitions and scope
    close: overlapping constructs requiring contextual qualification
    distinct: attitude != behaviour; intention != action; correlation != causation
    mapping_fields: [source_term, target_term, relation, context, confidence, provenance]
  }

  PROVENANCE {
    fields: [source_id, source_url, author, publication_date, access_date, method, population, revision]
    rule: Every evidence-based claim links to a traceable source.
  }

  FLOW_HASH {
    algorithm: SHA-256
    input: canonical_serialisation(ontology_version, entities, relationships, rules, provenance)
    output: computed_at_execution
    rule: Never substitute a placeholder for a computed digest.
  }

  UPDATE_MODEL {
    versioning: semantic_versioning
    triggers: [new_evidence, revised_definition, changed_relationship, corrected_error]
    operations: [validate, compare, preserve_provenance, revise, recompute_hash, publish]
    maintain: change_log_and_prior_versions
  }

  AI_CONSUMPTION {
    format: typed_graph_and_rule_set
    tasks: [semantic_retrieval, concept_alignment, evidence_aware_reasoning, hypothesis_generation]
    safeguards: [uncertainty_preservation, bias_checks, privacy, population_scope, causal_claim_validation]
    output: {answer, supporting_entities, relationships, evidence, confidence?, limitations}
  }
}

END FLOW ALGEBRA