// Political psychology, cognitive biases, group dynamics, leadership analysis, decision-making patterns for Swedish political intelligence
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| name | behavioral-analysis |
| description | Political psychology, cognitive biases, group dynamics, leadership analysis, decision-making patterns for Swedish political intelligence |
| license | Apache-2.0 |
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.
Apply this skill when:
Do NOT use for:
The CIA 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
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style A5 fill:#e1f5ff
style D fill:#ffe6cc
style E fill:#ccffcc
style F fill:#ffcccc
style G fill:#fff9cc
Political psychologists recognize voting deviation as a key indicator of cognitive dissonance - when a politician's personal beliefs conflict with party expectations. The CIA platform tracks this through multi-dimensional analysis.
Database Views:
view_riksdagen_vote_data_ballot_politician_summary_daily - Daily voting patternsview_riksdagen_politician_ballot_summary - Aggregated voting statisticsview_politician_behavioral_trends - Long-term behavioral trendsview_riksdagen_politician_decision_pattern - Decision pattern classification@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));
}
}
| Conformity Type | Deviation Rate | Behavioral Indicators | Strategic Implications |
|---|---|---|---|
| CONFORMIST | < 2% | Strong party loyalty, risk-averse, hierarchical mindset | Safe coalition partner, reliable vote |
| MODERATE | 2-5% | Balanced independence, calculated risks | Negotiable on key issues |
| INDEPENDENT | 5-10% | Constituency-driven, personal ideology | Swing vote potential |
| MAVERICK | > 10% | Highly independent, ideological purity | Unpredictable, high-risk alliance |
Political committees can develop echo chambers where dissenting views are suppressed. The CIA 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"
| Indicator | Measurement | Risk Threshold | Intelligence Assessment |
|---|---|---|---|
| Internal Density | Collaboration frequency within group | > 0.75 | High cohesion, low external input |
| Bridge Ratio | Cross-party collaboration rate | < 0.20 | Limited opposing viewpoints |
| Homophily Index | Voting similarity among members | > 0.85 | Ideological homogeneity |
| Dissent Suppression | Minority opinion frequency | < 5% | Conformity pressure |
| Echo Chamber Score | Composite metric | > 75 | Critical groupthink risk |
Political leadership styles significantly impact party effectiveness and coalition stability. The CIA 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));
}
}
| Style | Behavioral Indicators | Strengths | Weaknesses | Strategic Use |
|---|---|---|---|---|
| TRANSFORMATIONAL | High collaboration, cross-party bridges, inspires change | Coalition-building, reform leadership | Can compromise core values | Coalition negotiations |
| IDEOLOGICAL_PURIST | High deviation, confrontational, principle-driven | Policy consistency, base mobilization | Limited legislative success | Opposition leadership |
| PRAGMATIC_DEALMAKER | Low deviation, high effectiveness, flexible | Legislative productivity, majority-building | Perceived as lacking principles | Government formation |
| POLICY_ENTREPRENEUR | High document initiation, innovation-focused | Agenda-setting, thought leadership | Implementation challenges | Committee chairmanship |
| AUTHORITARIAN_BROKER | High centrality, low collaboration, control-oriented | Discipline enforcement, clarity | Stifles innovation, loyalty issues | Crisis management |
| BACKBENCHER | Low activity across all dimensions | Low-risk, loyal follower | Limited influence | Safe majority vote |
Political decisions are influenced by systematic cognitive biases. The CIA 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)
| Bias Type | Detection Method | Risk Threshold | Behavioral Impact | Intelligence Use |
|---|---|---|---|---|
| Confirmation Bias | Historical vote consistency | > 85% | Ignores contradictory evidence | Predict resistance to new information |
| Status Quo Bias | Reform opposition rate | > 70% | Blocks necessary change | Identify reform obstacles |
| Authority Bias | Leadership alignment | > 90% | Lacks independent judgment | Predict via party leadership |
| Recency Bias | Vote shift magnitude after events | > 30% shift | Overreacts to recent news | Exploit timing of proposals |
| Availability Bias | Media-salient issue focus | > 60% media-driven | Ignores non-salient issues | Assess media manipulation vulnerability |
Politicians balance party loyalty with constituency demands. The CIA 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;
The CIA platform integrates behavioral indicators with Drools risk rules to create comprehensive risk profiles. These profiles predict potential accountability failures.
Risk Rules Integration:
@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));
}
}
| Control | Behavioral Analysis Application |
|---|---|
| A.5.1 - Policies for information security | Apply behavioral analysis to detect policy violations and non-compliance patterns |
| A.5.15 - Access control | Behavioral profiling for insider threat detection and access privilege monitoring |
| A.8.16 - Monitoring activities | Continuous behavioral monitoring for anomaly detection |
| A.8.23 - Web filtering | Analyze access patterns to identify unauthorized information seeking |
| Function | Behavioral Analysis Integration |
|---|---|
| IDENTIFY (ID.AM) | Behavioral profiling of personnel with access to sensitive political intelligence |
| DETECT (DE.CM) | Continuous monitoring for anomalous behavior patterns |
| RESPOND (RS.AN) | Behavioral analysis to assess incident response effectiveness |
| Control | Application |
|---|---|
| CIS Control 6 - Access Control Management | Apply behavioral risk assessment to access privilege decisions |
| CIS Control 8 - Audit Log Management | Behavioral analysis of audit log patterns |
This skill implements requirements from: