LBTAS: Leveson-Based Trade Assessment Scale

A Research Framework for Digital Commerce Rating Systems

Network Theory Applied Research Institute, Inc.

Forge Laboratory

Educational Research Disclaimer

This whitepaper presents LBTAS as a research tool and educational framework for studying rating systems and assessment methodologies. Policy applications discussed herein represent theoretical research scenarios for academic analysis rather than advocacy positions. NTARI's mission focuses on developing research tools and educational resources for studying cooperative systems, not influencing specific legislation or policy outcomes.

All policy discussions are presented as hypothetical research applications to demonstrate the analytical capabilities of LBTAS methodology for academic study of economic systems, consumer protection mechanisms, and market efficiency frameworks.

Executive Summary

The Leveson-Based Trade Assessment Scale (LBTAS) represents a research framework for studying digital commerce rating systems through application of safety-critical software evaluation principles. Developed by the Network Theory Applied Research Institute's Forge Laboratory, LBTAS implements Nancy Leveson's aircraft software assessment methodology as a core rating logic module that enables research into meaningful, actionable feedback systems for digital trade environments.

Architectural Clarity: LBTAS is designed as a foundational rating calculation engine that requires integration with external systems for persistence, user interfaces, and advanced analytics. The core module provides rating collection, validation, and aggregation logic while deliberately remaining agnostic about implementation context to maximize reusability across diverse research platforms.

Unlike conventional rating systems designed for marketing rather than improvement, LBTAS provides a sophisticated 6-point scale (-1 to +4) that enables researchers to study precise evaluation mechanisms and their effects on trade interaction quality. This whitepaper documents the theoretical foundation, core module architecture, integration requirements, and research potential of LBTAS for studying cooperative digital commerce platforms and economic assessment methodologies.

Core Module Architecture vs. Integration Ecosystem

What LBTAS Core Module Provides

The LBTAS core module (available as open-source Python implementation) delivers:

• Rating Logic Engine: Precise 6-point scale implementation with validation and aggregation

• Agnostic Exchange Management: Generic "exchange" entities that can represent vendors, customers, or any ratable interaction

• Multi-Criteria Assessment: Four standardized criteria (reliability, usability, performance, support) with extensible framework

• Rating Validation: Input validation ensuring ratings fall within valid scale (-1 to +4)

• Statistical Aggregation: Average calculation and basic analytics on collected ratings

• Data Structure Management: In-memory storage optimized for integration with external persistence layers

Critical Design Principle: The core module intentionally avoids assumptions about implementation context, database systems, user interfaces, or platform requirements to maximize integration flexibility for research applications.

What Integration Platforms Must Provide

Successful LBTAS research deployment requires integration platforms to provide:

• Persistence Layer: Database integration for permanent rating storage and retrieval

• User Interface Systems: Web forms, mobile apps, or API endpoints for rating collection

• Authentication and Authorization: User management and access control appropriate to platform context

• Analytics and Visualization: Business intelligence tools for pattern analysis and reporting

• Platform-Specific Features: Integration with existing e-commerce, B2B, or specialized industry systems

Integration Philosophy: LBTAS provides the rating logic; platforms provide the infrastructure and user experience for research implementation.

Bidirectional Assessment Through Agnostic Design

Design Elegance of Context-Agnosticism

LBTAS achieves bidirectional assessment capability through intentional agnosticism rather than complex routing logic. The core module treats all ratable entities as generic "exchanges" without pre-assuming their role in commerce relationships.

Vendor Assessment Implementation:

python

rating_system.add_exchange("vendor_amazon")
rating_system.rate_exchange("vendor_amazon")  # Customer rates vendor

Customer Assessment Implementation:

python

rating_system.add_exchange("customer_john_doe")
rating_system.rate_exchange("customer_john_doe")  # Vendor rates customer

Bidirectional Transaction Assessment:

python

rating_system.add_exchange("transaction_tx_12345")
# Both parties can rate the same transaction interaction

This agnostic approach enables any research platform to implement bidirectional assessment by simply creating exchange entities for all parties requiring evaluation, without requiring the core module to understand commerce relationship complexity.

Platform Integration Responsibilities

Integration platforms must design user interfaces and workflows that:

• Determine which entities should be ratable in specific research contexts

• Present appropriate rating opportunities to relevant parties

• Manage the business logic of who can rate whom and when

• Aggregate bidirectional ratings for comprehensive reputation system research

The core module provides the rating calculation foundation; platforms implement the bidirectional user experience for research purposes.

Technical Architecture: Core Module Specifications

Minimal Dependencies Approach

LBTAS core module requires only:

• Python 3.6+: No external package dependencies

• Memory: Minimal RAM footprint for in-memory data structures

• Integration Interface: Simple class instantiation and method calls

No Built-in Requirements For:

• Database systems (integration platform responsibility)

• Web frameworks (integration platform responsibility)

• Authentication systems (integration platform responsibility)

• Network connectivity (integration platform responsibility)

Rating Scale Implementation

The 6-point Leveson scale is implemented with precise validation:

python

VALID_RATINGS = {
    -1: "No Trust - User was harmed, exploited, or received product/service with evidence of no discipline or malicious intent",
    0: "Cynical Satisfaction - Interaction fulfills basic promise requiring little discipline toward user satisfaction",
    1: "Basic Promise - Interaction meets all articulated user demands, no more",
    2: "Basic Satisfaction - Interaction meets socially acceptable standards exceeding articulated demands",
    3: "No Negative Consequences - Interaction designed to prevent loss, exceed basic quality",
    4: "Delight - Interaction anticipates evolution of user practices and concerns post-transaction"
}

• Input Validation: Core module rejects any rating outside the -1 to +4 range

• Aggregation Logic: Calculates precise averages and provides raw data access for advanced analytics

• Data Integrity: Maintains consistency in rating storage and retrieval

Multi-Criteria Framework

Four standardized assessment criteria provide comprehensive evaluation for research purposes:

• Reliability: Consistency and dependability of service/behavior delivery

• Usability: Interface quality, communication effectiveness, ease of interaction

• Performance: Speed, efficiency, technical capability demonstration

• Support: Problem resolution, customer service, ongoing assistance quality

Extensibility: Integration platforms can adapt criteria definitions to domain-specific research requirements while maintaining core assessment methodology.

Integration Requirements and Implementation Guide

Database Integration Specifications

Integration platforms must provide:

Persistent Storage Requirements:

• Rating history with timestamps for trend analysis

• Exchange metadata for business logic implementation

• User authentication data linking ratings to verified participants

• Audit trails for rating modification and dispute resolution

Recommended Schema Example:

sql

CREATE TABLE lbtas_ratings (
    id SERIAL PRIMARY KEY,
    exchange_id VARCHAR(255) NOT NULL,
    rater_id VARCHAR(255) NOT NULL,
    criterion VARCHAR(50) NOT NULL,
    rating INTEGER CHECK (rating >= -1 AND rating <= 4),
    timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    comment TEXT
);

Data Integration Pattern:

python

# Platform loads existing ratings into LBTAS core
rating_system = LevesonRatingSystem()
for rating_record in database.get_ratings(exchange_id):
    rating_system.exchanges[exchange_id][rating_record.criterion].append(rating_record.rating)

# Platform captures new ratings from LBTAS core
new_ratings = rating_system.get_recent_ratings()
database.store_ratings(new_ratings)

User Interface Integration Requirements

Successful LBTAS research deployment requires integration platforms to design:

• Rating Collection Interfaces: User-friendly forms presenting the 6-point scale with clear explanations

• Rating Display Systems: Aggregated rating presentation with meaningful visualization

• Historical Analysis Views: Trend analysis and pattern recognition interfaces

• Dispute Resolution Workflows: Mechanisms for addressing contested ratings

Example Integration Architecture:

python

# E-commerce platform integration
class PlatformRatingInterface:
    def __init__(self):
        self.lbtas = LevesonRatingSystem()
        self.database = PlatformDatabase()
        
    def collect_vendor_rating(self, user_id, vendor_id):
        # Platform handles authentication and authorization
        if self.platform.can_user_rate_vendor(user_id, vendor_id):
            # LBTAS handles rating logic
            self.lbtas.add_exchange(vendor_id)
            rating = self.lbtas.rate_exchange(vendor_id)
            # Platform handles persistence
            self.database.store_rating(user_id, vendor_id, rating)

Theoretical Research Application: Economic Assessment Framework Analysis

Research Questions for Academic Study

LBTAS methodology raises important research questions about assessment systems and economic frameworks:

• How might quality-based assessment systems compare to traditional economic evaluation mechanisms in terms of market efficiency and consumer protection?

• What methodological frameworks could help researchers study the relationship between assessment sophistication and market behavior?

• How do different rating scale designs affect user behavior and market outcomes in controlled research environments?

• What are the measurable effects of bidirectional assessment systems on trust and cooperation in digital commerce research?

Hypothetical Research Application: Trade Policy Analysis Framework

Academic Research Context: Researchers studying international trade policy effectiveness could theoretically utilize LBTAS methodology to analyze quality-based assessment alternatives to traditional geographic-based trade frameworks.

Research Methodology Framework:

python

# Hypothetical research implementation for academic study
class TradeResearchFramework:
    def __init__(self):
        self.assessment_system = LevesonRatingSystem()
        self.research_database = AcademicResearchDatabase()
        
    def study_supplier_assessment_patterns(self, supplier_id, assessment_data):
        # Research framework for studying international trade assessment
        self.assessment_system.add_exchange(supplier_id)
        
        # Multi-source assessment data for research analysis
        # - Transaction outcome data from research partnerships
        # - Quality audit results from research collaborations
        # - Regulatory compliance monitoring for academic study
        # - Standards certification data for research purposes
        
        for criterion, rating in assessment_data.items():
            self.assessment_system.exchanges[supplier_id][criterion].append(rating)
            
        # Generate research analysis
        average_score = self.assessment_system.view_ratings(supplier_id)
        research_metrics = self.calculate_research_indicators(average_score)
        
        return {
            "supplier": supplier_id,
            "lbtas_score": average_score,
            "research_indicators": research_metrics,
            "academic_analysis": self.generate_research_summary(average_score)
        }

Comparative Framework Analysis for Academic Research

Research Opportunity: Academic institutions could study how quality-based assessment frameworks compare to traditional approaches across multiple dimensions:

Traditional Assessment Limitations (Research Observations):

• Binary evaluation mechanisms regardless of actual quality variations

• Limited granularity for studying quality improvement incentives

• Difficulty distinguishing between high-quality and low-quality entities within categories

• Reduced research opportunities for studying quality-based market dynamics

LBTAS Research Framework Advantages (Theoretical):

• Quality-Based Differentiation: Enables research into merit-based evaluation regardless of categorical origin

• Consumer Protection Research: Systematic identification of reliable vs. unreliable entities for academic study

• Market Efficiency Studies: Creates research opportunities for studying quality-improvement incentives

• Behavioral Research: Quality-based assessment reduces confounding variables in market behavior studies

• Innovation Research: Enables study of quality-based competition rather than categorical protection

Research Implementation Framework for Academic Study

Academic Research Integration Example:

python

# Academic research framework implementation
class AcademicTradeResearch:
    def __init__(self):
        self.research_system = LevesonRatingSystem()
        self.academic_database = UniversityResearchDatabase()
        
    def analyze_quality_assessment_framework(self, entity_id, research_data):
        # Standardized research assessment framework
        self.research_system.add_exchange(entity_id)
        
        # Multi-source research data collection
        # - Academic partner feedback from controlled studies
        # - Third-party research audit results
        # - Regulatory compliance research data
        # - International standards research partnerships
        
        for criterion, rating in research_data.items():
            self.research_system.exchanges[entity_id][criterion].append(rating)
            
        # Generate academic research analysis
        average_score = self.research_system.view_ratings(entity_id)
        research_framework = self.calculate_academic_metrics(average_score)
        
        return {
            "research_entity": entity_id,
            "lbtas_research_score": average_score,
            "academic_indicators": research_framework,
            "research_methodology": self.generate_academic_analysis(average_score)
        }

Academic Research Benefits (Theoretical):

• Objective Standards: LBTAS provides transparent, criteria-based assessment for reducing research bias

• Quality Improvement Studies: Research subjects receive clear feedback for improvement rather than categorical exclusion

• Consumer Research: Studies focus on protecting research participants through quality assurance frameworks

• Evidence-Based Analysis: Research decisions based on demonstrated performance data rather than categorical considerations

• Global Standard Research: Academic implementation could encourage international research collaboration on quality-based frameworks

Example Research Application Framework: Rather than studying broad categorical approaches, researchers could implement LBTAS assessment considering:

• Reliability: Consistency testing, performance validation, standard fulfillment rates

• Usability: Documentation quality, support availability, compatibility research

• Performance: Efficiency ratings, durability research, innovation metrics analysis

• Support: Service quality research, problem resolution studies, sustainability program analysis

High-performing entities in research studies receive expedited processing advantages regardless of categorical origin, while low-performing entities face enhanced scrutiny and quality verification requirements for research purposes.

This approach enables researchers to study consumer protection and market efficiency frameworks while examining quality-based incentive systems and encouraging improvement in research contexts.

Research Applications and Academic Analysis Framework

Comparative Assessment Research Opportunities

LBTAS enables empirical research into assessment system effectiveness through:

• Quality-Based vs. Categorical Assessment Research: Comparative analysis of consumer outcomes, market efficiency, and behavioral patterns under different assessment frameworks

• Innovation Incentive Analysis: Measurement of how quality-based assessment advantages affect innovation rates in controlled research environments

• Consumer Protection Effectiveness Research: Assessment of whether LBTAS-based evaluation systems better protect research participants than traditional categorical approaches

• Market Competition Quality Studies: Analysis of whether quality-based assessment frameworks improve competitive dynamics in research settings

Economic Development Research Framework

International Development Research Applications: Analysis of how LBTAS-based assessment frameworks could support quality improvement research in developing economies rather than excluding producers from research studies

Supply Chain Resilience Research: Investigation of how quality-based supplier assessment creates more resilient systems in controlled research environments

Democratic Assessment Research: Study of how transparent, criteria-based evaluation could improve accountability in assessment systems through academic analysis

Integration Success Metrics and Performance Assessment

Core Module Performance Standards

• Rating Accuracy: Validation that LBTAS ratings correlate with measurable quality outcomes in research contexts

• System Reliability: Core module performance under high-volume rating collection scenarios for research purposes

• Integration Flexibility: Assessment of adaptation success across diverse research platform contexts

• Data Integrity: Verification of rating calculation accuracy and consistency in academic applications

Platform Integration Success Indicators

• User Adoption Rates: Measurement of user engagement with LBTAS-based rating systems compared to traditional alternatives in research settings

• Quality Improvement Evidence: Documentation of entity quality enhancement following LBTAS implementation in controlled studies

• Market Efficiency Gains: Analysis of improved decision-making and competitive dynamics in research environments

• Community Trust Development: Assessment of whether LBTAS implementation strengthens confidence in rating systems through academic study

Academic Research Implementation Metrics

• Research Quality Improvement: Measurement of assessment accuracy enhancement under LBTAS-based research frameworks

• Participant Protection Enhancement: Assessment of reduced problematic entity impact on research participants

• Research Relationship Quality: Analysis of collaboration effects from quality-based vs. categorical research approaches

• Academic Competition Health: Evaluation of competitive dynamics under quality-focused research frameworks

Technical Specifications Summary

Core Module Requirements

• Python 3.6+ (no external dependencies)

• Memory: <10MB typical usage

• Processing: Minimal CPU requirements for rating validation and aggregation

• Integration Interface: Simple class instantiation and method calls

Platform Integration Requirements

• Database System: SQL or NoSQL for persistent rating storage

• Web Framework: Flask, Django, or equivalent for user interface delivery

• Authentication System: User management appropriate to platform security requirements

• Analytics Platform: Business intelligence tools for rating pattern analysis

API Integration Template

python

# Minimal integration example
from LBTAS import LevesonRatingSystem

class PlatformIntegration:
    def __init__(self):
        self.rating_engine = LevesonRatingSystem()
        # Platform provides: database, auth, UI, analytics
        
    def process_rating(self, exchange_id, user_id, criterion, rating):
        # Platform handles authorization
        if self.authorize_rating(user_id, exchange_id):
            # LBTAS handles rating logic
            self.rating_engine.add_exchange(exchange_id)
            # Platform handles persistence
            self.store_rating(user_id, exchange_id, criterion, rating)

Conclusion: Research Framework for Assessment System Innovation

The Leveson-Based Trade Assessment Scale provides foundational rating logic that enables sophisticated research applications across digital commerce and economic assessment contexts. Through intentional architectural modesty—focusing on precise rating calculation while remaining agnostic about implementation context—LBTAS maximizes integration flexibility and reusability for academic research purposes.

Core Module Strengths for Research

• Precise 6-point scale implementation with validated assessment methodology for academic study

• Agnostic design enabling bidirectional assessment across diverse research contexts

• Minimal dependencies facilitating integration across research platforms and technologies

• Open-source architecture supporting community control and academic customization

Integration Platform Research Opportunities

• Database integration for comprehensive rating analytics and research trend analysis

• User interface development for optimal research participant experience and adoption

• Advanced analytics for academic intelligence and quality improvement research guidance

• Economic assessment applications providing alternatives to crude categorical measures through nuanced quality analysis

Academic Research Innovation

The application of LBTAS principles to assessment system research demonstrates how scientific evaluation methodology can advance academic study of economic systems, moving from categorical-based analysis to quality-based research frameworks. This represents a fundamental advancement toward evidence-based academic research that serves educational purposes while encouraging quality improvement in research contexts.

As digital platforms increasingly mediate academic research relationships and scholars seek alternatives to oversimplified assessment systems, LBTAS provides both practical implementation tools and theoretical framework for building research systems based on demonstrated quality rather than arbitrary categorical preferences.

The future of academic research into commerce systems and assessment methodologies depends on evaluation frameworks that reward genuine quality while protecting research participants from problematic actors. LBTAS provides foundational tools for building this research alternative, demonstrating how scientific assessment principles can create more effective and academically rigorous research environments for studying both digital and traditional commerce systems.

About the Network Theory Applied Research Institute

The Network Theory Applied Research Institute (NTARI) develops systems, protocols and programs for online global cooperatives inspired by network theory. Through the Forge Laboratory initiative, NTARI creates open-source research tools that enable community sovereignty, democratic participation, and cooperative economic development research.

Contact Information:Network Theory Applied Research Institute, Inc.1 Dupont Way Suite 4Louisville, KY 40207Email: info@ntari.orgWebsite: https://ntari.org

Document Information:Version: 3.0 - Educational Research Edition Date: June 2025 Repository: https://github.com/NTARI-ForgeLab/Leveson-Based-Trade-Assessment-Scale