electoral-analysis

name	electoral-analysis
description	Election forecasting models, campaign analysis, coalition prediction, voter behavior analysis for Swedish elections
license	Apache-2.0

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[] = np.exp(-df[] / )  
        df[] = np.sqrt(df[]) /   
        df[] = df[] * df[]
        
        
        aggregated = df.groupby().apply(
             x: np.average(x[], weights=x[])
        ).to_dict()
        
        
        standard_errors = df.groupby().apply(
             x: np.sqrt(np.average((x[] - aggregated[x.name])**, weights=x[]))
        ).to_dict()
        
        
        confidence_intervals = {
            party: {
                : aggregated[party],
                : aggregated[party] -  * standard_errors[party],
                : aggregated[party] +  * standard_errors[party]
            }
             party  .parties
        }
        
         confidence_intervals
    
     ():
        
        
        
        polls = .aggregate_polls_weighted()
        
        
        query = 
        
        economic_df = pd.read_sql(query, .db)
        economic_score = .calculate_economic_vote(economic_df)
        
        
        query_incumbent = 
        
        incumbency_df = pd.read_sql(query_incumbent, .db)
        incumbency_effects = .calculate_incumbency_penalty(incumbency_df)
        
        
        days_until_election = (election_date - datetime.now()).days
        campaign_factor =   days_until_election <     
        
        
        forecasts = {}
         party  .parties:
            poll_component = polls[party][] *  * campaign_factor
            economic_component = economic_score.get(party, ) * 
            incumbency_component = incumbency_effects.get(party, ) * 
            
            forecast = poll_component + economic_component + incumbency_component
            
            
            forecasts[party] = (, forecast)
        
        
        total = (forecasts.values())
        forecasts = {party: (vote / total) *   party, vote  forecasts.items()}
        
         forecasts
    
     ():
        
        
        gdp_growth = economic_df[economic_df[] == ][].iloc[]
        unemployment = economic_df[economic_df[] == ][].iloc[]
        inflation = economic_df[economic_df[] == ][].iloc[]
        
        
        economic_advantage = ( * gdp_growth) - ( * unemployment) - ( * inflation)
        
        
        query = 
        incumbent_parties = pd.read_sql(query, .db)[].tolist()
        
        
        economic_scores = {}
         party  .parties:
             party  incumbent_parties:
                economic_scores[party] = economic_advantage / (incumbent_parties)
            :
                economic_scores[party] = 
        
         economic_scores
    
     ():
        
        
        penalties = {}
         _, row  incumbency_df.iterrows():
             row[]:
                penalty = (- * row[], -)
                penalties[row[]] = penalty
            :
                penalties[row[]] = 
        
         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=):
    
    
    seat_simulations = {party: []  party  .parties}
    
     _  (n_simulations):
        
        sampled_votes = {}
         party  .parties:
            mean = vote_forecasts[party][]
            std = (vote_forecasts[party][] - vote_forecasts[party][]) / ( * )
            
            
            sampled_votes[party] = (, np.random.normal(mean, std))
        
        
        total = (sampled_votes.values())
        sampled_votes = {party: (vote / total) *   party, vote  sampled_votes.items()}
        
        
        :
            seats = .project_riksdag_seats(sampled_votes)
             party  .parties:
                seat_simulations[party].append(seats.get(party, ))
         ValueError:
            
            
    
    
    seat_projections = {}
     party  .parties:
        sims = seat_simulations[party]
        seat_projections[party] = {
            : (np.median(sims)),
            : np.mean(sims),
            : (np.percentile(sims, )),
            : (np.percentile(sims, )),
            : (s >   s  sims) / (sims)
        }
    
     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) + ):
             combo  combinations(parties, r):
                total_seats = (seat_projections[p][]  p  combo)
                
                
                 total_seats >= :
                    
                    compatible = 
                     p1, p2  combinations(combo, ):
                         (p1, p2)  incompatible_pairs  (p2, p1)  incompatible_pairs:
                            compatible = 
                            
                    
                     compatible:
                        viable_coalitions.append({
                            : combo,
                            : total_seats,
                            : (combo)
                        })
        
         viable_coalitions
    
     ():
        
        
        query = 
        
        alignment_df = pd.read_sql(query, .db)
        
        
        stability_score = alignment_df[].mean() * 
        
         stability_score
    
     ():
        
        
        coalition_scores = []
        
         coalition  viable_coalitions:
            parties = coalition[]
            seats = coalition[]
            size = coalition[]
            
            
            seat_surplus = seats - 
            seat_score = (seat_surplus / , ) *   
            
            
            size_score = (,  - (size - ) * )  
            
            
            stability = .calculate_coalition_stability(parties)
            stability_score = stability *   
            
            
            
            
             (parties).issubset({, , , }):
                ideology_score = 
             (parties).issubset({, , }):
                ideology_score = 
            :
                ideology_score =   
            
            total_score = seat_score + size_score + stability_score + ideology_score
            
            coalition_scores.append({
                : .join(parties),
                : parties,
                : seats,
                : total_score,
                : {
                    : seat_score,
                    : size_score,
                    : stability_score,
                    : ideology_score
                }
            })
        
        
        total_score = (c[]  c  coalition_scores)
         coalition  coalition_scores:
            coalition[] = (coalition[] / total_score) * 
        
        
        coalition_scores.sort(key= x: x[], reverse=)
        
         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 >   
         
      district_classification
 constituency_volatility_score
  avg_swing 
LIMIT ;

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_description,
            : event_impact,  
            : trend_change,      
            : ._calculate_p_value(model, X, y)
        }
    
     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 
         avg_support    
         avg_support    
         avg_support    
         
      threshold_risk,
    
    ROUND(
          (  stats.norm.cdf(, avg_support, support_volatility)), 
        
    )  probability_exceeds_threshold
 threshold_analysis
 avg_support    
  avg_support ;

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/