electoral-analysis
Originally fromhack23/riksdagsmonitor
SKILL.md
Electoral Analysis Skill
Purpose
This skill provides comprehensive methodologies for analyzing Swedish electoral dynamics, forecasting election outcomes, predicting coalition formations, and assessing campaign effectiveness. It integrates statistical modeling, polling analysis, and historical trend analysis to produce high-confidence intelligence products for democratic accountability assessment.
When to Use This Skill
Apply this skill when:
- ✅ Forecasting election outcomes (seat projections, vote shares)
- ✅ Analyzing polling trends and calculating poll aggregates
- ✅ Predicting coalition formation post-election
- ✅ Assessing swing voter behavior and electoral volatility
- ✅ Evaluating campaign effectiveness and messaging impact
- ✅ Calculating electoral system effects (proportional representation, thresholds)
- ✅ Identifying marginal constituencies and competitive races
Do NOT use for:
- ❌ Individual voter predictions (violates privacy, no granular data)
- ❌ Local/municipal elections (different dynamics, separate models)
- ❌ EU Parliament elections (different party configurations)
Swedish Electoral System Context
Key Electoral Characteristics
graph TB
subgraph "Electoral Framework"
A[349 Riksdag Seats]
A --> B[310 Constituency Seats<br/>29 constituencies]
A --> C[39 Leveling Seats<br/>National proportionality]
end
subgraph "Allocation Rules"
D[Modified Sainte-Laguë]
E[4% National Threshold]
F[12% Constituency Threshold]
D --> G[Seat Distribution]
E --> G
F --> G
end
subgraph "Electoral Cycle"
H[4-Year Fixed Term]
I[September Elections]
J[Sunday Voting]
H & I & J --> K[Election Day 2026<br/>September 13]
end
subgraph "Forecasting Inputs"
L[Historical Results<br/>1970-2022]
M[Opinion Polls<br/>Monthly tracking]
N[Demographic Shifts]
O[Campaign Events]
L & M & N & O --> P[Election Model]
end
P --> Q[Seat Projections]
P --> R[Coalition Scenarios]
style A fill:#e1f5ff
style D fill:#ffeb99
style P fill:#ffe6cc
style Q fill:#ccffcc
style R fill:#ccffcc
1. Election Forecasting Models
Polling Aggregation & Trend Estimation
Purpose: Combine multiple polls to estimate current vote intention with confidence intervals.
import pandas as pd
import numpy as np
from scipy import stats
from datetime import datetime, timedelta
class SwedishElectionForecaster:
"""
Electoral forecasting for Swedish Riksdag elections
Supports: Predictive Intelligence Framework
"""
def __init__(self, db_connection):
self.db = db_connection
self.parties = ['S', 'M', 'SD', 'C', 'V', 'KD', 'L', 'MP']
self.threshold = 4.0 # Electoral threshold
def aggregate_polls_weighted(self, lookback_days=90):
"""
Weighted poll aggregation using recency and sample size
Data Source: External polling data (Novus, Sifo, YouGov, Demoskop)
Intelligence Product: Current vote intention estimates
"""
# Query: Fetch recent polls
query = f"""
SELECT
poll_date,
polling_company,
sample_size,
party,
percentage
FROM opinion_polls
WHERE poll_date >= CURRENT_DATE - INTERVAL '{lookback_days} days'
ORDER BY poll_date DESC
"""
df = pd.read_sql(query, self.db)
# Calculate weights
df['days_ago'] = (pd.Timestamp.now() - pd.to_datetime(df['poll_date'])).dt.days
df['recency_weight'] = np.exp(-df['days_ago'] / 30) # Exponential decay, half-life 30 days
df['sample_weight'] = np.sqrt(df['sample_size']) / 1000 # Sample size adjustment
df['total_weight'] = df['recency_weight'] * df['sample_weight']
# Weighted average by party
aggregated = df.groupby('party').apply(
lambda x: np.average(x['percentage'], weights=x['total_weight'])
).to_dict()
# Calculate standard errors
standard_errors = df.groupby('party').apply(
lambda x: np.sqrt(np.average((x['percentage'] - aggregated[x.name])**2, weights=x['total_weight']))
).to_dict()
# 95% confidence intervals
confidence_intervals = {
party: {
'estimate': aggregated[party],
'lower_95': aggregated[party] - 1.96 * standard_errors[party],
'upper_95': aggregated[party] + 1.96 * standard_errors[party]
}
for party in self.parties
}
return confidence_intervals
def structural_forecast_model(self, election_date):
"""
Structural model combining polls, fundamentals, and historical patterns
Model Components:
1. Current polling average (weighted 50%)
2. Economic indicators (weighted 25%)
3. Incumbency advantage/disadvantage (weighted 15%)
4. Campaign effects (weighted 10%)
"""
# Component 1: Polling average
polls = self.aggregate_polls_weighted()
# Component 2: Economic fundamentals
query = """
SELECT
indicator_name,
value,
year
FROM world_bank_data
WHERE country_code = 'SWE'
AND indicator_name IN ('GDP growth', 'Unemployment rate', 'Inflation')
AND year = EXTRACT(YEAR FROM CURRENT_DATE) - 1
"""
economic_df = pd.read_sql(query, self.db)
economic_score = self.calculate_economic_vote(economic_df)
# Component 3: Incumbency factor
query_incumbent = """
SELECT
party,
in_government,
government_duration_years
FROM current_government_status
"""
incumbency_df = pd.read_sql(query_incumbent, self.db)
incumbency_effects = self.calculate_incumbency_penalty(incumbency_df)
# Component 4: Campaign effects (closer to election = more weight on polls)
days_until_election = (election_date - datetime.now()).days
campaign_factor = 1.0 if days_until_election < 30 else 0.5 # Polls more reliable near election
# Combine components
forecasts = {}
for party in self.parties:
poll_component = polls[party]['estimate'] * 0.5 * campaign_factor
economic_component = economic_score.get(party, 0) * 0.25
incumbency_component = incumbency_effects.get(party, 0) * 0.15
forecast = poll_component + economic_component + incumbency_component
# Ensure non-negative and sums to 100%
forecasts[party] = max(0, forecast)
# Normalize to 100%
total = sum(forecasts.values())
forecasts = {party: (vote / total) * 100 for party, vote in forecasts.items()}
return forecasts
def calculate_economic_vote(self, economic_df):
"""
Model economic voting: Good economy benefits incumbents
Formula: ΔVote = β₁*GDP_growth + β₂*Unemployment_change + β₃*Inflation
Coefficients based on Swedish electoral research
"""
gdp_growth = economic_df[economic_df['indicator_name'] == 'GDP growth']['value'].iloc[0]
unemployment = economic_df[economic_df['indicator_name'] == 'Unemployment rate']['value'].iloc[0]
inflation = economic_df[economic_df['indicator_name'] == 'Inflation']['value'].iloc[0]
# Economic vote model (simplified coefficients)
economic_advantage = (0.5 * gdp_growth) - (0.3 * unemployment) - (0.2 * inflation)
# Query: Which parties are in government
query = "SELECT party FROM current_government_status WHERE in_government = TRUE"
incumbent_parties = pd.read_sql(query, self.db)['party'].tolist()
# Allocate economic vote to incumbents
economic_scores = {}
for party in self.parties:
if party in incumbent_parties:
economic_scores[party] = economic_advantage / len(incumbent_parties)
else:
economic_scores[party] = 0
return economic_scores
def calculate_incumbency_penalty(self, incumbency_df):
"""
Model incumbency fatigue: Long-serving governments lose support
Penalty = -0.5% per year in government (capped at -5%)
"""
penalties = {}
for _, row in incumbency_df.iterrows():
if row['in_government']:
penalty = min(-0.5 * row['government_duration_years'], -5.0)
penalties[row['party']] = penalty
else:
penalties[row['party']] = 0
return penalties
Seat Projection Algorithm
Purpose: Convert vote share forecasts to seat allocations using Modified Sainte-Laguë method.
def project_riksdag_seats(self, vote_shares):
"""
Project Riksdag seat distribution from vote share forecasts
Method: Modified Sainte-Laguë with 4% threshold
Output: 349 seats allocated across parties
"""
# Apply 4% threshold
qualified_parties = {
party: vote for party, vote in vote_shares.items()
if vote >= self.threshold
}
if len(qualified_parties) == 0:
raise ValueError("No parties exceed 4% threshold")
# Allocate 349 seats
seats_allocated = {party: 0 for party in qualified_parties}
for seat_num in range(349):
# Calculate quotient for each party
quotients = {}
for party, vote_pct in qualified_parties.items():
if seats_allocated[party] == 0:
divisor = 1.4 # First seat divisor (modified Sainte-Laguë)
else:
divisor = 2 * seats_allocated[party] + 1
quotients[party] = vote_pct / divisor
# Award seat to party with highest quotient
winning_party = max(quotients, key=quotients.get)
seats_allocated[winning_party] += 1
return seats_allocated
def monte_carlo_seat_simulation(self, vote_forecasts, n_simulations=10000):
"""
Monte Carlo simulation for seat projection confidence intervals
Method: Sample from vote share distributions, calculate seats
Output: Probability distribution of seat outcomes
"""
seat_simulations = {party: [] for party in self.parties}
for _ in range(n_simulations):
# Sample vote shares from normal distributions (using forecast uncertainties)
sampled_votes = {}
for party in self.parties:
mean = vote_forecasts[party]['estimate']
std = (vote_forecasts[party]['upper_95'] - vote_forecasts[party]['lower_95']) / (2 * 1.96)
# Sample and ensure non-negative
sampled_votes[party] = max(0, np.random.normal(mean, std))
# Normalize to 100%
total = sum(sampled_votes.values())
sampled_votes = {party: (vote / total) * 100 for party, vote in sampled_votes.items()}
# Calculate seats for this sample
try:
seats = self.project_riksdag_seats(sampled_votes)
for party in self.parties:
seat_simulations[party].append(seats.get(party, 0))
except ValueError:
# Skip if no parties exceed threshold (rare edge case)
continue
# Calculate statistics
seat_projections = {}
for party in self.parties:
sims = seat_simulations[party]
seat_projections[party] = {
'median': int(np.median(sims)),
'mean': np.mean(sims),
'lower_95': int(np.percentile(sims, 2.5)),
'upper_95': int(np.percentile(sims, 97.5)),
'probability_in_riksdag': sum(s > 0 for s in sims) / len(sims)
}
return seat_projections
2. Coalition Formation Prediction
Purpose: Forecast which coalition is most likely to form government post-election.
class CoalitionPredictor:
"""
Coalition formation analysis using game theory and historical patterns
Supports: Decision Intelligence Framework
"""
def __init__(self, db_connection):
self.db = db_connection
def enumerate_viable_coalitions(self, seat_projections):
"""
Generate all mathematically viable coalition combinations
Criteria:
1. Total seats ≥ 175 (majority)
2. Ideologically compatible parties
3. No historical vetoes (e.g., no party wants coalition with SD except M/KD)
"""
from itertools import combinations
parties = list(seat_projections.keys())
viable_coalitions = []
# Define compatibility matrix based on Swedish political reality
incompatible_pairs = [
('S', 'M'), # Polar opposites
('S', 'SD'), # S refuses SD cooperation
('V', 'M'), # Ideological incompatibility
('V', 'KD'), # Ideological incompatibility
('MP', 'SD'), # Ideological incompatibility
('L', 'V'), # Ideological distance
]
# Iterate through all possible combinations
for r in range(1, len(parties) + 1):
for combo in combinations(parties, r):
total_seats = sum(seat_projections[p]['median'] for p in combo)
# Check majority threshold
if total_seats >= 175:
# Check compatibility
compatible = True
for p1, p2 in combinations(combo, 2):
if (p1, p2) in incompatible_pairs or (p2, p1) in incompatible_pairs:
compatible = False
break
if compatible:
viable_coalitions.append({
'parties': combo,
'total_seats': total_seats,
'size': len(combo)
})
return viable_coalitions
def calculate_coalition_stability(self, coalition_parties):
"""
Assess coalition stability using voting alignment history
Data Source: view_riksdagen_party_coalition_agreeableness
Output: Stability score 0-100
"""
query = f"""
SELECT
p1.party as party_a,
p2.party as party_b,
AVG(CASE WHEN p1.party_position = p2.party_position THEN 1.0 ELSE 0.0 END) as alignment_rate
FROM view_riksdagen_party_ballot_support_annual_summary p1
JOIN view_riksdagen_party_ballot_support_annual_summary p2
ON p1.ballot_id = p2.ballot_id
AND p1.party < p2.party
WHERE p1.party IN {tuple(coalition_parties)}
AND p2.party IN {tuple(coalition_parties)}
AND p1.vote_date >= CURRENT_DATE - INTERVAL '4 years'
GROUP BY p1.party, p2.party
"""
alignment_df = pd.read_sql(query, self.db)
# Average pairwise alignment
stability_score = alignment_df['alignment_rate'].mean() * 100
return stability_score
def predict_coalition_probability(self, viable_coalitions):
"""
Assign formation probability to each viable coalition
Factors:
1. Seat surplus (more seats = more stable)
2. Coalition size (fewer parties = easier negotiation)
3. Historical stability (voting alignment)
4. Ideological cohesion
"""
coalition_scores = []
for coalition in viable_coalitions:
parties = coalition['parties']
seats = coalition['total_seats']
size = coalition['size']
# Factor 1: Seat surplus (above 175)
seat_surplus = seats - 175
seat_score = min(seat_surplus / 50, 1.0) * 30 # Max 30 points
# Factor 2: Coalition size (fewer is better)
size_score = max(0, 30 - (size - 1) * 10) # 30 for single party, 20 for 2 parties, etc.
# Factor 3: Historical stability
stability = self.calculate_coalition_stability(parties)
stability_score = stability * 0.3 # Max 30 points
# Factor 4: Ideological cohesion (simplified)
# Center-right bloc: M, KD, L, C = high cohesion
# Left bloc: S, V, MP = high cohesion
if set(parties).issubset({'M', 'KD', 'L', 'C'}):
ideology_score = 10
elif set(parties).issubset({'S', 'V', 'MP'}):
ideology_score = 10
else:
ideology_score = 5 # Mixed bloc
total_score = seat_score + size_score + stability_score + ideology_score
coalition_scores.append({
'coalition': ' + '.join(parties),
'parties': parties,
'seats': seats,
'probability_score': total_score,
'factors': {
'seat_surplus': seat_score,
'size_penalty': size_score,
'stability': stability_score,
'ideology': ideology_score
}
})
# Normalize scores to probabilities
total_score = sum(c['probability_score'] for c in coalition_scores)
for coalition in coalition_scores:
coalition['formation_probability'] = (coalition['probability_score'] / total_score) * 100
# Sort by probability
coalition_scores.sort(key=lambda x: x['formation_probability'], reverse=True)
return coalition_scores
3. Swing Voter Analysis
Purpose: Identify and model voters likely to switch parties between elections.
-- Swing District Analysis: Identify constituencies with high volatility
WITH election_volatility AS (
SELECT
constituency_name,
election_year,
party_name,
percentage,
ABS(percentage - LAG(percentage) OVER (
PARTITION BY constituency_name, party_name
ORDER BY election_year
)) as vote_swing
FROM constituency_election_results
WHERE election_year >= 2010
),
constituency_volatility_score AS (
SELECT
constituency_name,
AVG(vote_swing) as avg_swing,
MAX(vote_swing) as max_swing,
STDDEV(vote_swing) as swing_volatility
FROM election_volatility
WHERE vote_swing IS NOT NULL
GROUP BY constituency_name
)
SELECT
constituency_name,
ROUND(avg_swing, 2) as avg_swing_pct,
ROUND(max_swing, 2) as max_swing_pct,
ROUND(swing_volatility, 2) as volatility,
CASE
WHEN avg_swing > 5.0 THEN 'HIGH VOLATILITY - Swing District'
WHEN avg_swing > 3.0 THEN 'MODERATE VOLATILITY'
ELSE 'LOW VOLATILITY - Safe District'
END as district_classification
FROM constituency_volatility_score
ORDER BY avg_swing DESC
LIMIT 20;
4. Campaign Effectiveness Analysis
Purpose: Measure impact of campaign events on polling and vote intention.
def analyze_campaign_event_impact(self, event_date, event_description):
"""
Interrupted time series analysis for campaign event impact
Method: Compare polling trend before/after event
Example Events: Leader debates, scandals, policy announcements
"""
# Query: Polling data 60 days before and after event
query = f"""
SELECT
poll_date,
party,
percentage
FROM opinion_polls
WHERE poll_date BETWEEN '{event_date - timedelta(days=60)}'
AND '{event_date + timedelta(days=60)}'
ORDER BY poll_date
"""
df = pd.read_sql(query, self.db)
# Create intervention variable
df['post_event'] = (df['poll_date'] > event_date).astype(int)
df['days_since_start'] = (df['poll_date'] - df['poll_date'].min()).dt.days
impact_results = {}
for party in df['party'].unique():
party_df = df[df['party'] == party].copy()
# Fit regression: Vote% ~ Time + Post_Event + Time*Post_Event
from sklearn.linear_model import LinearRegression
X = party_df[['days_since_start', 'post_event']]
X['interaction'] = X['days_since_start'] * X['post_event']
y = party_df['percentage']
model = LinearRegression()
model.fit(X, y)
# Extract coefficients
time_trend = model.coef_[0]
event_impact = model.coef_[1]
trend_change = model.coef_[2]
impact_results[party] = {
'event': event_description,
'immediate_impact': event_impact, # Jump in support at event
'trend_change': trend_change, # Change in trend slope
'statistical_significance': self._calculate_p_value(model, X, y)
}
return impact_results
5. Threshold Watch (4% Electoral Threshold)
Purpose: Monitor parties at risk of falling below 4% threshold.
-- Threshold Risk Analysis: Parties near 4% cutoff
WITH recent_polls AS (
SELECT
party,
poll_date,
percentage,
ROW_NUMBER() OVER (PARTITION BY party ORDER BY poll_date DESC) as recency_rank
FROM opinion_polls
WHERE poll_date >= CURRENT_DATE - INTERVAL '90 days'
),
threshold_analysis AS (
SELECT
party,
AVG(percentage) as avg_support,
STDDEV(percentage) as support_volatility,
MIN(percentage) as min_support,
MAX(percentage) as max_support,
COUNT(*) as poll_count
FROM recent_polls
WHERE recency_rank <= 10 -- Last 10 polls per party
GROUP BY party
)
SELECT
party,
ROUND(avg_support, 2) as current_support,
ROUND(support_volatility, 2) as volatility,
ROUND(min_support, 2) as lowest_poll,
ROUND(max_support, 2) as highest_poll,
CASE
WHEN avg_support < 4.0 THEN '🔴 BELOW THRESHOLD - No seats'
WHEN avg_support < 4.5 THEN '🟠 CRITICAL RISK - Within margin of error'
WHEN avg_support < 5.0 THEN '🟡 MODERATE RISK - Close to threshold'
ELSE '🟢 SAFE - Above threshold'
END as threshold_risk,
-- Probability of exceeding threshold (normal distribution assumption)
ROUND(
100 * (1 - stats.norm.cdf(4.0, avg_support, support_volatility)),
1
) as probability_exceeds_threshold
FROM threshold_analysis
WHERE avg_support <= 6.0 -- Focus on at-risk parties
ORDER BY avg_support ASC;
ISMS Compliance Mapping
ISO 27001:2022 Controls
A.5.9 - Inventory of Information and Other Associated Assets
- Electoral data sources cataloged and classified
- Polling methodology documented for transparency
A.5.33 - Protection of Records
- Historical election results maintained with integrity
- Version control for forecast models
NIST CSF 2.0 Functions
IDENTIFY (ID)
- ID.RA-1: Electoral volatility risks identified through swing analysis
- ID.RA-2: Threat intelligence on foreign election interference integrated
DETECT (DE)
- DE.AE-3: Event data aggregated and correlated (polling anomalies, manipulation)
CIS Controls v8.1
CIS Control 3: Data Protection
- 3.1: Establish data inventory (electoral data, polling sources)
- 3.12: Segment data processing and storage based on classification
CIS Control 12: Network Infrastructure Management
- 12.4: Deny unauthorized communication over network (protect polling data feeds)
Hack23 ISMS Policy References
Data Classification Policy
- Link: https://github.com/Hack23/ISMS-PUBLIC/blob/main/Data_Classification_Policy.md
- Application: Election forecasts classified as PUBLIC, polling models as INTERNAL
Privacy Policy
- Link: https://github.com/Hack23/ISMS-PUBLIC/blob/main/Privacy_Policy.md
- Application: No individual voter data processed, only aggregate statistics
AI Policy
- Link: https://github.com/Hack23/ISMS-PUBLIC/blob/main/AI_Policy.md
- Application: Election models follow AI transparency requirements
Threat Modeling
- Link: https://github.com/Hack23/ISMS-PUBLIC/blob/main/Threat_Modeling.md
- Application: Foreign interference scenarios modeled, mitigations documented
References
Official Documentation:
- Valmyndigheten (Swedish Election Authority): https://www.val.se/
- Riksdag Election Statistics: https://www.riksdagen.se/sv/sa-funkar-riksdagen/demokrati/val/
CIA Platform Documentation:
- Data Analysis: DATA_ANALYSIS_INTOP_OSINT.md
- Database Views: DATABASE_VIEW_INTELLIGENCE_CATALOG.md
- Risk Rules: RISK_RULES_INTOP_OSINT.md
Academic Sources:
- "Forecasting Elections" - Nate Silver (FiveThirtyEight methodology)
- "The Signal and the Noise" - Nate Silver (Bayesian forecasting)
- "Election Forecasting in Sweden" - Swedish National Election Studies
- "Modified Sainte-Laguë Method" - Electoral Studies Journal
Polling Organizations:
- Novus: https://novus.se/
- Sifo: https://www.kantarsifo.se/
- YouGov: https://yougov.se/
- Demoskop: https://www.demoskop.se/
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