Evidence Integration Example

This example demonstrates Kwasa-Kwasa’s sophisticated evidence integration capabilities, showing how to combine evidence from multiple sources, validate findings, and build robust scientific conclusions with uncertainty quantification.

Overview

Evidence integration in Kwasa-Kwasa enables:

• Multi-Source Evidence: Combining evidence from experiments, literature, simulations, and observations
• Uncertainty Quantification: Tracking and propagating uncertainty through analysis chains
• Bias Detection: Identifying and correcting for systematic biases
• Confidence Assessment: Providing confidence intervals and reliability scores
• Adaptive Learning: Updating conclusions as new evidence becomes available

Source Code

// Evidence integration example using Turbulance

import statistics
import temporal
import meta_analysis
import uncertainty

// Define evidence sources and types
item evidence_sources = {
    "experimental": ExperimentalEvidence,
    "literature": LiteratureEvidence,
    "computational": ComputationalEvidence,
    "observational": ObservationalEvidence
}

// Main evidence integration framework
evidence_integrator ClimatePredictionEvidence:
    name: "climate_change_impact_assessment"
    domain: "environmental_science"
    
    // Define evidence sources
    sources:
        - experimental_data: TemperatureExperiments
        - satellite_observations: SatelliteData
        - climate_models: ComputationalModels
        - paleoclimate_records: HistoricalData
        - literature_reviews: ScientificLiterature
    
    // Define collection parameters
    collection:
        temporal_scope: {
            start_date: "1880-01-01",
            end_date: "2024-12-31",
            resolution: "monthly"
        }
        spatial_scope: "global"
        quality_threshold: 0.8
        completeness_threshold: 0.9
    
    // Define integration methods
    integration_methods:
        - bayesian_fusion
        - weighted_ensemble
        - consensus_building
        - uncertainty_propagation
    
    // Define validation procedures
    validation:
        - cross_validation
        - bootstrap_resampling
        - sensitivity_analysis
        - bias_correction

// Evidence collection and processing
funxn collect_evidence():
    print("=== Evidence Collection Phase ===")
    
    // Collect experimental data
    item experimental_evidence = collect_experimental_data()
    
    // Collect observational data
    item observational_evidence = collect_observational_data()
    
    // Collect computational evidence
    item computational_evidence = collect_computational_evidence()
    
    // Collect literature evidence
    item literature_evidence = collect_literature_evidence()
    
    return {
        "experimental": experimental_evidence,
        "observational": observational_evidence,
        "computational": computational_evidence,
        "literature": literature_evidence
    }

// Experimental evidence collection
funxn collect_experimental_data():
    evidence ExperimentalTemperatureData:
        sources:
            - weather_stations: GlobalWeatherNetwork
            - ocean_buoys: OceanObservationSystem
            - atmospheric_balloons: UpperAtmosphereNetwork
        
        collection_methods:
            frequency: hourly
            calibration: automated
            quality_control: real_time
        
        processing:
            - remove_outliers(method="modified_z_score", threshold=3.5)
            - fill_missing_values(method="kriging")
            - homogenization_adjustment()
            - uncertainty_estimation()
    
    item temperature_data = ExperimentalTemperatureData.collect(
        time_range=("1880-01-01", "2024-12-31")
    )
    
    // Calculate global temperature anomalies
    item baseline_period = ("1951-01-01", "1980-12-31")
    item anomalies = calculate_anomalies(temperature_data, baseline_period)
    
    // Estimate measurement uncertainty
    item measurement_uncertainty = estimate_measurement_uncertainty(temperature_data)
    
    print("Collected {} temperature records", len(temperature_data))
    print("Average measurement uncertainty: {:.3f}°C", measurement_uncertainty)
    
    return {
        "data": temperature_data,
        "anomalies": anomalies,
        "uncertainty": measurement_uncertainty,
        "quality_score": assess_data_quality(temperature_data)
    }

// Observational evidence collection
funxn collect_observational_data():
    evidence SatelliteObservations:
        sources:
            - nasa_satellites: ["AQUA", "TERRA", "MODIS"]
            - esa_satellites: ["Sentinel-1", "Sentinel-2", "Sentinel-3"]
            - noaa_satellites: ["GOES-16", "GOES-17", "JPSS"]
        
        measurements:
            - surface_temperature
            - cloud_cover
            - ice_extent
            - vegetation_indices
            - atmospheric_composition
        
        processing:
            - atmospheric_correction()
            - geo_rectification()
            - cloud_masking()
            - temporal_compositing()
    
    item satellite_data = SatelliteObservations.collect()
    
    // Process different observation types
    item surface_temp = process_surface_temperature(satellite_data)
    item ice_extent = process_ice_extent(satellite_data)
    item vegetation = process_vegetation_indices(satellite_data)
    
    // Calculate trends
    item temperature_trend = calculate_trend(surface_temp, method="theil_sen")
    item ice_trend = calculate_trend(ice_extent, method="linear_regression")
    
    print("Satellite temperature trend: {:.4f}°C/decade", temperature_trend.slope * 10)
    print("Ice extent trend: {:.2f}% per decade", ice_trend.slope * 10)
    
    return {
        "surface_temperature": surface_temp,
        "ice_extent": ice_extent,
        "vegetation": vegetation,
        "trends": {
            "temperature": temperature_trend,
            "ice": ice_trend
        },
        "spatial_coverage": calculate_spatial_coverage(satellite_data)
    }

// Computational evidence collection
funxn collect_computational_evidence():
    evidence ClimateModelEnsemble:
        models:
            - "CESM2": CommunityEarthSystemModel
            - "GFDL-CM4": GeophysicalFluidDynamicsLab
            - "HadGEM3": UKMetOfficeModel
            - "IPSL-CM6": InstitutPierreSimonLaplace
            - "MPI-ESM": MaxPlanckInstitute
        
        scenarios:
            - "historical": HistoricalRuns
            - "ssp126": LowEmissionsScenario
            - "ssp245": MiddleEmissionsScenario
            - "ssp585": HighEmissionsScenario
        
        ensemble_size: 50  // Multiple runs per model
        
        validation:
            - historical_performance
            - physical_consistency
            - energy_balance_check
    
    item model_ensemble = ClimateModelEnsemble.run_ensemble()
    
    // Calculate ensemble statistics
    item ensemble_mean = calculate_ensemble_mean(model_ensemble)
    item ensemble_spread = calculate_ensemble_spread(model_ensemble)
    item model_agreement = assess_model_agreement(model_ensemble)
    
    // Evaluate model performance
    item performance_metrics = evaluate_model_performance(
        model_ensemble,
        observational_data
    )
    
    print("Ensemble size: {} model runs", len(model_ensemble))
    print("Model agreement score: {:.2f}", model_agreement)
    print("Historical performance: {:.3f}", performance_metrics.historical_skill)
    
    return {
        "ensemble_results": model_ensemble,
        "ensemble_statistics": {
            "mean": ensemble_mean,
            "spread": ensemble_spread,
            "agreement": model_agreement
        },
        "performance": performance_metrics,
        "projections": extract_future_projections(model_ensemble)
    }

// Literature evidence collection
funxn collect_literature_evidence():
    evidence ScientificLiterature:
        databases:
            - "pubmed": PubMedDatabase
            - "web_of_science": WebOfScience
            - "scopus": ScopusDatabase
            - "arxiv": ArXivRepository
        
        search_terms: [
            "climate change temperature trends",
            "global warming evidence",
            "climate sensitivity",
            "attribution studies"
        ]
        
        filters:
            publication_years: (2010, 2024)
            impact_factor: "> 2.0"
            peer_reviewed: true
        
        extraction_methods:
            - abstract_analysis
            - full_text_mining
            - figure_extraction
            - citation_analysis
    
    item literature_results = ScientificLiterature.search_and_extract()
    
    // Perform meta-analysis
    item meta_analysis_results = meta_analysis.conduct_meta_analysis(
        literature_results,
        outcome_variable="temperature_trend",
        effect_size_measure="weighted_mean_difference"
    )
    
    // Assess publication bias
    item publication_bias = meta_analysis.assess_publication_bias(
        literature_results,
        methods=["funnel_plot", "egger_test", "begg_test"]
    )
    
    print("Literature review: {} papers analyzed", len(literature_results))
    print("Meta-analysis effect size: {:.4f}°C/decade", meta_analysis_results.effect_size)
    print("Publication bias detected: {}", publication_bias.significant)
    
    return {
        "papers": literature_results,
        "meta_analysis": meta_analysis_results,
        "publication_bias": publication_bias,
        "consensus_assessment": assess_scientific_consensus(literature_results)
    }

// Evidence integration using propositions
proposition ClimateChangeEvidence:
    motion ObservableWarming("Global temperatures are increasing")
    motion HumanAttribution("Human activities are the primary cause")
    motion FutureProjections("Continued warming is projected")
    motion HighConfidence("Evidence supports high confidence conclusions")
    
    // Evaluate observable warming
    within experimental_evidence:
        given anomalies.trend > 0 and anomalies.significance < 0.001:
            support ObservableWarming with_confidence(0.95)
            print("✓ Significant warming trend in instrumental record")
    
    within observational_evidence:
        given trends.temperature.slope > 0 and trends.temperature.p_value < 0.01:
            support ObservableWarming with_confidence(0.90)
            print("✓ Satellite observations confirm warming")
    
    // Evaluate human attribution
    within computational_evidence:
        given ensemble_statistics.agreement > 0.8:
            support HumanAttribution with_confidence(0.85)
            print("✓ Climate models show human influence")
    
    within literature_evidence:
        given consensus_assessment.agreement > 0.97:
            support HumanAttribution with_confidence(0.99)
            print("✓ Scientific consensus on human causation")
    
    // Evaluate future projections
    within computational_evidence:
        given projections.warming_range_2050 > (1.0, 3.0):
            support FutureProjections with_confidence(0.80)
            print("✓ Consistent future warming projections")
    
    // Assess overall confidence
    item evidence_convergence = assess_evidence_convergence([
        experimental_evidence,
        observational_evidence,
        computational_evidence,
        literature_evidence
    ])
    
    given evidence_convergence > 0.9:
        support HighConfidence with_confidence(0.95)
        print("✓ Multiple lines of evidence converge")

// Advanced evidence integration
funxn integrate_evidence_advanced(all_evidence):
    print("=== Advanced Evidence Integration ===")
    
    // Weight evidence sources by reliability
    item evidence_weights = {
        "experimental": 0.30,
        "observational": 0.25,
        "computational": 0.25,
        "literature": 0.20
    }
    
    // Bayesian evidence integration
    item bayesian_integration = bayesian_evidence_fusion(
        all_evidence,
        evidence_weights,
        prior_distribution="uniform"
    )
    
    // Uncertainty propagation
    item propagated_uncertainty = uncertainty.propagate_uncertainty(
        all_evidence,
        correlation_matrix=estimate_evidence_correlations(all_evidence)
    )
    
    // Consensus building
    item consensus_result = build_evidence_consensus(
        all_evidence,
        consensus_threshold=0.8,
        disagreement_threshold=0.2
    )
    
    // Sensitivity analysis
    item sensitivity_results = perform_sensitivity_analysis(
        all_evidence,
        perturbation_range=0.1
    )
    
    return {
        "bayesian_result": bayesian_integration,
        "uncertainty": propagated_uncertainty,
        "consensus": consensus_result,
        "sensitivity": sensitivity_results
    }

// Uncertainty quantification
funxn quantify_uncertainties(evidence_integration):
    print("=== Uncertainty Quantification ===")
    
    // Aleatory uncertainty (natural variability)
    item aleatory_uncertainty = calculate_aleatory_uncertainty(evidence_integration)
    
    // Epistemic uncertainty (knowledge limitations)
    item epistemic_uncertainty = calculate_epistemic_uncertainty(evidence_integration)
    
    // Model uncertainty
    item model_uncertainty = assess_model_uncertainty(evidence_integration)
    
    // Measurement uncertainty
    item measurement_uncertainty = assess_measurement_uncertainty(evidence_integration)
    
    // Total uncertainty
    item total_uncertainty = combine_uncertainties([
        aleatory_uncertainty,
        epistemic_uncertainty,
        model_uncertainty,
        measurement_uncertainty
    ])
    
    print("Aleatory uncertainty: {:.3f}", aleatory_uncertainty)
    print("Epistemic uncertainty: {:.3f}", epistemic_uncertainty)
    print("Model uncertainty: {:.3f}", model_uncertainty)
    print("Measurement uncertainty: {:.3f}", measurement_uncertainty)
    print("Total uncertainty: {:.3f}", total_uncertainty)
    
    return {
        "aleatory": aleatory_uncertainty,
        "epistemic": epistemic_uncertainty,
        "model": model_uncertainty,
        "measurement": measurement_uncertainty,
        "total": total_uncertainty,
        "confidence_intervals": calculate_confidence_intervals(total_uncertainty)
    }

// Bias detection and correction
funxn detect_and_correct_biases(evidence_integration):
    print("=== Bias Detection and Correction ===")
    
    // Selection bias
    item selection_bias = detect_selection_bias(evidence_integration)
    
    // Confirmation bias
    item confirmation_bias = detect_confirmation_bias(evidence_integration)
    
    // Publication bias
    item publication_bias = detect_publication_bias(evidence_integration)
    
    // Measurement bias
    item measurement_bias = detect_measurement_bias(evidence_integration)
    
    // Apply corrections
    item corrected_evidence = evidence_integration
    
    given selection_bias.detected:
        corrected_evidence = correct_selection_bias(corrected_evidence, selection_bias)
        print("Applied selection bias correction")
    
    given confirmation_bias.detected:
        corrected_evidence = correct_confirmation_bias(corrected_evidence, confirmation_bias)
        print("Applied confirmation bias correction")
    
    given publication_bias.detected:
        corrected_evidence = correct_publication_bias(corrected_evidence, publication_bias)
        print("Applied publication bias correction")
    
    given measurement_bias.detected:
        corrected_evidence = correct_measurement_bias(corrected_evidence, measurement_bias)
        print("Applied measurement bias correction")
    
    return {
        "original_evidence": evidence_integration,
        "corrected_evidence": corrected_evidence,
        "bias_report": {
            "selection": selection_bias,
            "confirmation": confirmation_bias,
            "publication": publication_bias,
            "measurement": measurement_bias
        }
    }

// Generate final assessment
funxn generate_final_assessment(integrated_evidence, uncertainties, bias_correction):
    print("=== Final Evidence Assessment ===")
    
    // Calculate overall confidence score
    item confidence_score = calculate_overall_confidence(
        integrated_evidence,
        uncertainties,
        bias_correction
    )
    
    // Generate summary statistics
    item summary_stats = {
        "effect_size": integrated_evidence.bayesian_result.posterior_mean,
        "confidence_interval": uncertainties.confidence_intervals.ci_95,
        "evidence_strength": assess_evidence_strength(integrated_evidence),
        "convergence_score": integrated_evidence.consensus.convergence_score
    }
    
    // Generate recommendations
    item recommendations = generate_evidence_based_recommendations(
        integrated_evidence,
        confidence_score,
        summary_stats
    )
    
    // Create uncertainty visualization
    item uncertainty_viz = create_uncertainty_visualization(
        summary_stats,
        uncertainties
    )
    
    print("Overall confidence score: {:.2f}/1.0", confidence_score)
    print("Effect size: {:.4f} [{:.4f}, {:.4f}] (95% CI)", 
          summary_stats.effect_size,
          summary_stats.confidence_interval[0],
          summary_stats.confidence_interval[1])
    print("Evidence strength: {}", summary_stats.evidence_strength)
    
    return {
        "confidence_score": confidence_score,
        "summary_statistics": summary_stats,
        "recommendations": recommendations,
        "uncertainty_visualization": uncertainty_viz,
        "evidence_report": generate_comprehensive_report(
            integrated_evidence,
            uncertainties,
            bias_correction
        )
    }

// Main integration workflow
funxn main():
    print("Climate Change Evidence Integration Analysis")
    print("===========================================")
    
    // Step 1: Collect evidence from all sources
    item all_evidence = collect_evidence()
    
    // Step 2: Evaluate propositions
    item proposition_results = ClimateChangeEvidence.evaluate(all_evidence)
    
    // Step 3: Advanced integration
    item integrated_results = integrate_evidence_advanced(all_evidence)
    
    // Step 4: Quantify uncertainties
    item uncertainty_analysis = quantify_uncertainties(integrated_results)
    
    // Step 5: Detect and correct biases
    item bias_analysis = detect_and_correct_biases(integrated_results)
    
    // Step 6: Generate final assessment
    item final_assessment = generate_final_assessment(
        bias_analysis.corrected_evidence,
        uncertainty_analysis,
        bias_analysis
    )
    
    // Step 7: Output results
    print("\n=== Final Conclusions ===")
    for each recommendation in final_assessment.recommendations:
        print("• {}", recommendation)
    
    return {
        "evidence": all_evidence,
        "propositions": proposition_results,
        "integration": integrated_results,
        "uncertainties": uncertainty_analysis,
        "bias_correction": bias_analysis,
        "assessment": final_assessment
    }

// Adaptive learning component
metacognitive AdaptiveLearning:
    track:
        - evidence_quality_changes
        - new_data_integration
        - uncertainty_evolution
        - consensus_shifts
    
    adapt:
        given new_evidence_available():
            update_evidence_weights()
            re_evaluate_propositions()
            recalculate_uncertainties()
        
        given uncertainty_reduced():
            increase_confidence_scores()
            update_recommendations()
        
        given consensus_changed():
            flag_for_expert_review()
            update_integration_methods()
    
    learn:
        - pattern_recognition_improvement
        - bias_detection_enhancement
        - uncertainty_estimation_refinement
        - integration_method_optimization

Key Concepts Demonstrated

1. Multi-Source Evidence Integration

• Experimental Data: Direct measurements with uncertainty quantification
• Observational Data: Satellite and remote sensing observations
• Computational Models: Climate model ensembles with validation
• Literature Evidence: Meta-analysis and consensus assessment

2. Uncertainty Quantification

• Aleatory Uncertainty: Natural variability in the system
• Epistemic Uncertainty: Limitations in knowledge and understanding
• Model Uncertainty: Structural and parameter uncertainties in models
• Measurement Uncertainty: Instrument and observational uncertainties

3. Bias Detection and Correction

• Selection Bias: Non-representative sampling
• Confirmation Bias: Preferential treatment of confirming evidence
• Publication Bias: Underrepresentation of null results
• Measurement Bias: Systematic errors in data collection

4. Adaptive Learning

• Dynamic Evidence Weighting: Adjusting weights based on quality
• Continuous Integration: Incorporating new evidence streams
• Uncertainty Evolution: Tracking how uncertainties change over time
• Consensus Monitoring: Detecting shifts in scientific agreement

Running the Example

1. Save the code as evidence_integration.turb

2. Ensure required modules are available:

   kwasa install statistics temporal meta_analysis uncertainty
   

3. Run the analysis:

   kwasa run evidence_integration.turb
   

Expected Output

Climate Change Evidence Integration Analysis
===========================================

=== Evidence Collection Phase ===
Collected 156,844 temperature records
Average measurement uncertainty: 0.032°C

Satellite temperature trend: 0.0179°C/decade
Ice extent trend: -13.1% per decade

Ensemble size: 50 model runs
Model agreement score: 0.87
Historical performance: 0.892

Literature review: 1,247 papers analyzed
Meta-analysis effect size: 0.0183°C/decade
Publication bias detected: false

✓ Significant warming trend in instrumental record
✓ Satellite observations confirm warming
✓ Climate models show human influence
✓ Scientific consensus on human causation
✓ Consistent future warming projections
✓ Multiple lines of evidence converge

=== Advanced Evidence Integration ===
Bayesian posterior probability: 0.97
Evidence convergence score: 0.94

=== Uncertainty Quantification ===
Aleatory uncertainty: 0.045
Epistemic uncertainty: 0.028
Model uncertainty: 0.035
Measurement uncertainty: 0.032
Total uncertainty: 0.068

=== Bias Detection and Correction ===
Applied selection bias correction

=== Final Evidence Assessment ===
Overall confidence score: 0.92/1.0
Effect size: 0.0181 [0.0165, 0.0197] (95% CI)
Evidence strength: Very Strong

=== Final Conclusions ===
• Human-caused climate change is occurring with very high confidence
• Global temperature increase of 0.18°C per decade since 1980
• Multiple independent lines of evidence support conclusions
• Uncertainty ranges do not affect core conclusions
• Continued monitoring and model improvement recommended

Applications

1. Scientific Research

• Meta-analysis and systematic reviews
• Multi-modal data integration
• Hypothesis testing with multiple evidence types
• Uncertainty quantification in conclusions

2. Policy Decision Making

• Evidence-based policy formulation
• Risk assessment with uncertainty bounds
• Stakeholder communication of scientific findings
• Adaptive management strategies

3. Medical Research

• Clinical trial evidence synthesis
• Diagnostic test evaluation
• Treatment effectiveness assessment
• Drug safety monitoring

4. Environmental Assessment

• Ecosystem health evaluation
• Pollution impact studies
• Conservation effectiveness
• Climate impact assessment

Advanced Features

Real-Time Evidence Updates

stream continuous_evidence_integration():
    for each new_evidence in evidence_stream:
        item updated_integration = update_evidence_base(new_evidence)
        
        given significant_change_detected(updated_integration):
            alert_stakeholders(updated_integration)
            trigger_reassessment()

Machine Learning Enhancement

ml_enhanced evidence_quality_predictor:
    features:
        - data_source_reliability
        - measurement_precision
        - temporal_coverage
        - spatial_resolution
    
    model: random_forest
    validation: cross_validation
    
    predict: evidence_quality_score

This example demonstrates the sophisticated evidence integration capabilities of Kwasa-Kwasa, showing how multiple sources of evidence can be systematically combined to build robust scientific conclusions with quantified uncertainties and bias corrections.