behavioral-analysis（行为分析）

Behavioral Analysis Skill

Purpose

This skill provides comprehensive behavioral analysis methodologies for understanding political decision-making, cognitive patterns, and psychological dynamics within the Swedish Parliament. It combines political psychology research with OSINT intelligence to identify behavioral indicators, predict policy positions, and assess leadership effectiveness through evidence-based analysis of voting patterns, speech behavior, and collaboration networks.

When to Use This Skill

Apply this skill when:

• ✅ Analyzing voting deviation patterns to understand internal party conflicts
• ✅ Identifying cognitive biases in parliamentary decision-making
• ✅ Assessing leadership styles and personality traits of political figures
• ✅ Detecting group polarization and echo chamber effects in committees
• ✅ Evaluating constituency influence on voting behavior
• ✅ Predicting policy positions based on behavioral indicators
• ✅ Conducting psychological profiling for strategic intelligence
• ✅ Analyzing coalition dynamics and negotiation patterns
• ✅ Identifying behavioral risk indicators (absenteeism, isolation, radicalization)

Do NOT use for:

• ❌ Clinical psychological diagnosis (not qualified medical assessment)
• ❌ Personal mental health speculation without public disclosure
• ❌ Behavioral profiling for harassment or discrimination
• ❌ Non-evidence-based personality claims

Behavioral Analysis Framework

Political Psychology Dimensions

The Riksdagsmonitor platform analyzes five core behavioral dimensions to create comprehensive political profiles:

graph TB
    subgraph "Behavioral Intelligence Collection"
        A1[🗳️ Voting Behavior<br/>3.5M+ votes analyzed<br/>Deviation tracking]
        A2[👥 Social Networks<br/>Collaboration patterns<br/>Influence metrics]
        A3[📄 Productivity Signals<br/>Document authorship<br/>Committee activity]
        A4[🎤 Communication Style<br/>Speech analysis<br/>Rhetoric patterns]
        A5[⏱️ Temporal Patterns<br/>Attendance trends<br/>Engagement cycles]
    end

    subgraph "Psychological Analysis"
        A1 --> B1[Decision-Making Analysis]
        A2 --> B2[Group Dynamics Assessment]
        A3 --> B3[Motivation Evaluation]
        A4 --> B4[Leadership Style Profiling]
        A5 --> B5[Behavioral Consistency Check]
    end

    subgraph "Cognitive Bias Detection"
        B1 --> C1{Confirmation Bias}
        B2 --> C2{Groupthink}
        B3 --> C3{Status Quo Bias}
        B4 --> C4{Authority Bias}
        B5 --> C5{Recency Bias}
    end

    subgraph "Intelligence Product"
        C1 & C2 & C3 & C4 & C5 --> D[🧠 Behavioral Profile]
        D --> E[Predictive Insights]
        D --> F[Risk Indicators]
        D --> G[Leadership Assessment]
    end

    style A1 fill:#e1f5ff
    style A2 fill:#e1f5ff
    style A3 fill:#e1f5ff
    style A4 fill:#e1f5ff
    style A5 fill:#e1f5ff
    style D fill:#ffe6cc
    style E fill:#ccffcc
    style F fill:#ffcccc
    style G fill:#fff9cc

1. Voting Deviation Analysis

Cognitive Dissonance Detection

Political psychologists recognize voting deviation as a key indicator of cognitive dissonance - when a politician's personal beliefs conflict with party expectations. The Riksdagsmonitor platform tracks this through multi-dimensional analysis.

Database Views:

• view_riksdagen_vote_data_ballot_politician_summary_daily - Daily voting patterns
• view_riksdagen_politician_ballot_summary - Aggregated voting statistics
• view_politician_behavioral_trends - Long-term behavioral trends
• view_riksdagen_politician_decision_pattern - Decision pattern classification

Party Line Conformity Analysis

@Component
public class PartyConformityAnalyzer {

    /**
     * Analyzes voting deviation patterns to identify cognitive dissonance.
     *
     * High deviation indicates:
     * - Internal conflict with party platform
     * - Constituency pressure overriding party discipline
     * - Personal ideology asserting independence
     * - Strategic positioning for leadership
     */
    @Transactional(readOnly = true)
    public PartyConformityProfile analyzeConformity(String politicianId, String partyId) {
        String sql = """
            SELECT
                p.person_id,
                p.first_name || ' ' || p.last_name as name,
                p.party as current_party,
                vbs.total_votes,
                vbs.won_votes,
                vbs.lost_votes,
                vbs.rebel_votes,
                vbs.avg_vote_win_rate,
                vbs.vote_effectiveness_score,
                ROUND(100.0 * vbs.rebel_votes / NULLIF(vbs.total_votes, 0), 2) as deviation_rate,

                -- Behavioral indicators
                CASE
                    WHEN vbs.rebel_votes::float / NULLIF(vbs.total_votes, 0) < 0.02 THEN 'CONFORMIST'
                    WHEN vbs.rebel_votes::float / NULLIF(vbs.total_votes, 0) < 0.05 THEN 'MODERATE'
                    WHEN vbs.rebel_votes::float / NULLIF(vbs.total_votes, 0) < 0.10 THEN 'INDEPENDENT'
                    ELSE 'MAVERICK'
                END as conformity_type,

                -- Cognitive dissonance indicators
                CASE
                    WHEN vbs.rebel_votes > 50 AND vbs.rebel_votes::float / vbs.total_votes > 0.10
                        THEN 'HIGH_DISSONANCE'
                    WHEN vbs.rebel_votes > 20 AND vbs.rebel_votes::float / vbs.total_votes > 0.05
                        THEN 'MODERATE_DISSONANCE'
                    ELSE 'LOW_DISSONANCE'
                END as dissonance_level

            FROM view_riksdagen_politician p
            JOIN view_riksdagen_politician_ballot_summary vbs ON p.person_id = vbs.person_id
            WHERE p.person_id = :politicianId
                AND p.party = :partyId
            """;

        return jdbcTemplate.queryForObject(sql, PartyConformityProfile.class,
            Map.of("politicianId", politicianId, "partyId", partyId));
    }
}

Psychological Profile Types

2. Group Dynamics & Polarization

Echo Chamber Detection

Political committees can develop echo chambers where dissenting views are suppressed. The Riksdagsmonitor platform identifies these through collaboration pattern analysis.

import pandas as pd
import networkx as nx
from typing import Dict, List, Tuple

class EchoChamberDetector:
    """
    Detects echo chambers in parliamentary committees using network analysis.

    Indicators of echo chambers:
    - High internal connectivity, low external bridges
    - Ideological homogeneity exceeding party baseline
    - Resistance to cross-party collaboration
    - Information isolation from opposing viewpoints
    """

    def analyze_committee_network(self, committee_id: str) -> Dict:
        """
        Analyzes committee collaboration networks for echo chamber indicators.

        Returns metrics:
        - Internal density: Collaboration within ideological cluster
        - Bridge centrality: Cross-cluster information flow
        - Homophily index: Ideological similarity preference
        - Polarization score: Cluster separation intensity
        """

        query = """
        SELECT
            c.org_code,
            c.committee_name,

            -- Network structure metrics
            COUNT(DISTINCT cm.person_id) as member_count,
            COUNT(DISTINCT cm.party) as party_diversity,

            -- Collaboration patterns (co-authorship, co-sponsorship)
            (SELECT COUNT(*)
             FROM document_person dp1
             JOIN document_person dp2 ON dp1.document_id = dp2.document_id
             WHERE dp1.person_id IN (SELECT person_id FROM committee_member WHERE org_code = c.org_code)
               AND dp2.person_id IN (SELECT person_id FROM committee_member WHERE org_code = c.org_code)
               AND dp1.person_id < dp2.person_id
            ) as internal_collaboration,

            -- Cross-party bridge activity
            (SELECT COUNT(*)
             FROM document_person dp1
             JOIN document_person dp2 ON dp1.document_id = dp2.document_id
             JOIN person p1 ON dp1.person_id = p1.person_id
             JOIN person p2 ON dp2.person_id = p2.person_id
             WHERE dp1.person_id IN (SELECT person_id FROM committee_member WHERE org_code = c.org_code)
               AND dp2.person_id IN (SELECT person_id FROM committee_member WHERE org_code = c.org_code)
               AND p1.party != p2.party
               AND dp1.person_id < dp2.person_id
            ) as cross_party_bridges,

            -- Ideological homogeneity (voting similarity)
            AVG(
                (SELECT AVG(
                    CASE WHEN v1.vote = v2.vote THEN 1.0 ELSE 0.0 END
                ) FROM vote v1, vote v2
                WHERE v1.person_id IN (SELECT person_id FROM committee_member WHERE org_code = c.org_code)
                  AND v2.person_id IN (SELECT person_id FROM committee_member WHERE org_code = c.org_code)
                  AND v1.ballot_id = v2.ballot_id
                  AND v1.person_id < v2.person_id
                )
            ) as internal_voting_similarity

        FROM committee c
        JOIN committee_member cm ON c.org_code = cm.org_code
        WHERE c.org_code = %s
        GROUP BY c.org_code, c.committee_name
        """

        df = pd.read_sql(query, self.connection, params=[committee_id])

        # Calculate echo chamber indicators
        internal_density = df['internal_collaboration'].iloc[0] / (df['member_count'].iloc[0] ** 2)
        bridge_ratio = df['cross_party_bridges'].iloc[0] / max(df['internal_collaboration'].iloc[0], 1)
        homophily_index = df['internal_voting_similarity'].iloc[0]

        # Echo chamber score (0-100, higher = stronger echo chamber)
        echo_chamber_score = (
            (internal_density * 30) +
            ((1 - bridge_ratio) * 30) +
            (homophily_index * 40)
        )

        return {
            'committee_id': committee_id,
            'echo_chamber_score': round(echo_chamber_score, 2),
            'internal_density': round(internal_density, 3),
            'bridge_ratio': round(bridge_ratio, 3),
            'homophily_index': round(homophily_index, 3),
            'classification': self._classify_echo_chamber(echo_chamber_score)
        }

    def _classify_echo_chamber(self, score: float) -> str:
        """Classify echo chamber severity."""
        if score >= 75:
            return "SEVERE_ECHO_CHAMBER"
        elif score >= 60:
            return "MODERATE_ECHO_CHAMBER"
        elif score >= 40:
            return "MILD_POLARIZATION"
        else:
            return "HEALTHY_DIVERSITY"

Groupthink Detection Criteria

3. Leadership Style Profiling

Five Leadership Dimensions

Political leadership styles significantly impact party effectiveness and coalition stability. The Riksdagsmonitor platform classifies leaders across five dimensions based on behavioral evidence.

@Service
public class LeadershipStyleAnalyzer {

    /**
     * Analyzes leadership effectiveness through behavioral indicators.
     *
     * Based on transformational leadership theory (Bass & Riggio, 2006)
     * and political leadership research (Burns, 1978).
     */
    public LeadershipProfile analyzeLeadership(String politicianId) {
        String sql = """
            WITH leadership_metrics AS (
                SELECT
                    p.person_id,
                    p.first_name || ' ' || p.last_name as name,
                    p.party,

                    -- Dimension 1: Collaborative vs. Authoritarian
                    vim.collaboration_score,
                    vim.network_centrality,

                    -- Dimension 2: Ideological vs. Pragmatic
                    vbs.rebel_votes::float / NULLIF(vbs.total_votes, 0) as ideological_purity,
                    vbs.vote_effectiveness_score as pragmatic_success,

                    -- Dimension 3: Proactive vs. Reactive
                    COUNT(DISTINCT d.document_id) as initiated_documents,
                    vbs.total_votes as participation_votes,

                    -- Dimension 4: Consensus-builder vs. Confrontational
                    vim.cross_party_collaboration_score,
                    vbs.rebel_votes as confrontational_votes,

                    -- Dimension 5: Visible vs. Behind-scenes
                    COUNT(DISTINCT CASE WHEN d.document_type = 'motion' THEN d.document_id END) as public_initiatives,
                    COUNT(DISTINCT CASE WHEN d.document_type = 'interpellation' THEN d.document_id END) as oversight_activity

                FROM view_riksdagen_politician p
                LEFT JOIN view_riksdagen_politician_ballot_summary vbs ON p.person_id = vbs.person_id
                LEFT JOIN view_riksdagen_politician_influence_metrics vim ON p.person_id = vim.person_id
                LEFT JOIN view_riksdagen_politician_document d ON p.person_id = d.person_id
                WHERE p.person_id = :politicianId
                GROUP BY p.person_id, p.first_name, p.last_name, p.party,
                         vim.collaboration_score, vim.network_centrality,
                         vbs.rebel_votes, vbs.total_votes, vbs.vote_effectiveness_score
            )
            SELECT
                *,
                -- Leadership style classification
                CASE
                    WHEN collaboration_score > 0.7 AND cross_party_collaboration_score > 0.6
                        THEN 'TRANSFORMATIONAL'
                    WHEN ideological_purity > 0.15 AND confrontational_votes > 100
                        THEN 'IDEOLOGICAL_PURIST'
                    WHEN pragmatic_success > 0.75 AND cross_party_collaboration_score > 0.5
                        THEN 'PRAGMATIC_DEALMAKER'
                    WHEN initiated_documents > 50 AND public_initiatives > 30
                        THEN 'POLICY_ENTREPRENEUR'
                    WHEN network_centrality > 0.8 AND collaboration_score < 0.4
                        THEN 'AUTHORITARIAN_BROKER'
                    ELSE 'BACKBENCHER'
                END as leadership_style
            FROM leadership_metrics
            """;

        return jdbcTemplate.queryForObject(sql, LeadershipProfile.class,
            Map.of("politicianId", politicianId));
    }
}

Leadership Style Taxonomy

4. Cognitive Bias Identification

Decision-Making Bias Framework

Political decisions are influenced by systematic cognitive biases. The Riksdagsmonitor platform identifies these patterns through voting behavior analysis.

from dataclasses import dataclass
from typing import List, Optional
from datetime import datetime, timedelta

@dataclass
class CognitiveBiasIndicators:
    """Indicators of cognitive biases in political decision-making."""
    politician_id: str
    confirmation_bias_score: float
    status_quo_bias_score: float
    authority_bias_score: float
    recency_bias_score: float
    availability_bias_score: float

class CognitiveBiasDetector:
    """
    Identifies cognitive biases through voting pattern analysis.

    Based on Kahneman & Tversky's cognitive bias research
    applied to political decision-making contexts.
    """

    def detect_confirmation_bias(self, politician_id: str) -> float:
        """
        Detects confirmation bias: Tendency to vote with pre-existing beliefs.

        Measured by:
        - Consistency with historical positions
        - Resistance to policy evolution despite new evidence
        - Selective attention to information supporting prior stance
        """

        query = """
        WITH politician_voting AS (
            SELECT
                v.person_id,
                v.vote,
                b.issue_category,
                b.vote_date,
                LAG(v.vote) OVER (
                    PARTITION BY v.person_id, b.issue_category
                    ORDER BY b.vote_date
                ) as previous_vote,
                LAG(b.vote_date) OVER (
                    PARTITION BY v.person_id, b.issue_category
                    ORDER BY b.vote_date
                ) as previous_date
            FROM vote v
            JOIN ballot b ON v.ballot_id = b.ballot_id
            WHERE v.person_id = %s
                AND b.vote_date >= NOW() - INTERVAL '4 years'
        )
        SELECT
            person_id,
            -- Consistency score: How often votes align with historical position
            AVG(CASE WHEN vote = previous_vote THEN 1.0 ELSE 0.0 END) as consistency_rate,

            -- Rigidity score: Resistance to policy evolution over time
            COUNT(CASE WHEN vote != previous_vote
                       AND previous_date < vote_date - INTERVAL '1 year'
                       THEN 1 END)::float / COUNT(*) as evolution_resistance,

            COUNT(*) as total_comparable_votes

        FROM politician_voting
        WHERE previous_vote IS NOT NULL
        GROUP BY person_id
        """

        result = pd.read_sql(query, self.connection, params=[politician_id])

        if result.empty or result['total_comparable_votes'].iloc[0] < 10:
            return 0.0

        # Confirmation bias score: High consistency + high resistance = stronger bias
        consistency_rate = result['consistency_rate'].iloc[0]
        evolution_resistance = result['evolution_resistance'].iloc[0]

        bias_score = (consistency_rate * 0.6) + (evolution_resistance * 0.4)
        return round(bias_score * 100, 2)

    def detect_status_quo_bias(self, politician_id: str) -> float:
        """
        Detects status quo bias: Preference for maintaining current state.

        Measured by:
        - Voting against reform proposals
        - Supporting incumbent policies
        - Resisting change initiatives
        """

        query = """
        SELECT
            v.person_id,
            COUNT(CASE WHEN b.is_reform_proposal = TRUE AND v.vote = 'Nej' THEN 1 END)::float /
            NULLIF(COUNT(CASE WHEN b.is_reform_proposal = TRUE THEN 1 END), 0) as reform_opposition_rate,

            COUNT(CASE WHEN b.is_status_quo_motion = TRUE AND v.vote = 'Ja' THEN 1 END)::float /
            NULLIF(COUNT(CASE WHEN b.is_status_quo_motion = TRUE THEN 1 END), 0) as status_quo_support_rate,

            COUNT(*) as total_policy_votes

        FROM vote v
        JOIN ballot b ON v.ballot_id = b.ballot_id
        WHERE v.person_id = %s
            AND (b.is_reform_proposal = TRUE OR b.is_status_quo_motion = TRUE)
            AND b.vote_date >= NOW() - INTERVAL '2 years'
        GROUP BY v.person_id
        """

        result = pd.read_sql(query, self.connection, params=[politician_id])

        if result.empty or result['total_policy_votes'].iloc[0] < 5:
            return 0.0

        reform_opposition = result['reform_opposition_rate'].iloc[0] or 0.0
        status_quo_support = result['status_quo_support_rate'].iloc[0] or 0.0

        bias_score = (reform_opposition * 0.5) + (status_quo_support * 0.5)
        return round(bias_score * 100, 2)

    def detect_authority_bias(self, politician_id: str) -> float:
        """
        Detects authority bias: Over-reliance on party leadership guidance.

        Measured by:
        - Voting alignment with party leadership
        - Lack of independent positions
        - Deference to authority figures
        """

        query = """
        WITH party_leader_votes AS (
            SELECT
                v.ballot_id,
                v.vote as leader_vote
            FROM vote v
            JOIN person p ON v.person_id = p.person_id
            WHERE p.is_party_leader = TRUE
                AND p.party = (SELECT party FROM person WHERE person_id = %s)
        )
        SELECT
            v.person_id,
            COUNT(CASE WHEN v.vote = plv.leader_vote THEN 1 END)::float /
            NULLIF(COUNT(*), 0) as leadership_alignment_rate,

            COUNT(*) as total_votes_with_leader

        FROM vote v
        JOIN party_leader_votes plv ON v.ballot_id = plv.ballot_id
        WHERE v.person_id = %s
        GROUP BY v.person_id
        """

        result = pd.read_sql(query, self.connection, params=[politician_id, politician_id])

        if result.empty or result['total_votes_with_leader'].iloc[0] < 20:
            return 0.0

        alignment_rate = result['leadership_alignment_rate'].iloc[0]

        # Authority bias score: Very high alignment suggests deference
        if alignment_rate > 0.95:
            return 100.0
        elif alignment_rate > 0.90:
            return 75.0
        elif alignment_rate > 0.85:
            return 50.0
        else:
            return round((alignment_rate - 0.70) * 200, 2)  # Scale 70-85% to 0-30

    def detect_recency_bias(self, politician_id: str) -> float:
        """
        Detects recency bias: Disproportionate weight on recent information.

        Measured by:
        - Vote position changes after recent media coverage
        - Inconsistency with long-term stance based on recent events
        - Rapid policy shifts following public attention
        """

        query = """
        WITH recent_votes AS (
            SELECT
                v.person_id,
                v.vote,
                b.issue_category,
                b.vote_date,
                CASE WHEN b.vote_date >= NOW() - INTERVAL '90 days' THEN 'recent'
                     WHEN b.vote_date >= NOW() - INTERVAL '1 year' THEN 'medium_term'
                     ELSE 'historical' END as time_period
            FROM vote v
            JOIN ballot b ON v.ballot_id = b.ballot_id
            WHERE v.person_id = %s
                AND b.vote_date >= NOW() - INTERVAL '3 years'
        ),
        consistency_analysis AS (
            SELECT
                person_id,
                issue_category,
                AVG(CASE WHEN time_period = 'recent' AND vote = 'Ja' THEN 1.0 ELSE 0.0 END) as recent_support,
                AVG(CASE WHEN time_period = 'historical' AND vote = 'Ja' THEN 1.0 ELSE 0.0 END) as historical_support
            FROM recent_votes
            GROUP BY person_id, issue_category
            HAVING COUNT(CASE WHEN time_period = 'recent' THEN 1 END) >= 3
               AND COUNT(CASE WHEN time_period = 'historical' THEN 1 END) >= 5
        )
        SELECT
            person_id,
            AVG(ABS(recent_support - historical_support)) as avg_shift_magnitude,
            COUNT(*) as analyzed_categories
        FROM consistency_analysis
        WHERE ABS(recent_support - historical_support) > 0.20  -- Significant shift threshold
        GROUP BY person_id
        """

        result = pd.read_sql(query, self.connection, params=[politician_id])

        if result.empty or result['analyzed_categories'].iloc[0] < 3:
            return 0.0

        shift_magnitude = result['avg_shift_magnitude'].iloc[0]

        # Recency bias score: Larger shifts = stronger bias
        bias_score = min(shift_magnitude * 150, 100)  # Cap at 100
        return round(bias_score, 2)

Cognitive Bias Risk Matrix

5. Constituency Influence Analysis

Electoral Pressure Indicators

Politicians balance party loyalty with constituency demands. The Riksdagsmonitor platform measures this tension through deviation analysis correlated with electoral data.

-- Constituency Influence Scoring
WITH constituency_characteristics AS (
    SELECT
        er.election_region_id,
        er.region_name,
        er.population,
        er.urban_rural_classification,
        er.median_income,
        er.education_level,

        -- Electoral competitiveness (closer races = more pressure)
        er.winning_margin_percentage,
        CASE
            WHEN er.winning_margin_percentage < 5 THEN 'MARGINAL_SEAT'
            WHEN er.winning_margin_percentage < 10 THEN 'COMPETITIVE_SEAT'
            ELSE 'SAFE_SEAT'
        END as seat_classification,

        -- Ideological distance from party median
        er.constituency_ideology_score,
        p.party_ideology_score,
        ABS(er.constituency_ideology_score - p.party_ideology_score) as ideological_distance

    FROM election_region er
    JOIN party p ON er.winning_party = p.party_id
),
politician_constituency_behavior AS (
    SELECT
        pol.person_id,
        pol.first_name || ' ' || pol.last_name as name,
        pol.party,
        pol.constituency_id,
        cc.seat_classification,
        cc.ideological_distance,

        -- Voting behavior
        vbs.rebel_votes,
        vbs.total_votes,
        vbs.rebel_votes::float / NULLIF(vbs.total_votes, 0) as deviation_rate,

        -- Document activity reflecting constituency concerns
        COUNT(DISTINCT pd.document_id) as constituency_documents,

        -- Constituency influence score
        CASE
            WHEN cc.seat_classification = 'MARGINAL_SEAT'
                AND cc.ideological_distance > 15
                AND vbs.rebel_votes::float / vbs.total_votes > 0.05
                THEN 'HIGH_CONSTITUENCY_INFLUENCE'
            WHEN cc.seat_classification = 'COMPETITIVE_SEAT'
                AND vbs.rebel_votes::float / vbs.total_votes > 0.03
                THEN 'MODERATE_CONSTITUENCY_INFLUENCE'
            WHEN cc.seat_classification = 'SAFE_SEAT'
                AND vbs.rebel_votes::float / vbs.total_votes < 0.02
                THEN 'PARTY_DISCIPLINE_DOMINANT'
            ELSE 'BALANCED_INFLUENCE'
        END as influence_classification

    FROM view_riksdagen_politician pol
    JOIN constituency_characteristics cc ON pol.constituency_id = cc.election_region_id
    JOIN view_riksdagen_politician_ballot_summary vbs ON pol.person_id = vbs.person_id
    LEFT JOIN view_riksdagen_politician_document pd ON pol.person_id = pd.person_id
    GROUP BY pol.person_id, pol.first_name, pol.last_name, pol.party, pol.constituency_id,
             cc.seat_classification, cc.ideological_distance,
             vbs.rebel_votes, vbs.total_votes, pd.document_id
)
SELECT
    person_id,
    name,
    party,
    seat_classification,
    deviation_rate,
    influence_classification,

    -- Strategic intelligence assessment
    CASE
        WHEN influence_classification = 'HIGH_CONSTITUENCY_INFLUENCE'
            THEN 'Target for constituency-based persuasion campaigns'
        WHEN influence_classification = 'PARTY_DISCIPLINE_DOMINANT'
            THEN 'Requires party leadership negotiation'
        ELSE 'Balanced approach needed'
    END as strategic_approach

FROM politician_constituency_behavior
ORDER BY deviation_rate DESC, ideological_distance DESC;

6. Behavioral Risk Indicators

Comprehensive Risk Profiling

The Riksdagsmonitor platform integrates behavioral indicators with Drools risk rules to create comprehensive risk profiles. These profiles predict potential accountability failures.

Risk Rules Integration:

• PoliticianLazy.drl - Absenteeism indicating disengagement
• PoliticianIneffectiveVoting.drl - Chronic minority voting
• PartyRebelVoting.drl - Excessive deviation indicating instability
• CommitteeInactive.drl - Committee withdrawal patterns

@Component
public class BehavioralRiskAssessment {

    /**
     * Comprehensive behavioral risk assessment integrating multiple indicators.
     *
     * Risk dimensions:
     * 1. Engagement risk (absenteeism, withdrawal)
     * 2. Effectiveness risk (minority voting, low productivity)
     * 3. Stability risk (high deviation, erratic patterns)
     * 4. Collaboration risk (isolation, network periphery)
     * 5. Cognitive risk (bias indicators, decision-making quality)
     */
    public ComprehensiveRiskProfile assessBehavioralRisks(String politicianId) {
        String sql = """
            SELECT
                p.person_id,
                p.first_name || ' ' || p.last_name as name,
                p.party,

                -- Engagement Risk Indicators
                vbs_daily.avg_absent_percentage as daily_absence_rate,
                vbs_monthly.avg_absent_percentage as monthly_absence_rate,
                vbs_annual.avg_absent_percentage as annual_absence_rate,

                -- Effectiveness Risk Indicators
                vbs.vote_effectiveness_score,
                vbs.avg_vote_win_rate,
                vbs.lost_votes::float / NULLIF(vbs.total_votes, 0) as loss_rate,

                -- Stability Risk Indicators
                vbs.rebel_votes,
                vbs.rebel_votes::float / NULLIF(vbs.total_votes, 0) as deviation_rate,
                STDDEV(CASE WHEN v.vote != p.party_vote THEN 1 ELSE 0 END) as deviation_volatility,

                -- Collaboration Risk Indicators
                vim.collaboration_score,
                vim.network_centrality,
                vim.cross_party_collaboration_score,

                -- Productivity Indicators
                COUNT(DISTINCT pd.document_id) as total_documents,

                -- Overall Risk Score (0-100, higher = higher risk)
                (
                    COALESCE(vbs_annual.avg_absent_percentage, 0) * 0.25 +
                    COALESCE((1 - vbs.vote_effectiveness_score) * 100, 0) * 0.25 +
                    COALESCE(vbs.rebel_votes::float / NULLIF(vbs.total_votes, 0) * 100, 0) * 0.20 +
                    COALESCE((1 - vim.collaboration_score) * 100, 0) * 0.15 +
                    COALESCE((1 - vim.network_centrality) * 100, 0) * 0.15
                ) as composite_risk_score,

                -- Risk Classification
                CASE
                    WHEN (
                        COALESCE(vbs_annual.avg_absent_percentage, 0) * 0.25 +
                        COALESCE((1 - vbs.vote_effectiveness_score) * 100, 0) * 0.25 +
                        COALESCE(vbs.rebel_votes::float / NULLIF(vbs.total_votes, 0) * 100, 0) * 0.20 +
                        COALESCE((1 - vim.collaboration_score) * 100, 0) * 0.15 +
                        COALESCE((1 - vim.network_centrality) * 100, 0) * 0.15
                    ) >= 70 THEN 'CRITICAL_RISK'
                    WHEN (
                        COALESCE(vbs_annual.avg_absent_percentage, 0) * 0.25 +
                        COALESCE((1 - vbs.vote_effectiveness_score) * 100, 0) * 0.25 +
                        COALESCE(vbs.rebel_votes::float / NULLIF(vbs.total_votes, 0) * 100, 0) * 0.20 +
                        COALESCE((1 - vim.collaboration_score) * 100, 0) * 0.15 +
                        COALESCE((1 - vim.network_centrality) * 100, 0) * 0.15
                    ) >= 50 THEN 'HIGH_RISK'
                    WHEN (
                        COALESCE(vbs_annual.avg_absent_percentage, 0) * 0.25 +
                        COALESCE((1 - vbs.vote_effectiveness_score) * 100, 0) * 0.25 +
                        COALESCE(vbs.rebel_votes::float / NULLIF(vbs.total_votes, 0) * 100, 0) * 0.20 +
                        COALESCE((1 - vim.collaboration_score) * 100, 0) * 0.15 +
                        COALESCE((1 - vim.network_centrality) * 100, 0) * 0.15
                    ) >= 30 THEN 'MODERATE_RISK'
                    ELSE 'LOW_RISK'
                END as risk_classification

            FROM view_riksdagen_politician p
            LEFT JOIN view_riksdagen_politician_ballot_summary vbs ON p.person_id = vbs.person_id
            LEFT JOIN view_riksdagen_vote_data_ballot_politician_summary_daily vbs_daily ON p.person_id = vbs_daily.person_id
            LEFT JOIN view_riksdagen_vote_data_ballot_politician_summary_monthly vbs_monthly ON p.person_id = vbs_monthly.person_id
            LEFT JOIN view_riksdagen_vote_data_ballot_politician_summary_annual vbs_annual ON p.person_id = vbs_annual.person_id
            LEFT JOIN view_riksdagen_politician_influence_metrics vim ON p.person_id = vim.person_id
            LEFT JOIN view_riksdagen_politician_document pd ON p.person_id = pd.person_id
            LEFT JOIN vote v ON p.person_id = v.person_id
            WHERE p.person_id = :politicianId
            GROUP BY p.person_id, p.first_name, p.last_name, p.party,
                     vbs_daily.avg_absent_percentage, vbs_monthly.avg_absent_percentage,
                     vbs_annual.avg_absent_percentage, vbs.vote_effectiveness_score,
                     vbs.avg_vote_win_rate, vbs.lost_votes, vbs.total_votes,
                     vbs.rebel_votes, vim.collaboration_score, vim.network_centrality,
                     vim.cross_party_collaboration_score
            """;

        return jdbcTemplate.queryForObject(sql, ComprehensiveRiskProfile.class,
            Map.of("politicianId", politicianId));
    }
}

ISMS Compliance Mapping

ISO 27001:2022 Controls

NIST Cybersecurity Framework 2.0

CIS Controls v8

Hack23 ISMS Policy References

This skill implements requirements from:

• Secure Development Policy - Intelligence analysis quality standards
• Information Security Policy - Data ethics in behavioral analysis
• Access Control Policy - Behavioral risk-based access decisions
• Privacy Policy - GDPR-compliant behavioral profiling
• Threat Modeling - Insider threat behavioral indicators

References

Political Psychology Literature

1. Kahneman, D., & Tversky, A. (1979). "Prospect Theory: An Analysis of Decision under Risk." *Econometrica*, 47(2), 263-291.
2. Bass, B. M., & Riggio, R. E. (2006). *Transformational Leadership* (2nd ed.). Psychology Press.
3. Burns, J. M. (1978). *Leadership*. Harper & Row.
4. Janis, I. L. (1982). *Groupthink: Psychological Studies of Policy Decisions and Fiascoes*. Houghton Mifflin.
5. Tetlock, P. E. (2005). *Expert Political Judgment: How Good Is It? How Can We Know?* Princeton University Press.

Database Intelligence Sources

• DATABASE_VIEW_INTELLIGENCE_CATALOG.md - Complete view documentation
• RISK_RULES_INTOP_OSINT.md - Risk rule specifications
• DATA_ANALYSIS_INTOP_OSINT.md - Analysis frameworks
• INTELLIGENCE_DATA_FLOW.md - Data flow mapping

Swedish Political Context

• Swedish Parliament (Riksdagen) - Official documentation of parliamentary procedures
• V-Dem Institute - Democracy measurement and behavioral indicators
• Swedish Election Authority - Electoral competitiveness data