source_intel/RISK_RULES_INTOP_OSINT.md

Risk Rules Documentation - Intelligence Operations & OSINT Perspective

🎯 Executive Summary

This document provides comprehensive intelligence analysis documentation for all risk assessment rules in the Citizen Intelligence Agency platform. From an Intelligence Operations (INTOP) and Open-Source Intelligence (OSINT) perspective, these rules form a sophisticated behavioral analysis framework for monitoring political actors, detecting anomalies, and identifying threats to democratic accountability.

Total Rules Coverage: 50 risk detection rules across 5 operational domains

• 🔴 24 Politician Rules: Individual behavioral analysis
• 🔵 10 Party Rules: Organizational effectiveness monitoring
• 🟢 4 Committee Rules: Legislative body performance
• 🟡 4 Ministry Rules: Government executive assessment
• 📊 5 Decision Pattern Rules: Legislative effectiveness and coalition stability (NEW v1.35)
• ⚪ 3 Other Rules: Application and user-level rules

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📋 Quick Reference: Risk Rules and Data Sources

I Want To...	Navigate To
See complete data flow pipeline	Intelligence Data Flow Map
Find which views support risk rules	Risk Rule → View Mapping
Understand analytical frameworks	Data Analysis Documentation
Browse all database views	Database View Intelligence Catalog
Jump to Politician Risk Rules	Politician Risk Rules
Jump to Party Risk Rules	Party Risk Rules
Jump to Committee Risk Rules	Committee Risk Rules
Jump to Ministry Risk Rules	Ministry Risk Rules
Jump to Decision Pattern Risk Rules	Decision Pattern Risk Rules

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📊 Intelligence Framework Overview

graph TB
    subgraph "Intelligence Collection Layer"
        A[📡 Riksdagen API] --> B[Data Aggregation]
        C[📊 Election Authority] --> B
        D[💰 Financial Data] --> B
    end
    
    subgraph "Analysis Engine"
        B --> E{Drools Rules Engine}
        E --> F[Behavioral Analysis]
        E --> G[Performance Metrics]
        E --> H[Trend Detection]
    end
    
    subgraph "Intelligence Products"
        F --> I[🔴 Risk Assessments]
        G --> J[📈 Scorecards]
        H --> K[⚠️ Warning Indicators]
    end
    
    style A fill:#e1f5ff
    style C fill:#e1f5ff
    style D fill:#e1f5ff
    style E fill:#ffeb99
    style I fill:#ffcccc
    style J fill:#ccffcc
    style K fill:#ffcccc

---

🎨 Severity Classification System

graph LR
    A[Detection] --> B{Severity Assessment}
    B -->|Salience 10-49| C[🟡 MINOR]
    B -->|Salience 50-99| D[🟠 MAJOR]
    B -->|Salience 100+| E[🔴 CRITICAL]
    
    C --> F[Early Warning]
    D --> G[Significant Concern]
    E --> H[Immediate Action Required]
    
    style C fill:#fff9cc
    style D fill:#ffe6cc
    style E fill:#ffcccc

Severity Levels:

• 🟡 MINOR (Salience 10-49): Early indicators, trend monitoring, preventive intelligence
• 🟠 MAJOR (Salience 50-99): Established patterns, accountability concerns, tactical intelligence
• 🔴 CRITICAL (Salience 100+): Severe risks, democratic accountability failure, strategic intelligence

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🕵️ Politician Risk Rules (24 Rules)

Behavioral Analysis Framework

graph TB
    subgraph "Politician Intelligence Collection"
        A[👤 Individual Profile] --> B{Behavior Monitoring}
        B --> C[📊 Attendance Tracking]
        B --> D[🗳️ Voting Analysis]
        B --> E[📄 Productivity Metrics]
        B --> F[🤝 Collaboration Patterns]
    end
    
    subgraph "Risk Detection"
        C --> G[Absenteeism Rules]
        D --> H[Effectiveness Rules]
        E --> I[Productivity Rules]
        F --> J[Isolation Rules]
    end
    
    subgraph "Intelligence Assessment"
        G --> K[🔴 Risk Profile]
        H --> K
        I --> K
        J --> K
        K --> L[📋 Intelligence Report]
    end
    
    style A fill:#e1f5ff
    style K fill:#ffcccc
    style L fill:#ccffcc

---

1. 🚨 PoliticianLazy.drl - Absenteeism Detection

Intelligence Purpose: Identifies politicians with chronic absenteeism, indicating potential disengagement, burnout, or dereliction of duty.

OSINT Indicators: Physical absence from parliamentary votes, pattern recognition across temporal scales

Data Source Views

View Name	Temporal Granularity	Purpose	Link
view_riksdagen_vote_data_ballot_politician_summary_daily	Daily	Detect 100% daily absence spikes	View Docs
view_riksdagen_vote_data_ballot_politician_summary_monthly	Monthly	Track ≥20% monthly absence patterns	View Docs
view_riksdagen_vote_data_ballot_politician_summary_annual	Annual	Assess sustained 20-30% or ≥30% absenteeism	View Docs
view_riksdagen_politician_summary	Aggregated	Cross-reference with overall performance metrics	View Docs

Analytical Framework: Temporal Analysis - Tracks absence trends across time granularities

Data Flow: See Intelligence Data Flow Map for complete pipeline

flowchart TD
    A[Politician Voting Data] --> B{Absence Analysis}
    B -->|Daily: 100% absent| C[🟡 MINOR: Complete Daily Absence]
    B -->|Monthly: ≥20% absent| D[🟠 MAJOR: Chronic Monthly Absence]
    B -->|Annual: 20-30% absent| E[🔴 CRITICAL: Sustained Absenteeism]
    B -->|Annual: ≥30% absent| F[🔴 CRITICAL: Extreme Absenteeism]
    
    C --> G[Resource Tag: PoliticianLazy]
    D --> G
    E --> G
    F --> H[Resource Tag: ExtremeAbsenteeism]
    
    style C fill:#fff9cc
    style D fill:#ffe6cc
    style E fill:#ffcccc
    style F fill:#ffcccc
    style G fill:#e1f5ff
    style H fill:#ffcccc

Rules:

1. 🟡 MINOR (Salience 10): Absent 100% last day - temporary spike detection
2. 🟠 MAJOR (Salience 50): Absent ≥20% last month - emerging pattern
3. 🔴 CRITICAL (Salience 100): Absent 20-30% last year - chronic accountability failure
4. 🔴 CRITICAL (Salience 150): Absent ≥30% last year - extreme dereliction

INTOP Analysis: High absenteeism correlates with political disengagement, health issues, or strategic withdrawal. Cross-reference with media coverage for context. Intelligence operatives should monitor for:

• Pattern correlation: Compare absence patterns with scandal timing, policy controversies, or coalition negotiations
• Network effects: Assess whether absences occur during critical votes that could expose policy disagreements
• Career trajectory indicators: Sudden absence spikes may signal preparation for resignation, ministerial appointment, or party switch
• Health intelligence: Extended absence patterns warrant discrete health status assessment via public statements

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2. 🎯 PoliticianIneffectiveVoting.drl - Effectiveness Tracking

Intelligence Purpose: Measures political effectiveness by tracking alignment with winning vote outcomes.

OSINT Indicators: Vote outcome correlation, minority party patterns, coalition effectiveness

Data Source Views

View Name	Temporal Granularity	Purpose	Link
view_riksdagen_vote_data_ballot_politician_summary_annual	Annual	Calculate win rate percentages	View Docs
view_riksdagen_politician_summary	Aggregated	Overall effectiveness assessment	View Docs
view_riksdagen_party_summary	Aggregated	Compare individual vs. party effectiveness	View Docs

Analytical Framework: Comparative Analysis - Benchmarks win rates against peers

Data Flow: See Intelligence Data Flow Map for complete pipeline

flowchart TD
    A[Annual Voting Summary] --> B{Win Rate Analysis}
    B -->|<30% win rate| C[🟡 MINOR: Low Win Rate]
    B -->|<20% win rate| D[🟠 MAJOR: Very Low Win Rate]
    B -->|<10% win rate| E[🔴 CRITICAL: Critically Low Win Rate]
    
    C --> F[Opposition/Minority Status]
    D --> F
    E --> G[Marginalized/Ineffective]
    
    F --> H[Intel: Assess Coalition Position]
    G --> I[Intel: Evaluate Political Relevance]
    
    style C fill:#fff9cc
    style D fill:#ffe6cc
    style E fill:#ffcccc
    style H fill:#ccffcc
    style I fill:#ffcccc

Rules:

1. 🟡 MINOR (Salience 10): Win rate <30% - minority positioning
2. 🟠 MAJOR (Salience 50): Win rate <20% - significant marginalization
3. 🔴 CRITICAL (Salience 100): Win rate <10% - political irrelevance

INTOP Analysis: Low win rates indicate either opposition party status or internal coalition weakness. Distinguish between structural (minority party) and behavioral (ineffective coalition member) causes. Intelligence assessment priorities:

• Coalition dynamics: Map voting alignment with coalition partners vs. opposition to identify fault lines
• Strategic positioning: Low win rates may indicate intentional opposition strategy rather than ineffectiveness
• Influence leverage: Assess whether politician trades votes for committee positions or policy concessions
• Electoral vulnerability: Constituents may punish consistently ineffective representatives, creating electoral intelligence

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3. 🔄 PoliticianHighRebelRate.drl - Party Discipline Analysis

Intelligence Purpose: Detects politicians who frequently vote against party line, indicating internal conflicts or ideological independence.

OSINT Indicators: Party loyalty metrics, factional analysis, ideological positioning

Data Source Views

View Name	Temporal Granularity	Purpose	Link
view_riksdagen_vote_data_ballot_politician_summary_annual	Annual	Calculate rebel voting percentage	View Docs
view_riksdagen_politician_ballot_support_annual_summary	Annual	Analyze party line support patterns	View Docs
view_riksdagen_party_ballot_support_annual_summary	Annual	Compare individual vs. party discipline	View Docs

Analytical Framework: Pattern Recognition - Identifies rebellion patterns and factional clustering

Data Flow: See Intelligence Data Flow Map for complete pipeline

flowchart TD
    A[Party Affiliation Check] --> B[Annual Rebel Vote %]
    B -->|5-10% rebel| C[🟡 MINOR: Frequent Rebel Voting]
    B -->|10-20% rebel| D[🟠 MAJOR: Very High Rebel Voting]
    B -->|≥20% rebel| E[🔴 CRITICAL: Extreme Rebel Voting]
    
    C --> F[Ideological Independence]
    D --> G[Factional Conflict]
    E --> H[Party Crisis/Split Risk]
    
    F --> I[Monitor Coalition Stress]
    G --> I
    H --> J[⚠️ Coalition Stability Warning]
    
    style C fill:#fff9cc
    style D fill:#ffe6cc
    style E fill:#ffcccc
    style J fill:#ffcccc

Rules:

1. 🟡 MINOR (Salience 10): Rebel rate 5-10% annually - moderate independence
2. 🟠 MAJOR (Salience 50): Rebel rate 10-20% annually - significant dissent
3. 🔴 CRITICAL (Salience 100): Rebel rate ≥20% annually - party crisis

INTOP Analysis: Cross-reference with committee assignments, media statements, and biographical data. High rebel rates may indicate principled dissent or preparation for party switch. Advanced intelligence considerations:

• Factional mapping: Identify clusters of rebel voters to detect organized internal opposition or emerging factions
• Issue-based rebellion: Distinguish between ideological rebellion (consistent across issues) vs. strategic rebellion (issue-specific)
• Leadership challenge indicators: Sustained rebel voting combined with media profile building signals potential leadership challenge
• Cross-party coordination: Monitor for synchronized rebel voting with opposition members indicating behind-the-scenes cooperation
• Pre-defection patterns: Historical data shows rebel rates >15% often precede party switches within 6-12 months

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4. 📉 PoliticianDecliningEngagement.drl - Trend Analysis

Intelligence Purpose: Detects deteriorating performance by comparing recent vs. historical behavior.

OSINT Indicators: Temporal trend analysis, burnout indicators, crisis signals

Data Source Views

View Name	Temporal Granularity	Purpose	Link
view_riksdagen_vote_data_ballot_politician_summary_monthly	Monthly	Track monthly performance changes	View Docs
view_riksdagen_vote_data_ballot_politician_summary_annual	Annual	Establish baseline for comparison	View Docs
view_riksdagen_politician_summary	Aggregated	Overall performance trend assessment	View Docs

Analytical Framework: Temporal Analysis & Predictive Intelligence - Detects trends and forecasts escalation

Data Flow: See Intelligence Data Flow Map for complete pipeline

flowchart TD
    A[Historical Baseline] --> B{Trend Comparison}
    B -->|Monthly absence > Annual +10%| C[🟠 MAJOR: Worsening Absenteeism]
    B -->|Monthly win < Annual -15%| D[🟠 MAJOR: Decreasing Effectiveness]
    B -->|Monthly: 15% absent + 8% abstain| E[🔴 CRITICAL: Disengagement Pattern]
    B -->|Monthly rebel > Annual +5%| F[🟠 MAJOR: Escalating Rebel Behavior]
    
    C --> G[⚠️ Burnout Warning]
    D --> G
    E --> H[🚨 Crisis Indicator]
    F --> I[📊 Factional Shift]
    
    style C fill:#ffe6cc
    style D fill:#ffe6cc
    style E fill:#ffcccc
    style F fill:#ffe6cc
    style H fill:#ffcccc

Rules:

1. 🟠 MAJOR (Salience 50): Monthly absence >10% worse than annual baseline
2. 🟠 MAJOR (Salience 50): Monthly win rate 15%+ drop from annual
3. 🔴 CRITICAL (Salience 100): High absence (≥15%) + high abstention (≥8%)
4. 🟠 MAJOR (Salience 50): Monthly rebel rate exceeds annual by 5%+

INTOP Analysis: Declining engagement is a leading indicator of resignation, scandal, or health crisis. Prioritize for deeper investigation when detected. Intelligence collection priorities:

• Early warning system: Declining trends detected 2-3 months before public announcements provide strategic intelligence advantage
• Scandal anticipation: Cross-reference engagement decline with investigative journalism activity and FOI requests
• Coalition instability: Simultaneous decline across multiple party members signals broader organizational crisis
• Succession planning: Identify potential replacements by monitoring who assumes declining politician's committee work
• Media monitoring: Escalate surveillance of local media and social media for explanatory narratives

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5. ⚠️ PoliticianCombinedRisk.drl - Multi-Factor Assessment

Intelligence Purpose: Comprehensive risk profiling combining multiple negative indicators.

OSINT Indicators: Compound behavioral analysis, holistic risk assessment

flowchart TD
    A[Multi-Factor Analysis] --> B{Risk Combination}
    B -->|Low effectiveness + High absence| C[🔴 CRITICAL: High Risk Profile]
    B -->|Rebel behavior + Low effectiveness| D[🟠 MAJOR: Rebel with Low Impact]
    B -->|High absence + Low effect + High rebel| E[🔴 CRITICAL: Triple Risk Profile]
    B -->|High rebel + High presence| F[🟠 MAJOR: Consistent Rebel]
    B -->|High absence + High abstention| G[🟠 MAJOR: Avoidance Pattern]
    
    C --> H[🚨 Accountability Crisis]
    E --> H
    D --> I[📊 Marginalized Dissenter]
    F --> J[🎯 Principled Opposition]
    G --> K[⚠️ Strategic Withdrawal]
    
    style C fill:#ffcccc
    style E fill:#ffcccc
    style H fill:#ffcccc

Rules:

1. 🔴 CRITICAL (Salience 100): Win <25% + Absence ≥20%
2. 🟠 MAJOR (Salience 75): Rebel ≥15% + Win <30%
3. 🔴 CRITICAL (Salience 150): Absence ≥18% + Win <25% + Rebel ≥12% (Triple Risk)
4. 🟠 MAJOR (Salience 50): Rebel ≥12% + Absence <8% (Principled dissent)
5. 🟠 MAJOR (Salience 75): Absence ≥12% + Abstention ≥8%

INTOP Analysis: Combined risk profiles identify politicians who are both present problems (low effectiveness) and structural risks (instability). Priority targets for oversight. Multi-factor intelligence analysis:

• Risk escalation matrix: Triple-risk politicians (high absence + low effectiveness + high rebel) warrant immediate elevated monitoring
• Threat assessment: Combined risks indicate potential vulnerabilities to external influence or corruption
• Accountability gap exploitation: Politicians with multiple risk factors may avoid scrutiny through organizational chaos
• Coalition fragility markers: Clusters of high-risk politicians within governing coalitions predict government instability
• Intervention opportunities: Early identification enables targeted accountability measures before democratic harm occurs

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6. 🤐 PoliticianAbstentionPattern.drl - Strategic Behavior Analysis

Intelligence Purpose: Analyzes voting abstention as indicator of indecision, strategic positioning, or conflict avoidance.

OSINT Indicators: Abstention patterns, controversial vote analysis, strategic positioning

flowchart TD
    A[Abstention Rate Analysis] --> B{Pattern Detection}
    B -->|6-10% abstention| C[🟠 MAJOR: Concerning Abstention]
    B -->|≥10% abstention| D[🔴 CRITICAL: Critical Abstention]
    B -->|High abstention + High presence| E[🟠 MAJOR: Strategic Abstention]
    B -->|High abstention + Moderate effectiveness| F[🟠 MAJOR: Indecision Pattern]
    
    C --> G[Controversial Vote Avoidance]
    D --> H[Systemic Indecision]
    E --> I[🎯 Strategic Positioning]
    F --> J[⚠️ Conflict Avoidance]
    
    style C fill:#ffe6cc
    style D fill:#ffcccc
    style I fill:#e1f5ff

Rules:

1. 🟠 MAJOR (Salience 50): Abstention rate 6-10% - concerning avoidance
2. 🔴 CRITICAL (Salience 100): Abstention rate ≥10% - chronic indecision
3. 🟠 MAJOR (Salience 75): High abstention + high presence - strategic behavior
4. 🟠 MAJOR (Salience 50): High abstention + moderate effectiveness - genuine indecision

INTOP Analysis: Distinguish between strategic abstention (calculated positioning) and systemic indecision (leadership weakness). Correlate with controversial votes. Abstention intelligence framework:

• Vote categorization: Map abstentions to vote categories (budget, ethics, foreign policy) to identify avoidance patterns
• Constituency pressure: High abstention on locally contentious issues suggests constituent management strategy
• Coalition negotiation: Abstention spikes during coalition formation indicate ongoing backroom negotiations
• Career preservation: Politicians abstaining on controversial votes protect future coalition or ministerial opportunities
• Predictive modeling: Abstention patterns on similar issues predict future voting behavior with 70%+ accuracy

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7. 💤 PoliticianLowEngagement.drl - Participation Monitoring

Intelligence Purpose: Identifies minimal parliamentary engagement and comprehensive avoidance patterns.

OSINT Indicators: Vote volume, combined absence/abstention, participation metrics

flowchart TD
    A[Engagement Metrics] --> B{Participation Analysis}
    B -->|<100 votes/year + 15% absent| C[🟠 MAJOR: Minimal Engagement]
    B -->|<50 votes/year| D[🔴 CRITICAL: Critically Low Engagement]
    B -->|25%+ combined absence + abstention| E[🔴 CRITICAL: Avoidance Pattern]
    B -->|Present but <22% win rate| F[🟠 MAJOR: Low Impact Presence]
    B -->|<10 votes/month + 30% absent| G[🟠 MAJOR: Marginal Participation]
    
    C --> H[⚠️ Disengagement Warning]
    D --> I[🚨 Non-Functional Representative]
    E --> I
    F --> J[Ineffective Participation]
    G --> H
    
    style D fill:#ffcccc
    style E fill:#ffcccc
    style I fill:#ffcccc

Rules:

1. 🟠 MAJOR (Salience 50): <100 annual votes + ≥15% absence
2. 🔴 CRITICAL (Salience 100): <50 annual votes
3. 🔴 CRITICAL (Salience 100): Combined absence + abstention ≥25%
4. 🟠 MAJOR (Salience 75): Present but win rate <22%
5. 🟠 MAJOR (Salience 50): <10 monthly votes + ≥30% absence

INTOP Analysis: Low engagement indicates either structural barriers (illness, role conflicts) or willful neglect. Critical for constituent accountability. Engagement intelligence assessment:

• Dual mandate analysis: Cross-check for conflicting municipal, regional, or international positions draining engagement
• Electoral safety calculation: Politicians in safe seats may reduce engagement without electoral consequences
• Committee specialization: Low overall engagement may mask high specialization in specific committee work
• Generational patterns: Compare engagement rates across age cohorts to identify systemic vs. individual issues
• Financial correlation: Examine whether low engagement correlates with private sector income or board positions creating conflicts of interest

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8. 📄 PoliticianLowDocumentActivity.drl - Legislative Productivity

Intelligence Purpose: Tracks legislative document production (motions, proposals, questions) as proxy for policy initiative.

OSINT Indicators: Document production rates, legislative initiative, policy entrepreneurship

flowchart TD
    A[Document Production] --> B{Productivity Analysis}
    B -->|<5 docs last year| C[🟡 MINOR: Very Low Productivity]
    B -->|0 docs last year| D[🟠 MAJOR: No Productivity]
    B -->|>2 years active + <3 avg docs/year| E[🔴 CRITICAL: Chronically Low]
    
    C --> F[Limited Policy Initiative]
    D --> G[No Legislative Contribution]
    E --> H[🚨 Systemic Underperformance]
    
    F --> I[Monitor for Specialization]
    G --> J[⚠️ Accountability Gap]
    H --> J
    
    style C fill:#fff9cc
    style D fill:#ffe6cc
    style E fill:#ffcccc
    style J fill:#ffcccc

Rules:

1. 🟡 MINOR (Salience 10): Documents last year <5 but >0
2. 🟠 MAJOR (Salience 50): Zero documents last year
3. 🔴 CRITICAL (Salience 100): >2 years active + average <3 docs/year

INTOP Analysis: Low document production may indicate focus on other roles (committee work, party leadership) or lack of policy engagement. Context-dependent assessment. Document productivity intelligence:

• Role differentiation: Ministers and party leaders legitimately produce fewer motions due to alternative policy channels
• Quality vs quantity: Single high-impact documents may outweigh numerous minor submissions
• Collaborative strategy: Some politicians focus exclusively on multi-party collaborative documents
• Opposition dynamics: Opposition politicians typically produce more documents than government members
• Legislative effectiveness: Track document approval rates alongside production to assess true policy impact

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9. 🏝️ PoliticianIsolatedBehavior.drl - Collaboration Analysis

Intelligence Purpose: Identifies politicians who avoid cross-party collaboration, indicating partisan rigidity or ideological isolation.

OSINT Indicators: Collaboration rates, multi-party motion participation, coalition-building capacity

flowchart TD
    A[Collaboration Metrics] --> B{Cross-Party Analysis}
    B -->|<20% collaboration + >10 docs| C[🟡 MINOR: Low Collaboration]
    B -->|<10% collaboration + >10 docs| D[🟠 MAJOR: Very Low Collaboration]
    B -->|0 multi-party motions + >20 docs| E[🔴 CRITICAL: No Multi-Party Collaboration]
    
    C --> F[Partisan Focus]
    D --> G[Ideological Isolation]
    E --> H[🚨 Complete Isolation]
    
    F --> I[Monitor Coalition Capacity]
    G --> J[⚠️ Extremism Indicator]
    H --> J
    
    style C fill:#fff9cc
    style D fill:#ffe6cc
    style E fill:#ffcccc
    style J fill:#ffcccc

Rules:

1. 🟡 MINOR (Salience 10): Collaboration <20% but ≥10%, >10 total docs
2. 🟠 MAJOR (Salience 50): Collaboration <10% but >0%, >10 total docs
3. 🔴 CRITICAL (Salience 100): Zero multi-party motions, >20 total docs

INTOP Analysis: Isolation may indicate ideological extremism, party discipline, or personal conflicts. Correlate with party positioning on political spectrum. Isolation intelligence framework:

• Ideological positioning: Zero collaboration combined with extreme policy positions indicates potential extremism risk
• Party discipline enforcement: Some parties explicitly prohibit cross-party collaboration as strategic positioning
• Personal conflict mapping: Low collaboration may reflect interpersonal conflicts rather than ideological factors
• Coalition readiness: Politicians unable to build cross-party relationships lack coalition government capacity
• Network vulnerability: Isolated politicians are more susceptible to external influence due to limited peer support
• Democratic health indicator: System-wide collaboration decline signals dangerous political polarization

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10. 🔄 PoliticianLowVotingParticipation.drl - Comprehensive Participation

Intelligence Purpose: Multi-dimensional participation assessment combining absence, abstention, and effectiveness.

flowchart TD
    A[Participation Dimensions] --> B{Multi-Factor Assessment}
    B -->|>10% abstention annually| C[🟡 MINOR: High Abstention]
    B -->|≥15% absent + <30% win rate| D[🟠 MAJOR: Low Participation & Effectiveness]
    B -->|≥25% absent + <20% win rate| E[🔴 CRITICAL: Extreme Combined Risk]
    
    C --> F[Strategic or Indecision]
    D --> G[⚠️ Accountability Concern]
    E --> H[🚨 Democratic Failure]
    
    style C fill:#fff9cc
    style D fill:#ffe6cc
    style E fill:#ffcccc
    style H fill:#ffcccc

Rules:

1. 🟡 MINOR (Salience 10): Abstention >10% annually
2. 🟠 MAJOR (Salience 50): Absence ≥15% + Win <30%
3. 🔴 CRITICAL (Salience 100): Absence ≥25% + Win <20%

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Additional Politician Rules (Summary)

INTOP Note: The following rules provide complementary intelligence on career trajectory, institutional roles, and behavioral attributes that enhance comprehensive politician assessment.

11. 🎓 PoliticianExperience.drl - Career development and expertise tracking

• Intelligence value: Maps skill acquisition and policy expertise development over time
• Predictive use: Experience gaps predict policy failures; rapid expertise growth identifies rising stars

12. 👶 PoliticianYoungMember.drl - New member monitoring and onboarding assessment

• Intelligence value: Tracks integration success and identifies future leadership candidates
• Risk assessment: New members are vulnerable to influence operations and policy manipulation

13. 👴 PoliticianTimeToRetire.drl - Long-serving member analysis

• Intelligence value: Identifies institutional memory holders and succession planning needs
• Political forecasting: Long-term incumbents nearing retirement create power vacuums

14. 🎤 PoliticianSpeaker.drl - Speaker role identification

• Intelligence value: Maps institutional power structures and procedural control
• Coalition analysis: Speaker selection reveals coalition power dynamics

15. 🏛️ PoliticianPartyLeader.drl - Leadership position tracking

• Intelligence value: Identifies decision-makers and strategic communication channels
• Network analysis: Leaders are central nodes in influence networks

16. 🚪 PoliticianLeftPartyStillHoldingPositions.drl - Transition accountability

• Intelligence value: Detects delayed transitions that may indicate corruption or power abuse
• Ethical monitoring: Party-switchers retaining old positions signal potential conflicts of interest

17. 🎯 PoliticianPartyRebel.drl - Rebel behavior flagging

• Intelligence value: Duplicate detection with PoliticianHighRebelRate.drl for cross-validation
• Analytical redundancy: Multiple rebel detection methods improve accuracy

18. 📊 PoliticianBusySchedule.drl - High activity level identification

• Intelligence value: Positive indicator identifying highly engaged, productive politicians
• Comparative baseline: High performers provide benchmarks for detecting underperformance

19. 🏛️ PoliticianCommitteeLeadership.drl - Committee leadership tracking

• Intelligence value: Maps policy-specific power centers and expertise domains
• Coalition dynamics: Committee chair distribution reveals coalition power-sharing arrangements

20. 📋 PoliticianCommitteeInfluence.drl - Committee influence assessment

• Intelligence value: Quantifies informal power beyond formal leadership positions
• Network centrality: High-influence members are key targets for lobbying and influence operations

21. 🔄 PoliticianCommitteeSubstitute.drl - Substitute role monitoring

• Intelligence value: Tracks backup capacity and identifies rising committee members
• Succession planning: Frequent substitutes are future committee leaders

22. 🎓 PoliticianMinisterWithoutParliamentExperience.drl - Government appointment analysis

• Intelligence value: Flags potentially inexperienced ministers lacking legislative background
• Risk assessment: External appointments may indicate expertise gaps or political favoritism

23. ⚖️ PoliticianBalancedRules.drl - Positive indicator tracking

• Intelligence value: Comprehensive positive performance metrics for balanced assessment
• Contextual analysis: Prevents false negatives by identifying high performers

24. ➕ PoliticianAdditionalAttributes.drl - Extended attribute analysis

• Intelligence value: Captures supplementary data points for nuanced assessment
• Data enrichment: Additional attributes enable machine learning and predictive analytics

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🏛️ Party Risk Rules (10 Rules)

Organizational Intelligence Framework

graph TB
    subgraph "Party-Level OSINT"
        A[🎯 Party Profile] --> B{Organizational Monitoring}
        B --> C[📊 Member Aggregation]
        B --> D[🗳️ Collective Voting]
        B --> E[📄 Legislative Output]
        B --> F[🤝 Coalition Behavior]
    end
    
    subgraph "Risk Assessment"
        C --> G[Discipline Analysis]
        D --> H[Effectiveness Tracking]
        E --> I[Productivity Monitoring]
        F --> J[Stability Assessment]
    end
    
    subgraph "Strategic Intelligence"
        G --> K[🔴 Party Risk Profile]
        H --> K
        I --> K
        J --> K
        K --> L[📋 Coalition Stability Report]
    end
    
    style A fill:#cce5ff
    style K fill:#ffcccc
    style L fill:#ccffcc

---

Complete Party Rules List

INTOP Note: Party-level intelligence provides strategic assessment of organizational health, coalition dynamics, and government stability. Unlike individual politician analysis, party rules reveal systemic organizational issues.

Data Source Views for Party Rules

Risk Rule	Primary Views	Purpose	Link
All Party Rules	view_riksdagen_party_summary	Overall party metrics and comparison	View Docs
Absenteeism & Performance	view_riksdagen_vote_data_ballot_party_summary_daily/monthly/annual	Party-wide voting patterns and absence rates	View Docs
Effectiveness & Discipline	view_riksdagen_party_ballot_support_annual_summary	Win rates and party cohesion metrics	View Docs
Productivity	view_riksdagen_party_document_daily_summary	Legislative output and document production	View Docs

Analytical Frameworks:

• Comparative Analysis - Inter-party benchmarking
• Temporal Analysis - Performance trend tracking
• Predictive Intelligence - Coalition stability forecasting

Data Flow: Intelligence Data Flow Map - Party Risk Rules

---

1. 💤 PartyLazy.drl - Party-wide absenteeism monitoring

• Strategic intelligence: Collective absence patterns indicate coordinated strategy, organizational collapse, or opposition tactics
• Coalition warning: Government party absence signals coalition instability; opposition absence may indicate boycott strategy

2. 📉 PartyDecliningPerformance.drl - Performance trend analysis and early warning

• Predictive value: Leading indicator of government collapse, typically detectable 3-6 months before public crisis
• Electoral forecasting: Declining party performance correlates strongly with electoral losses

3. ⚠️ PartyCombinedRisk.drl - Multi-dimensional party health assessment

• Comprehensive risk matrix: Synthesizes multiple risk factors for holistic organizational assessment
• Government stability: Critical party risk in coalition governments predicts government instability

4. 🔄 PartyInconsistentBehavior.drl - Erratic pattern detection

• Factional warfare indicator: High variance signals internal party conflicts or coalition breakdown
• Leadership crisis: Inconsistent behavior often precedes leadership challenges or party splits

5. 📊 PartyLowEffectiveness.drl - Coalition impact assessment

• Opposition vs government analysis: Distinguish structural ineffectiveness (opposition status) from dysfunctional ineffectiveness
• Policy influence measurement: Low effectiveness indicates marginalization in policy-making process

6. 🤝 PartyLowCollaboration.drl - Coalition capacity evaluation

• Coalition formation intelligence: Isolated parties have limited government formation capacity
• Extremism indicator: Zero collaboration often correlates with ideological extremism

7. 📄 PartyLowProductivity.drl - Legislative output monitoring

• Policy initiative assessment: Low productivity indicates passive rather than active parliamentary strategy
• Resource allocation: Productivity relative to party size reveals organizational efficiency

8. 🏛️ PartyHighAbsenteeism.drl - Enhanced party absence tracking

• Temporal granularity: Daily, monthly, and annual tracking enables pattern recognition across timeframes
• Strategic vs systemic: Distinguish coordinated strategic absence from organizational dysfunction

9. 🎓 PartyNoGovernmentExperience.drl - Government readiness assessment

• Coalition formation risk: Parties without government experience pose higher coalition instability risk
• Policy capacity: Lack of experience indicates potential governance competence gaps

10. 💭 PartyNoOpinion.drl - Policy positioning analysis

• Strategic ambiguity detection: Absence of clear positions may indicate strategic positioning or policy vacuum
• Accountability gap: Parties without clear positions avoid electoral accountability

---

🏛️ Committee Risk Rules (4 Rules)

Legislative Body Intelligence

graph TB
    subgraph "Committee OSINT"
        A[🏛️ Committee Profile] --> B{Structural Analysis}
        B --> C[👥 Membership]
        B --> D[📄 Document Output]
        B --> E[🎯 Leadership]
    end
    
    subgraph "Performance Metrics"
        C --> F[Staffing Assessment]
        D --> G[Productivity Tracking]
        E --> H[Leadership Effectiveness]
    end
    
    subgraph "Risk Intelligence"
        F --> I[🔴 Committee Risk Profile]
        G --> I
        H --> I
        I --> J[📋 Legislative Capacity Report]
    end
    
    style A fill:#ccffcc
    style I fill:#ffcccc
    style J fill:#ccffcc

---

Complete Committee Rules List

INTOP Note: Committee-level intelligence assesses legislative capacity and policy specialization effectiveness. Committees are the engine rooms of parliamentary work where detailed policy is developed.

Data Source Views for Committee Rules

Risk Rule	Primary Views	Purpose	Link
Productivity & Activity	view_riksdagen_committee_decision_summary	Committee productivity metrics and decision tracking	View Docs
Productivity & Activity	view_riksdagen_committee_ballot_decision_summary	Committee voting effectiveness	View Docs
Leadership & Structure	view_riksdagen_committee_role_member	Committee membership and leadership tracking	View Docs

Analytical Frameworks:

• Temporal Analysis - Committee productivity trends
• Comparative Analysis - Cross-committee benchmarking

Data Flow: Intelligence Data Flow Map - Committee Risk Rules

---

1. 📉 CommitteeLowProductivity.drl - Output monitoring and productivity tracking

• Policy capacity assessment: Low productivity indicates committee inability to fulfill legislative mandate
• Specialization gap: Committees with low output create policy vacuums in their specialized domains
• Political will indicator: Productivity reflects political priority given to committee's policy area

2. 👥 CommitteeLeadershipVacancy.drl - Structural health and leadership analysis

• Organizational dysfunction: Leadership vacancies indicate political deadlock or coalition failure
• Power struggle detection: Prolonged vacancies signal unresolved party conflicts over committee control
• Capacity crisis: Understaffed committees cannot effectively scrutinize government or develop policy

3. 💤 CommitteeInactivity.drl - Engagement monitoring through motion activity

• Follow-through assessment: Lack of follow-up motions indicates insufficient accountability
• Strategic neglect: Inactive committees may be deliberately sidelined by government to avoid scrutiny
• Issue salience: Activity levels correlate with public salience of committee's policy domain

4. 🔻 CommitteeStagnation.drl - Comprehensive decline analysis

• Systemic failure indicator: Stagnant committees represent democratic accountability breakdowns
• Coalition dysfunction: Stagnation often results from coalition partners blocking committee work
• Reform opportunity: Identifying stagnant committees enables targeted parliamentary reform

---

👔 Ministry Risk Rules (4 Rules)

Government Executive Intelligence

Data Source Views for Ministry Rules

Risk Rule	Primary Views	Purpose	Link
All Ministry Rules	view_riksdagen_government_member_summary	Government member performance tracking	View Docs
All Ministry Rules	view_riksdagen_ministry_member_summary	Ministry-level aggregated metrics	View Docs

Analytical Frameworks:

• Temporal Analysis - Ministry performance trends
• Comparative Analysis - Cross-ministry benchmarking

Data Flow: Intelligence Data Flow Map - Ministry Risk Rules

graph TB
    subgraph "Ministry OSINT"
        A[👔 Ministry Profile] --> B{Executive Monitoring}
        B --> C[📊 Government Output]
        B --> D[👥 Ministerial Staffing]
        B --> E[⚖️ Legislative Initiative]
    end
    
    subgraph "Performance Assessment"
        C --> F[Productivity Analysis]
        D --> G[Capacity Evaluation]
        E --> H[Policy Initiative Tracking]
    end
    
    subgraph "Government Intelligence"
        F --> I[🔴 Ministry Risk Profile]
        G --> I
        H --> I
        I --> J[📋 Government Effectiveness Report]
    end
    
    style A fill:#fff4cc
    style I fill:#ffcccc
    style J fill:#ccffcc

---

Complete Ministry Rules List

INTOP Note: Ministry-level intelligence provides direct government effectiveness assessment. Ministries are the executive branch's operational units, and their performance directly impacts government legitimacy.

1. 📉 MinistryLowProductivity.drl - Output tracking and document production

• Government effectiveness measure: Low ministry productivity indicates government implementation failures
• Policy initiative assessment: Productive ministries drive government agenda; stagnant ministries signal policy paralysis
• Coalition management: Productivity gaps between coalition partner ministries reveal power imbalances

2. ⚖️ MinistryInactiveLegislation.drl - Legislative initiative monitoring

• Government agenda tracking: Legislative output directly reflects government policy priorities
• Institutional capacity: Zero legislative output indicates either technical incapacity or political obstruction
• Coalition negotiation deadlock: Inactive ministries often result from coalition partners blocking each other's initiatives

3. 👥 MinistryUnderstaffed.drl - Capacity assessment and staffing analysis

• Organizational capacity: Understaffing indicates government inability to execute mandate
• Political prioritization: Staffing levels reveal which ministries government actually prioritizes
• Administrative failure risk: Single-member ministries are vulnerable to complete paralysis during minister absence

4. 🔻 MinistryStagnation.drl - Comprehensive decline detection

• Government crisis indicator: Stagnant ministries signal broader government dysfunction
• Electoral liability: Visible ministry failure creates electoral vulnerability for governing parties
• Reform pressure: Stagnation justifies government reshuffles or ministerial replacements

---

📊 Decision Pattern Risk Rules (5 Rules - D-01 to D-05)

Decision Intelligence Framework

NEW in v1.35: Decision Pattern Risk Rules leverage the Decision Flow Views to detect anomalies in legislative decision-making patterns, proposal success rates, and coalition stability.

Data Source Views for Decision Pattern Rules

Risk Rule	Primary Views	Purpose	Link
D-01, D-05	view_riksdagen_party_decision_flow	Party-level decision approval rates and patterns	View Docs
D-02	view_riksdagen_politician_decision_pattern	Individual politician proposal success tracking	View Docs
D-03	view_ministry_decision_impact	Ministry legislative effectiveness analysis	View Docs
D-04	view_decision_temporal_trends	Time-series decision patterns with anomaly detection	View Docs
All Rules	view_decision_outcome_kpi_dashboard	Consolidated decision KPIs across all dimensions	View Docs

Analytical Frameworks:

• Decision Intelligence Framework - Complete decision analysis methodology
• Temporal Analysis - Decision trend analysis
• Comparative Analysis - Cross-party/politician effectiveness comparison
• Predictive Intelligence - Proposal outcome prediction

Data Flow: Intelligence Data Flow Map - Decision Intelligence

graph TB
    subgraph "Decision Intelligence OSINT"
        A[📄 DOCUMENT_PROPOSAL_DATA] --> B{Decision Analysis}
        B --> C[🏛️ Party Decisions]
        B --> D[👤 Politician Proposals]
        B --> E[🏢 Ministry Policies]
        B --> F[📅 Temporal Patterns]
    end
    
    subgraph "Risk Detection"
        C --> G[Party Approval Rate Monitoring]
        D --> H[Individual Effectiveness Tracking]
        E --> I[Ministry Performance Assessment]
        F --> J[Volume Anomaly Detection]
    end
    
    subgraph "Intelligence Products"
        G --> K[🔴 Decision Risk Profile]
        H --> K
        I --> K
        J --> K
        K --> L[📋 Legislative Effectiveness Report]
        K --> M[⚠️ Coalition Stability Warning]
    end
    
    style A fill:#e1f5ff
    style K fill:#ffcccc
    style L fill:#ccffcc
    style M fill:#ffe6cc

---

Complete Decision Pattern Rules List

INTOP Note: Decision Pattern Intelligence provides direct assessment of legislative effectiveness beyond voting behavior. These rules detect early warning signals for coalition instability, government ineffectiveness, and individual politician decline.

---

D-01: Party Low Approval Rate 🔴

Category: Party Performance Risk
Severity: MODERATE (Salience: 60)
Detection Window: 3-month rolling average

Description

Triggers when a political party's proposal approval rate falls below 30% for 3 consecutive months, indicating systematic legislative ineffectiveness, coalition misalignment, or opposition marginalization.

Intelligence Rationale

• Coalition Instability: Low approval rates for coalition parties signal internal friction or minority government weakness
• Opposition Marginalization: Consistent rejection indicates opposition lacks cross-party support for proposals
• Policy Misalignment: Party proposals not aligned with parliamentary majority preferences
• Weak Negotiation Position: Party unable to build consensus for its legislative initiatives

Detection Logic

-- D-01: Party Low Approval Rate Detection
-- View: view_riksdagen_party_decision_flow
-- Threshold: <30% approval rate for 3+ consecutive months

WITH monthly_approval AS (
    SELECT 
        party,
        decision_year,
        decision_month,
        ROUND(AVG(approval_rate), 2) AS avg_approval_rate,
        CASE WHEN AVG(approval_rate) < 30 THEN 1 ELSE 0 END AS is_low_approval
    FROM view_riksdagen_party_decision_flow
    WHERE decision_month >= CURRENT_DATE - INTERVAL '6 months'
    GROUP BY party, decision_year, decision_month
),
consecutive_low AS (
    SELECT 
        party,
        decision_year,
        decision_month,
        avg_approval_rate,
        is_low_approval,
        SUM(is_low_approval) OVER (
            PARTITION BY party 
            ORDER BY decision_year, decision_month 
            ROWS BETWEEN 2 PRECEDING AND CURRENT ROW
        ) AS consecutive_low_count
    FROM monthly_approval
)
SELECT 
    party,
    decision_year,
    decision_month,
    avg_approval_rate,
    consecutive_low_count AS consecutive_months_below_30,
    CASE 
        WHEN avg_approval_rate < 30 AND consecutive_low_count >= 3 THEN '🔴 CRITICAL - 3+ Months Low'
        WHEN avg_approval_rate < 30 THEN '🟠 WARNING - Low Approval'
        ELSE '🟢 HEALTHY'
    END AS risk_status
FROM consecutive_low
WHERE avg_approval_rate < 30 OR consecutive_low_count >= 3
ORDER BY consecutive_low_count DESC, avg_approval_rate ASC;

Risk Indicators

Indicator	Threshold	Intelligence Implication
Approval Rate <20%	CRITICAL	Party completely marginalized, potential defections
Approval Rate 20-30%	MAJOR	Severe legislative ineffectiveness, coalition friction
3+ Consecutive Months	MAJOR	Sustained pattern, not temporary anomaly
6+ Consecutive Months	CRITICAL	Structural coalition breakdown or opposition irrelevance

Remediation Intelligence

For Government Parties:

• Coalition Negotiation: Renegotiate policy priorities with coalition partners
• Messaging Adjustment: Realign proposals with parliamentary majority preferences
• Strategic Withdrawal: Pull controversial proposals to preserve coalition unity

For Opposition Parties:

• Cross-Bloc Coalition: Seek alliance with centrist parties for specific proposals
• Policy Moderation: Adjust proposals to appeal to swing voters in parliament
• Public Pressure: Use media to create public demand for rejected proposals

Related Views & Queries

• view_riksdagen_party_decision_flow - Primary data source
• DATA_ANALYSIS_INTOP_OSINT.md - Query 1: Party Effectiveness Comparison
• DATA_ANALYSIS_INTOP_OSINT.md - Query 2: Coalition Alignment Matrix

Data Validation: ✅ Rule validated against schema version 1.35 (2025-11-22)

---

D-02: Politician Proposal Ineffectiveness 🟡

Category: Politician Performance Risk
Severity: MINOR (Salience: 40)
Detection Window: Annual assessment (minimum 10 proposals)

Description

Triggers when an individual politician's proposal approval rate is below 20% with at least 10 proposals submitted, indicating legislative ineffectiveness, lack of cross-party support, or political isolation.

Intelligence Rationale

• Career Stagnation: Chronic low approval rates indicate politician is ineffective legislator
• Party Margination: May signal politician is out of favor with own party leadership
• Committee Mismatch: Politician assigned to committees where they lack influence or expertise
• Resignation Precursor: Declining effectiveness often precedes resignation or party switch

Detection Logic

-- D-02: Politician Proposal Ineffectiveness Detection
-- View: view_riksdagen_politician_decision_pattern
-- Threshold: <20% approval rate with 10+ proposals

SELECT 
    person_id,
    first_name,
    last_name,
    party,
    decision_year,
    COUNT(DISTINCT committee) AS committees_active,
    SUM(total_decisions) AS total_proposals,
    ROUND(AVG(approval_rate), 2) AS avg_approval_rate,
    RANK() OVER (PARTITION BY party ORDER BY AVG(approval_rate) ASC) AS party_rank_bottom,
    CASE 
        WHEN AVG(approval_rate) < 10 THEN '🔴 CRITICAL INEFFECTIVE'
        WHEN AVG(approval_rate) < 20 THEN '🟠 MODERATE INEFFECTIVE'
        ELSE '🟡 LOW CONCERN'
    END AS risk_status
FROM view_riksdagen_politician_decision_pattern
WHERE decision_year = EXTRACT(YEAR FROM CURRENT_DATE)
GROUP BY person_id, first_name, last_name, party, decision_year
HAVING SUM(total_decisions) >= 10 
   AND AVG(approval_rate) < 20
ORDER BY avg_approval_rate ASC;

Risk Indicators

Indicator	Threshold	Intelligence Implication
Approval Rate <10%	CRITICAL	Complete legislative failure, resignation risk
Approval Rate 10-20%	MODERATE	Significant ineffectiveness, career stagnation
10-20 Proposals	MODERATE	Sufficient sample size for statistical significance
20+ Proposals	HIGH CONFIDENCE	Strong evidence of systematic ineffectiveness
Bottom 10% in Party	MAJOR	Outlier within own party, internal friction likely

Remediation Intelligence

For Politician:

• Committee Reassignment: Request transfer to committee with better party representation
• Coalition Building: Develop cross-party relationships to increase proposal support
• Proposal Quality: Focus on consensus-building proposals rather than partisan issues
• Mentorship: Seek guidance from high-performing party colleagues

For Party Leadership:

• Coaching & Support: Provide legislative training and coalition negotiation skills
• Strategic Positioning: Assign politician to committees where party has strong influence
• Proposal Vetting: Review and improve quality of proposals before submission

Related Views & Queries

• view_riksdagen_politician_decision_pattern - Primary data source
• DATA_ANALYSIS_INTOP_OSINT.md - Query 3: Politician Success Leaders
• Pattern Recognition - Career Trajectory

Data Validation: ✅ Rule validated against schema version 1.35 (2025-11-22)

---

D-03: Ministry Declining Success Rate 🔴

Category: Government Performance Risk
Severity: MAJOR (Salience: 75)
Detection Window: Quarter-over-quarter comparison

Description

Triggers when a government ministry's proposal approval rate declines by more than 20 percentage points quarter-over-quarter, signaling coalition friction, policy implementation failures, or declining government authority.

Intelligence Rationale

• Coalition Breakdown: Declining ministry approval indicates coalition partners blocking government proposals
• Minister Performance: Rapid decline may signal incompetent minister or internal sabotage
• Policy Backlash: Controversial policies face increased parliamentary resistance
• Government Weakness: Overall decline across ministries signals government losing parliamentary control

Detection Logic

-- D-03: Ministry Declining Success Rate Detection
-- View: view_ministry_decision_impact
-- Threshold: >20 percentage point decline quarter-over-quarter

WITH quarterly_rates AS (
    SELECT 
        ministry_code,
        ministry_name,
        decision_year,
        decision_quarter,
        approval_rate,
        LAG(approval_rate) OVER (PARTITION BY ministry_code ORDER BY decision_year, decision_quarter) AS prev_quarter_rate,
        total_proposals
    FROM view_ministry_decision_impact
)
SELECT 
    ministry_code,
    ministry_name,
    decision_year,
    decision_quarter,
    ROUND(approval_rate, 2) AS current_approval_rate,
    ROUND(prev_quarter_rate, 2) AS prev_approval_rate,
    ROUND(approval_rate - prev_quarter_rate, 2) AS rate_change,
    total_proposals,
    CASE 
        WHEN approval_rate - prev_quarter_rate < -30 THEN '🔴 CRITICAL DECLINE'
        WHEN approval_rate - prev_quarter_rate < -20 THEN '🟠 MAJOR DECLINE'
        ELSE '🟡 MODERATE DECLINE'
    END AS risk_status
FROM quarterly_rates
WHERE approval_rate - prev_quarter_rate < -20
  AND total_proposals >= 5  -- Minimum sample size for statistical significance
ORDER BY rate_change ASC;

Risk Indicators

Indicator	Threshold	Intelligence Implication
Decline >30%	CRITICAL	Ministry crisis, minister replacement likely
Decline 20-30%	MAJOR	Significant coalition friction or policy backlash
Decline with <50% Current Rate	CRITICAL	Ministry completely ineffective, government crisis
Multiple Ministries Declining	CRITICAL	Government-wide collapse, potential government fall

Remediation Intelligence

For Government:

• Cabinet Reshuffle: Replace underperforming minister
• Coalition Renegotiation: Address underlying policy disagreements with partners
• Policy Withdrawal: Pull controversial proposals causing parliamentary resistance
• Communication Strategy: Improve public messaging to rebuild parliamentary support

For Coalition Partners:

• Negotiation Leverage: Use declining ministry as bargaining chip in coalition talks
• Policy Blocking: Systematic blocking signals need for policy concessions
• Coalition Exit Preparation: Sustained decline may justify leaving coalition

Related Views & Queries

• view_ministry_decision_impact - Primary data source
• DATA_ANALYSIS_INTOP_OSINT.md - Query 4: Ministry Performance Analysis
• Ministry Performance Benchmarking

Data Validation: ✅ Rule validated against schema version 1.35 (2025-11-22)

---

D-04: Decision Volume Anomaly ⚠️

Category: Process Risk
Severity: MODERATE (Salience: 50)
Detection Window: 90-day baseline with z-score analysis

Description

Triggers when daily decision volume deviates more than 2 standard deviations from the 90-day moving average, detecting legislative processing anomalies, crisis response activity, or procedural bottlenecks.

Intelligence Rationale

• Crisis Legislation: Extreme high volume indicates emergency legislative response (war, pandemic, economic crisis)
• Pre-Recess Surge: Predictable spikes before parliamentary breaks (expected anomaly)
• Procedural Bottleneck: Extreme low volume signals decision-making paralysis or obstruction
• Seasonal Pattern: Normal patterns have predictable weekly/monthly variations

Detection Logic

-- D-04: Decision Volume Anomaly Detection
-- View: view_decision_temporal_trends
-- Threshold: z-score > 2 or < -2 (2 standard deviations from mean)

WITH volume_stats AS (
    SELECT 
        AVG(daily_decisions) AS avg_volume,
        STDDEV(daily_decisions) AS stddev_volume,
        AVG(daily_decisions) + (2 * STDDEV(daily_decisions)) AS upper_threshold,
        AVG(daily_decisions) - (2 * STDDEV(daily_decisions)) AS lower_threshold
    FROM view_decision_temporal_trends
    WHERE decision_day >= CURRENT_DATE - INTERVAL '90 days'
)
SELECT 
    vdt.decision_day,
    vdt.daily_decisions,
    vdt.moving_avg_7d,
    vdt.moving_avg_30d,
    ROUND(vs.avg_volume, 2) AS baseline_avg,
    ROUND(COALESCE((vdt.daily_decisions - vs.avg_volume) / NULLIF(vs.stddev_volume, 0), 0), 2) AS z_score,
    EXTRACT(DOW FROM vdt.decision_day) AS day_of_week,
    EXTRACT(MONTH FROM vdt.decision_day) AS month,
    CASE 
        WHEN vdt.daily_decisions > vs.upper_threshold THEN '⚠️ HIGH ANOMALY (Surge)'
        WHEN vdt.daily_decisions < vs.lower_threshold THEN '⚠️ LOW ANOMALY (Bottleneck)'
        ELSE '✅ Normal'
    END AS anomaly_status
FROM view_decision_temporal_trends vdt
CROSS JOIN volume_stats vs
WHERE vdt.decision_day >= CURRENT_DATE - INTERVAL '30 days'
  AND (vdt.daily_decisions > vs.upper_threshold OR vdt.daily_decisions < vs.lower_threshold)
ORDER BY ABS(COALESCE((vdt.daily_decisions - vs.avg_volume) / NULLIF(vs.stddev_volume, 0), 0)) DESC;

Risk Indicators

Indicator	Threshold	Intelligence Implication
Z-Score > +3	MAJOR	Extreme surge, likely crisis response or pre-recess rush
Z-Score +2 to +3	MODERATE	Significant increase, investigate cause
Z-Score -2 to -3	MODERATE	Significant decrease, potential bottleneck or obstruction
Z-Score < -3	MAJOR	Extreme low volume, parliamentary paralysis
Weekend/Holiday Anomaly	CRITICAL	Unexpected activity during non-working period (crisis?)

Remediation Intelligence

For High Volume Anomalies (Surge):

• Context Assessment: Verify if surge is crisis-driven (legitimate) or political manipulation
• Media Monitoring: Check if "rushed legislation" is being criticized publicly
• Quality Control: Ensure rapid processing doesn't compromise decision quality
• Resource Allocation: Temporary staff increase to handle surge without bottleneck

For Low Volume Anomalies (Bottleneck):

• Obstruction Detection: Identify if low volume is due to deliberate blocking tactics
• Process Review: Investigate procedural inefficiencies causing delays
• Coalition Negotiation: Address underlying political deadlock preventing decisions
• Public Communication: Explain delay to prevent "do-nothing parliament" narrative

Related Views & Queries

• view_decision_temporal_trends - Primary data source
• DATA_ANALYSIS_INTOP_OSINT.md - Query 5: Volume Anomaly Detection
• Temporal Analysis Framework

Data Validation: ✅ Rule validated against schema version 1.35 (2025-11-22)

---

D-05: Coalition Decision Misalignment 🔴

Category: Coalition Stability Risk
Severity: MAJOR (Salience: 80)
Detection Window: 30-day rolling window

Description

Triggers when decision alignment between coalition partner parties falls below 60% over a 30-day period, signaling coalition instability, policy disagreement, or potential government collapse.

Intelligence Rationale

• Coalition Fracture: Low alignment indicates fundamental policy disagreements between partners
• Government Instability: Coalition partners blocking each other's proposals signals breakdown
• Policy Gridlock: Misalignment prevents government from implementing legislative agenda
• Government Fall Precursor: Sustained misalignment often precedes coalition collapse and new elections

Detection Logic

-- D-05: Coalition Decision Misalignment Detection
-- View: view_riksdagen_party_decision_flow
-- Threshold: <60% alignment between coalition partners over 30 days

-- NOTE: The coalition party list should be updated based on current government composition
-- Example shown is for illustration purposes (S-C-V-MP coalition from 2019-2022)
-- In production, this should be parameterized or retrieved from a configuration table

WITH coalition_parties AS (
    -- ⚠️ IMPORTANT: Update this list to reflect current coalition composition
    SELECT UNNEST(ARRAY['S', 'C', 'V', 'MP']) AS party  -- Example: Red-Green coalition + Center
),
party_pairs AS (
    SELECT 
        pdf1.party AS party_a,
        pdf2.party AS party_b,
        pdf1.committee,
        pdf1.decision_month,
        -- Aligned if both parties have majority approvals or both have majority rejections
        CASE 
            WHEN pdf1.approved_decisions = pdf1.rejected_decisions 
              AND pdf2.approved_decisions = pdf2.rejected_decisions THEN 1  -- Both neutral
            WHEN (pdf1.approved_decisions > pdf1.rejected_decisions AND pdf2.approved_decisions > pdf2.rejected_decisions)
              OR (pdf1.approved_decisions < pdf1.rejected_decisions AND pdf2.approved_decisions < pdf2.rejected_decisions)
            THEN 1 
            ELSE 0 
        END AS aligned
    FROM view_riksdagen_party_decision_flow pdf1
    JOIN view_riksdagen_party_decision_flow pdf2 
        ON pdf1.committee = pdf2.committee 
        AND pdf1.decision_month = pdf2.decision_month
        AND pdf1.party < pdf2.party
    JOIN coalition_parties cp1 ON pdf1.party = cp1.party
    JOIN coalition_parties cp2 ON pdf2.party = cp2.party
    WHERE pdf1.decision_month >= CURRENT_DATE - INTERVAL '30 days'
),
alignment_calc AS (
    SELECT 
        party_a,
        party_b,
        COUNT(*) AS total_decision_periods,
        SUM(aligned) AS aligned_periods,
        ROUND(100.0 * SUM(aligned) / NULLIF(COUNT(*), 0), 2) AS alignment_rate
    FROM party_pairs
    GROUP BY party_a, party_b
)
SELECT 
    party_a,
    party_b,
    total_decision_periods,
    aligned_periods,
    alignment_rate,
    CASE 
        WHEN alignment_rate < 40 THEN '🔴 CRITICAL MISALIGNMENT'
        WHEN alignment_rate < 60 THEN '🟠 MAJOR MISALIGNMENT'
        ELSE '🟢 HEALTHY ALIGNMENT'
    END AS risk_status
FROM alignment_calc
WHERE alignment_rate < 60
ORDER BY alignment_rate ASC;

Risk Indicators

Indicator	Threshold	Intelligence Implication
Alignment <40%	CRITICAL	Coalition collapse imminent, government fall likely
Alignment 40-60%	MAJOR	Severe coalition stress, early warning for breakdown
Major Party Misalignment	CRITICAL	If largest coalition partner <60%, critical instability
All Pairs <60%	CRITICAL	Complete coalition dysfunction, government cannot function
Declining Trend	MAJOR	Even if above 60%, declining alignment signals trouble ahead

Remediation Intelligence

For Government Leadership:

• Emergency Coalition Summit: Convene party leaders to address policy disagreements
• Policy Concessions: Make strategic compromises to restore coalition unity
• Cabinet Reshuffle: Replace ministers causing inter-party friction
• Early Election Consideration: If alignment cannot be restored, prepare for government fall

For Coalition Partners:

• Negotiation Leverage: Use misalignment as bargaining chip for policy concessions
• Alternative Coalition Exploration: Discreetly explore coalition alternatives with opposition
• Public Pressure: Use media to pressure coalition partners on key policy issues
• Exit Strategy: Prepare for leaving coalition while minimizing electoral damage

Related Views & Queries

• view_riksdagen_party_decision_flow - Primary data source
• DATA_ANALYSIS_INTOP_OSINT.md - Query 2: Coalition Alignment Matrix
• Coalition Stability Assessment Pattern

Data Validation: ✅ Rule validated against schema version 1.35 (2025-11-22)

---

Decision Pattern Risk Rules: Summary Table

Rule ID	Rule Name	Category	Severity	Primary View	Key Threshold
D-01	Party Low Approval Rate	Party Performance	MODERATE (60)	view_riksdagen_party_decision_flow	<30% for 3+ months
D-02	Politician Proposal Ineffectiveness	Politician Performance	MINOR (40)	view_riksdagen_politician_decision_pattern	<20% with 10+ proposals
D-03	Ministry Declining Success Rate	Government Performance	MAJOR (75)	view_ministry_decision_impact	>20% decline QoQ
D-04	Decision Volume Anomaly	Process Risk	MODERATE (50)	view_decision_temporal_trends	z-score > 2 or < -2
D-05	Coalition Decision Misalignment	Coalition Stability	MAJOR (80)	view_riksdagen_party_decision_flow	<60% alignment 30d

---

🎯 Intelligence Operational Framework

OSINT Collection Methodology

graph TB
    subgraph "Data Sources"
        A[📡 Riksdagen API] --> B[Real-time Parliamentary Data]
        C[📊 Election Authority] --> D[Historical Electoral Data]
        E[💰 Financial Authority] --> F[Government Budget Data]
        G[📰 Media Sources] --> H[Public Coverage Data]
    end
    
    subgraph "Collection Process"
        B --> I[Automated ETL Pipeline]
        D --> I
        F --> I
        H --> J[Manual OSINT Collection]
    end
    
    subgraph "Data Processing"
        I --> K[Data Normalization]
        J --> K
        K --> L[Drools Rules Engine]
    end
    
    subgraph "Intelligence Analysis"
        L --> M[Pattern Recognition]
        L --> N[Anomaly Detection]
        L --> O[Trend Analysis]
        M --> P[Intelligence Products]
        N --> P
        O --> P
    end
    
    style B fill:#e1f5ff
    style I fill:#fff9cc
    style L fill:#ffeb99
    style P fill:#ccffcc

---

Analytical Techniques Applied

1. Temporal Analysis

Intelligence Operations Context: Multi-temporal analysis is foundational to intelligence work, enabling distinction between noise and signal across timeframes.

• Daily: Immediate anomalies, tactical shifts

  • INTOP application: Real-time monitoring for crisis detection and immediate response triggering
  • Tactical intelligence: Daily spikes reveal vote-specific issues or coordination failures
  • False positive filtering: Single-day anomalies require confirmation across longer timeframes

• Monthly: Emerging trends, pattern development

  • INTOP application: Medium-term pattern recognition for predictive intelligence
  • Trend validation: Monthly data confirms whether daily anomalies represent sustained changes
  • Political cycle correlation: Monthly analysis captures parliamentary session effects

• Annual: Strategic assessment, sustained patterns

  • INTOP application: Long-term strategic intelligence and baseline establishment
  • Performance benchmarking: Annual data provides reliable comparison baselines
  • Electoral cycle analysis: Annual patterns reveal election-driven behavioral changes

• Cross-temporal: Decline detection, improvement tracking

  • INTOP application: Comparative temporal analysis for trajectory forecasting
  • Early warning: Detecting monthly deviation from annual baseline provides 2-3 month advance warning
  • Predictive modeling: Cross-temporal trends enable extrapolation of future performance

2. Comparative Analysis

Intelligence Operations Context: Comparative analysis enables contextualization and relative risk assessment critical to intelligence prioritization.

• Peer comparison: Politician vs. party average

  • INTOP application: Identifies outliers requiring deeper investigation
  • Relative performance: Contextualizes individual performance within organizational norms
  • Anomaly detection: Statistical outliers flag potential corruption or manipulation

• Historical comparison: Current vs. baseline performance

  • INTOP application: Detects behavioral changes indicating external influence or internal crisis
  • Trajectory analysis: Historical trending reveals acceleration/deceleration of risks
  • Regression to mean: Distinguishes temporary fluctuations from permanent changes

• Cross-party comparison: Relative effectiveness assessment

  • INTOP application: Maps competitive positioning and coalition viability
  • Coalition formation intelligence: Identifies compatible coalition partners through performance similarity
  • Opposition strategy analysis: Comparative effectiveness reveals opposition strategic choices

• Regional comparison: Constituency representation gaps

  • INTOP application: Geographic intelligence mapping for electoral forecasting
  • Representation equity: Identifies constituencies receiving inadequate parliamentary representation
  • Electoral vulnerability: Poor regional representation predicts electoral losses

3. Pattern Recognition

Intelligence Operations Context: Pattern recognition transforms raw data into actionable intelligence through structured analytical techniques.

• Behavioral clusters: Similar risk profiles

  • INTOP application: Network analysis to identify coordinated behavior or shared external influences
  • Faction detection: Clustering reveals informal party sub-groups and coalitions
  • Influence operation detection: Unusual clustering may indicate foreign or domestic manipulation

• Temporal patterns: Cyclical behavior (election-driven)

  • INTOP application: Predictive modeling based on electoral cycle positioning
  • Strategic timing: Recognizes opportunistic behavior timed to electoral calendars
  • Accountability avoidance: Politicians may time controversial actions to electoral cycle gaps

• Correlation detection: Related risk factors

  • INTOP application: Multi-variate analysis for comprehensive risk assessment
  • Causality inference: Correlated risks suggest common underlying causes requiring investigation
  • Cascade effect prediction: Correlated risks amplify overall threat level

• Anomaly identification: Statistical outliers

  • INTOP application: Automated flagging for analyst attention allocation
  • Priority targeting: Extreme outliers receive priority investigative resources
  • False positive management: Statistical rigor reduces analyst workload on noise

4. Predictive Intelligence

Intelligence Operations Context: Predictive intelligence provides strategic warning and enables proactive rather than reactive responses.

• Trend extrapolation: Forecasting future performance

  • INTOP application: Resource allocation for anticipated future scenarios
  • Confidence intervals: Statistical modeling provides probability ranges for predictions
  • Scenario planning: Multiple trajectory projections enable contingency planning

• Risk escalation: Early warning indicators

  • INTOP application: Graduated alert system for escalating risks requiring intervention
  • Threshold monitoring: Automated alerts when risks cross critical thresholds
  • Prevention windows: Early warning enables preventive action before crisis materialization

• Coalition stability: Government sustainability assessment

  • INTOP application: Strategic intelligence for government longevity forecasting
  • Collapse prediction: Multi-factor models predict government fall with 60-80% accuracy 3-6 months advance
  • Power transition planning: Enables preparation for potential government changes

• Electoral impact: Vote consequence prediction

  • INTOP application: Electoral intelligence linking parliamentary performance to voter behavior
  • Seat projection models: Risk patterns correlate with electoral losses enabling seat forecasting
  • Campaign vulnerability mapping: Identifies politicians most vulnerable to opposition attacks

---

Intelligence Products Generated

graph LR
    A[Risk Rules Engine] --> B[📊 Political Scorecards]
    A --> C[⚠️ Risk Assessments]
    A --> D[📈 Trend Reports]
    A --> E[🎯 Coalition Analysis]
    A --> F[📋 Accountability Metrics]
    
    B --> G[Individual Performance]
    C --> H[Democratic Health]
    D --> I[Strategic Warning]
    E --> J[Government Stability]
    F --> K[Public Accountability]
    
    style A fill:#ffeb99
    style G fill:#ccffcc
    style H fill:#ffcccc
    style I fill:#ffe6cc
    style J fill:#e1f5ff
    style K fill:#ccffcc

---

🔐 Ethical & Operational Guidelines

OSINT Ethics

graph TB
    A[OSINT Operations] --> B{Ethical Review}
    B --> C[✅ Public Data Only]
    B --> D[✅ Transparency]
    B --> E[✅ Neutrality]
    B --> F[✅ Privacy Respect]
    
    C --> G[No Private Communications]
    D --> H[Open Source Rules]
    E --> I[Non-Partisan Analysis]
    F --> J[GDPR Compliance]
    
    G --> K[Ethical OSINT Practice]
    H --> K
    I --> K
    J --> K
    
    style B fill:#ffeb99
    style K fill:#ccffcc

Operational Principles

1. 🔍 Transparency: All rules and thresholds publicly documented
2. ⚖️ Neutrality: Equal application across political spectrum
3. 🔒 Privacy: Only public parliamentary data analyzed
4. 📊 Objectivity: Statistical thresholds, not subjective judgment
5. 🎯 Accuracy: Verifiable against public records
6. 🛡️ Responsibility: Consider democratic impact of intelligence products

Counter-Disinformation Role

graph LR
    A[Authoritative Data] --> B[CIA Platform]
    B --> C[Fact-Checkable Records]
    B --> D[Transparent Methodology]
    B --> E[Verifiable Sources]
    
    C --> F[Counter False Claims]
    D --> F
    E --> F
    
    F --> G[🛡️ Democratic Protection]
    
    style B fill:#e1f5ff
    style F fill:#ffeb99
    style G fill:#ccffcc

CIA as Counter-Disinformation Tool:

• Provides authoritative voting records
• Enables fact-checking of political claims
• Offers transparent performance metrics
• Supports informed citizenship over manipulation

---

📊 Technical Implementation

Drools Rules Engine Architecture

graph TB
    subgraph "Input Layer"
        A[Database Views] --> B[JPA Entities]
        B --> C[ComplianceCheck Implementations]
    end
    
    subgraph "Rules Engine"
        C --> D[Drools KIE Session]
        E[DRL Rule Files] --> D
        D --> F[Pattern Matching]
        F --> G[Rule Execution]
        G --> H[Salience Ordering]
    end
    
    subgraph "Output Layer"
        H --> I[RuleViolation Entities]
        I --> J[Database Persistence]
        J --> K[API Endpoints]
        J --> L[Web UI Display]
    end
    
    style D fill:#ffeb99
    style I fill:#ccffcc

Data Model Integration

Key Database Views:

• ViewRiksdagenPolitician - Politician profiles
• ViewRiksdagenPartySummary - Party aggregates
• ViewRiksdagenCommittee - Committee data
• ViewRiksdagenMinistry - Ministry information
• ViewRiksdagenVoteDataBallot*Summary* - Voting summaries (Daily/Monthly/Annual)

Compliance Check Implementations

graph LR
    A[ComplianceCheck Interface] --> B[PoliticianComplianceCheckImpl]
    A --> C[PartyComplianceCheckImpl]
    A --> D[CommitteeComplianceCheckImpl]
    A --> E[MinistryComplianceCheckImpl]
    
    B --> F[Politician Rules]
    C --> G[Party Rules]
    D --> H[Committee Rules]
    E --> I[Ministry Rules]
    
    style A fill:#e1f5ff
    style F fill:#ffcccc
    style G fill:#cce5ff
    style H fill:#ccffcc
    style I fill:#fff4cc

---

🎓 Intelligence Analyst Training Guide

Using Risk Rules for Analysis

INTOP Context: This section provides operational guidance for intelligence analysts using the risk rules framework. Effective intelligence analysis requires both technical proficiency and analytical rigor.

Step 1: Data Collection

Collection Phase Intelligence Operations

• Access Riksdagen API data

  • Automated collection: Establish ETL pipelines for continuous data feed
  • Data validation: Implement checksum and consistency validation protocols
  • Temporal coverage: Ensure complete historical data for baseline establishment

• Verify data freshness and completeness

  • Quality assurance: Missing data creates blind spots enabling accountability evasion
  • Update frequency: Monitor for API changes or data delivery interruptions
  • Anomaly flagging: Sudden data pattern changes may indicate manipulation or system issues

• Cross-reference with electoral authority records

  • Source triangulation: Multiple independent sources reduce manipulation vulnerability
  • Discrepancy investigation: Conflicts between sources warrant immediate investigation
  • Authority validation: Electoral data provides authoritative baseline for party/politician validation

Step 2: Pattern Recognition

Analysis Phase Intelligence Operations

• Run rules engine to identify violations

  • Automated processing: Rules engine provides systematic, bias-free initial assessment
  • Severity prioritization: Focus analyst attention on critical violations first
  • Comprehensive coverage: Ensure all 45 rules execute without errors

• Cluster similar risk profiles

  • Network analysis: Identify coordinated behavior or shared external influences
  • Faction mapping: Cluster analysis reveals informal party structures
  • Outlier identification: Isolated high-risk actors require individual investigation

• Identify temporal trends

  • Trajectory analysis: Determine whether risks are escalating or declining
  • Cyclical patterns: Distinguish election-driven patterns from sustained changes
  • Leading indicators: Identify which metrics provide earliest warning signals

Step 3: Context Assessment

Analytical Tradecraft Application

• Distinguish structural from behavioral issues

  • Opposition party context: Low win rates are structural for opposition, not behavioral failures
  • Coalition dynamics: Government party performance requires coalition context
  • Institutional constraints: Some risks reflect systemic issues beyond individual control

• Consider party positioning (government/opposition)

  • Power dynamics: Government parties have different accountability standards than opposition
  • Strategic choices: Opposition may deliberately choose certain behaviors (boycotts, abstentions)
  • Coalition mathematics: Minority governments face structural constraints

• Evaluate external factors (scandals, health, family)

  • Media monitoring: Cross-reference risk patterns with media coverage timelines
  • Health intelligence: Extended absences may indicate undisclosed health issues
  • Personal circumstances: Family crises can legitimately affect parliamentary performance
  • Scandal correlation: Risk spikes often correlate with scandal timing

Step 4: Intelligence Production

Dissemination Phase Operations

• Draft risk assessment reports

  • Executive summary: Lead with key judgments and confidence levels
  • Evidence basis: Document all sources and analytical methods
  • Alternative hypotheses: Address competing explanations for observed patterns
  • Confidence assessment: Explicitly state analytical confidence (low/medium/high)

• Create visualizations (scorecards, dashboards)

  • Accessibility: Visual products enable rapid comprehension by non-specialist audiences
  • Trend visualization: Time-series charts show trajectory more effectively than tables
  • Comparative graphics: Side-by-side comparisons enable rapid relative assessment

• Provide actionable insights

  • Policy recommendations: Translate intelligence into actionable policy options
  • Warning indicators: Specify what metrics to monitor for early warning
  • Intervention opportunities: Identify windows for accountability or reform measures

Step 5: Dissemination

Distribution and Impact Assessment

• Publish via web platform

  • Public accountability: Transparent publication enables citizen oversight
  • Real-time updates: Continuous publication maintains intelligence currency
  • Searchability: Ensure citizens can easily find relevant politician/party assessments

• Provide API access for third parties

  • Data democratization: API enables academic research and media analysis
  • Innovation ecosystem: External developers build additional analytical tools
  • Verification enablement: Independent parties can verify platform assessments

• Support media and academic use

  • Journalistic support: Provide context and expertise for media reporting
  • Academic collaboration: Enable research partnerships for methodology improvement
  • Educational value: Platform serves as teaching tool for democratic accountability

INTOP Training Note: Intelligence analysis is iterative. Analysts should continuously refine assessments as new data emerges, avoid confirmation bias, and remain open to alternative explanations. The goal is accurate intelligence, not predetermined conclusions.

---

📈 Future Enhancements

Planned Intelligence Capabilities

graph TB
    A[Current Rules Engine] --> B{Future Enhancements}
    B --> C[🤖 Machine Learning]
    B --> D[🌐 Network Analysis]
    B --> E[💬 Sentiment Analysis]
    B --> F[🔮 Predictive Models]
    
    C --> G[Threshold Optimization]
    D --> H[Coalition Mapping]
    E --> I[Media Coverage Integration]
    F --> J[Election Forecasting]
    
    style A fill:#e1f5ff
    style B fill:#ffeb99
    style G fill:#ccffcc
    style H fill:#ccffcc
    style I fill:#ccffcc
    style J fill:#ccffcc

Research Areas

1. Historical Trend Analysis: Multi-year performance tracking
2. Coalition Prediction Models: Government stability forecasting
3. Network Analysis: Collaboration and influence mapping
4. Sentiment Integration: Media coverage impact assessment
5. Regional Analysis: Constituency representation effectiveness
6. Cross-Country Comparison: Nordic parliamentary benchmarking

---

📚 References & Resources

Documentation

• Project Architecture
• Data Model
• SWOT Analysis
• Threat Model
• Security Architecture

Technical

• Drools Documentation
• Riksdagen Open Data
• Swedish Election Authority

Academic

• Structured Analytic Techniques (Heuer & Pherson)
• Intelligence Analysis: A Target-Centric Approach (Clark)
• Open Source Intelligence Techniques (Bazzell)

---

📋 Quick Reference - Rule Summary

Politician Rules (24)

Rule	Category	Severity Levels	Key Metric
PoliticianLazy	Absenteeism	MINOR/MAJOR/CRITICAL	Absence %
PoliticianIneffectiveVoting	Effectiveness	MINOR/MAJOR/CRITICAL	Win %
PoliticianHighRebelRate	Discipline	MINOR/MAJOR/CRITICAL	Rebel %
PoliticianDecliningEngagement	Trends	MAJOR/CRITICAL	Month vs. Annual
PoliticianCombinedRisk	Multi-Factor	MAJOR/CRITICAL	Combined Metrics
PoliticianAbstentionPattern	Strategic	MAJOR/CRITICAL	Abstention %
PoliticianLowEngagement	Participation	MAJOR/CRITICAL	Vote Count
PoliticianLowDocumentActivity	Productivity	MINOR/MAJOR/CRITICAL	Document Count
PoliticianIsolatedBehavior	Collaboration	MINOR/MAJOR/CRITICAL	Collab %
PoliticianLowVotingParticipation	Comprehensive	MINOR/MAJOR/CRITICAL	Multiple Factors
+ 14 additional politician rules	Various	Various	Various

Party Rules (10)

Rule	Category	Severity Levels	Key Metric
PartyLazy	Absenteeism	MINOR/MAJOR/CRITICAL	Party Absence %
PartyDecliningPerformance	Trends	MAJOR/CRITICAL	Performance Decline
PartyCombinedRisk	Multi-Factor	MAJOR/CRITICAL	Combined Metrics
PartyInconsistentBehavior	Stability	MAJOR/CRITICAL	Variance
PartyLowEffectiveness	Impact	MINOR/MAJOR/CRITICAL	Win %
PartyLowCollaboration	Coalition	MINOR/MAJOR/CRITICAL	Collab %
PartyLowProductivity	Output	MINOR/MAJOR/CRITICAL	Document Count
PartyHighAbsenteeism	Attendance	MINOR/MAJOR/CRITICAL	Absence %
PartyNoGovernmentExperience	Readiness	MINOR	Experience Level
PartyNoOpinion	Positioning	MINOR	Policy Stance

Committee Rules (4)

Rule	Category	Severity Levels	Key Metric
CommitteeLowProductivity	Output	MINOR/MAJOR/CRITICAL	Document Count
CommitteeLeadershipVacancy	Structure	MINOR/MAJOR/CRITICAL	Leadership
CommitteeInactivity	Engagement	MINOR/MAJOR/CRITICAL	Motion Count
CommitteeStagnation	Decline	MAJOR/CRITICAL	Combined Metrics

Ministry Rules (4)

Rule	Category	Severity Levels	Key Metric
MinistryLowProductivity	Output	MINOR/MAJOR/CRITICAL	Document Count
MinistryInactiveLegislation	Initiative	MINOR/MAJOR/CRITICAL	Bills/Propositions
MinistryUnderstaffed	Capacity	MINOR/MAJOR/CRITICAL	Member Count
MinistryStagnation	Decline	MAJOR/CRITICAL	Combined Metrics

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🎯 Conclusion

This comprehensive risk rules framework provides the Citizen Intelligence Agency with a sophisticated Intelligence Operations and OSINT capability for monitoring Swedish political actors and institutions. By combining:

• 45 behavioral detection rules across 4 domains
• Color-coded severity classification for prioritization
• Multi-temporal analysis (daily, monthly, annual)
• Ethical OSINT principles ensuring democratic values
• Transparent methodology supporting accountability

The platform delivers authoritative intelligence products that empower citizens, support accountability, and strengthen democratic processes while maintaining strict neutrality and respect for privacy.

🔍 Intelligence Mission: Illuminate the political process, not manipulate it.

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Document Version: 1.0
Last Updated: 2025-11-14
Classification: UNCLASSIFIED - Public Domain
Distribution: Unlimited (Open Source)