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// SPDX-License-Identifier: Apache-2.0
/**
* @module Utils/IntelligenceAnalysis
* @description Pure intelligence analysis utility functions for structured
* assessment of European Parliament data. All functions are stateless and
* safely handle malformed or missing MCP data. No side effects.
*/
import { escapeHTML } from './file-utils.js';
import type {
VotingAnomalyIntelligence,
CoalitionIntelligence,
MEPInfluenceScore,
LegislativeVelocity,
StakeholderPerspective,
StakeholderOutcomeMatrix,
AnalysisStakeholderType,
VotingRecord,
VotingPattern,
VotingIntensity,
CoalitionShift,
PolarizationIndex,
VotingTrend,
CoalitionStabilityReport,
LegislativeVelocityReport,
LegislativeDocument,
} from '../types/index.js';
import { ALL_STAKEHOLDER_TYPES } from '../types/index.js';
// ─── Internal type aliases ────────────────────────────────────────────────────
type SignificanceLevel = VotingAnomalyIntelligence['significance'];
type RiskLevel = CoalitionIntelligence['riskLevel'];
type AlignmentTrend = CoalitionIntelligence['alignmentTrend'];
// ─── Validation constants ─────────────────────────────────────────────────────
/** Valid significance levels in descending priority order */
const SIGNIFICANCE_LEVELS: readonly string[] = ['critical', 'high', 'medium', 'low'];
/** Valid risk levels for coalition and bottleneck assessment */
const RISK_LEVELS: readonly string[] = ['high', 'medium', 'low'];
/** Valid alignment trend values */
const ALIGNMENT_TRENDS: readonly string[] = ['strengthening', 'weakening', 'stable'];
/** Priority weights for significance-based ranking */
const SIGNIFICANCE_WEIGHTS: Readonly<Record<string, number>> = {
critical: 4,
high: 3,
medium: 2,
low: 1,
};
// ─── Private value-extraction helpers ────────────────────────────────────────
/**
* Safely extract a string from an unknown field value
*
* @param val - Unknown value to coerce
* @returns The string value or empty string if not a string
*/
function asStr(val: unknown): string {
return typeof val === 'string' ? val : '';
}
/**
* Safely extract a finite number from an unknown field value
*
* @param val - Unknown value to coerce
* @param fallback - Value returned when input is not a finite number
* @returns Finite number or fallback
*/
function asNum(val: unknown, fallback = 0): number {
return typeof val === 'number' && Number.isFinite(val) ? val : fallback;
}
/**
* Safely extract an array of strings from an unknown field value
*
* @param val - Unknown value to coerce
* @returns Array of strings (empty array if input is not an array)
*/
function asStrArr(val: unknown): string[] {
if (!Array.isArray(val)) return [];
return (val as unknown[]).filter((v): v is string => typeof v === 'string');
}
/**
* Coerce an unknown value to a non-null Record or return null
*
* @param input - Value to cast
* @returns Record or null for null/undefined/non-object input
*/
function toRecord(input: unknown): Record<string, unknown> | null {
if (input === null || input === undefined || typeof input !== 'object') return null;
return input as Record<string, unknown>;
}
// ─── Private parsing validators ───────────────────────────────────────────────
/**
* Validate and normalise a raw significance level string
*
* @param raw - Raw string from MCP data
* @returns Validated SignificanceLevel, defaulting to 'low'
*/
function parseSignificance(raw: string): SignificanceLevel {
const lower = raw.toLowerCase();
return SIGNIFICANCE_LEVELS.includes(lower) ? (lower as SignificanceLevel) : 'low';
}
/**
* Validate and normalise a raw risk level string
*
* @param raw - Raw string from MCP data
* @returns Validated RiskLevel, defaulting to 'medium'
*/
function parseRiskLevel(raw: string): RiskLevel {
const lower = raw.toLowerCase();
return RISK_LEVELS.includes(lower) ? (lower as RiskLevel) : 'medium';
}
/**
* Validate and normalise a raw alignment trend string
*
* @param raw - Raw string from MCP data
* @returns Validated AlignmentTrend, defaulting to 'stable'
*/
function parseAlignmentTrend(raw: string): AlignmentTrend {
const lower = raw.toLowerCase();
return ALIGNMENT_TRENDS.includes(lower) ? (lower as AlignmentTrend) : 'stable';
}
// ─── Exported intelligence functions ─────────────────────────────────────────
/**
* Parse and score a voting anomaly from raw MCP data.
* Returns null for null, undefined, non-object, or inputs missing a valid
* anomaly identifier.
*
* @param rawAnomaly - Raw MCP anomaly data (unknown shape)
* @returns Structured VotingAnomalyIntelligence or null if input is invalid
*/
export function scoreVotingAnomaly(rawAnomaly: unknown): VotingAnomalyIntelligence | null {
const a = toRecord(rawAnomaly);
if (!a) return null;
const anomalyId = asStr(a['anomalyId']) || asStr(a['id']);
if (!anomalyId) return null;
return {
anomalyId,
significance: parseSignificance(asStr(a['significance'])),
description: asStr(a['description']),
affectedGroups: asStrArr(a['affectedGroups']),
deviationPercentage: asNum(a['deviationPercentage']),
historicalContext: asStr(a['historicalContext']),
implication: asStr(a['implication']),
};
}
/**
* Analyse a coalition's cohesion from raw MCP coalition data.
* Returns null for null, undefined, non-object, or inputs missing a valid
* coalition identifier.
*
* @param rawCoalition - Raw MCP coalition data (unknown shape)
* @returns Structured CoalitionIntelligence or null if input is invalid
*/
export function analyzeCoalitionCohesion(rawCoalition: unknown): CoalitionIntelligence | null {
const c = toRecord(rawCoalition);
if (!c) return null;
const coalitionId = asStr(c['coalitionId']) || asStr(c['id']);
if (!coalitionId) return null;
return {
coalitionId,
groups: asStrArr(c['groups']),
cohesionScore: Math.min(1, Math.max(0, asNum(c['cohesionScore']))),
alignmentTrend: parseAlignmentTrend(asStr(c['alignmentTrend'])),
keyVotes: Math.max(0, Math.round(asNum(c['keyVotes']))),
riskLevel: parseRiskLevel(asStr(c['riskLevel'])),
};
}
/**
* Extract and score MEP influence from raw MCP influence data.
* Returns null for null, undefined, non-object, or inputs missing both a
* valid MEP identifier and display name.
*
* @param rawInfluence - Raw MCP MEP influence data (unknown shape)
* @returns Structured MEPInfluenceScore or null if input is invalid
*/
export function scoreMEPInfluence(rawInfluence: unknown): MEPInfluenceScore | null {
const m = toRecord(rawInfluence);
if (!m) return null;
const mepId = asStr(m['mepId']) || asStr(m['id']);
const mepName = asStr(m['mepName']) || asStr(m['name']);
if (!mepId || !mepName) return null;
return {
mepId,
mepName,
overallScore: Math.min(100, Math.max(0, asNum(m['overallScore']))),
votingActivity: Math.min(100, Math.max(0, asNum(m['votingActivity']))),
legislativeOutput: Math.min(100, Math.max(0, asNum(m['legislativeOutput']))),
committeeEngagement: Math.min(100, Math.max(0, asNum(m['committeeEngagement']))),
rank: asStr(m['rank']),
};
}
/**
* Calculate legislative velocity from raw MCP procedure data.
* Returns null for null, undefined, non-object, or inputs missing a valid
* procedure identifier or title.
*
* @param rawProcedure - Raw MCP procedure data (unknown shape)
* @returns Structured LegislativeVelocity or null if input is invalid
*/
export function calculateLegislativeVelocity(rawProcedure: unknown): LegislativeVelocity | null {
const p = toRecord(rawProcedure);
if (!p) return null;
const procedureId = asStr(p['procedureId']) || asStr(p['id']);
const title = asStr(p['title']);
if (!procedureId || !title) return null;
return {
procedureId,
title,
stage: asStr(p['stage']) || 'Unknown',
daysInCurrentStage: Math.max(0, Math.round(asNum(p['daysInCurrentStage']))),
velocityScore: Math.min(1, Math.max(0, asNum(p['velocityScore']))),
bottleneckRisk: parseRiskLevel(asStr(p['bottleneckRisk'])),
predictedCompletion: asStr(p['predictedCompletion']),
};
}
/**
* Sort items by significance level descending, with numeric score as
* tie-breaker. Items with higher significance or scores appear first.
* The original array is not mutated.
*
* @param items - Array of items with optional significance and score fields
* @returns New sorted array ordered by significance then score
*/
export function rankBySignificance<
T extends {
significance?: string | undefined;
overallScore?: number | undefined;
cohesionScore?: number | undefined;
},
>(items: T[]): T[] {
return [...items].sort((a, b) => {
const sigA = SIGNIFICANCE_WEIGHTS[a.significance ?? ''] ?? 0;
const sigB = SIGNIFICANCE_WEIGHTS[b.significance ?? ''] ?? 0;
if (sigA !== sigB) return sigB - sigA;
const scoreA = a.overallScore ?? a.cohesionScore ?? 0;
const scoreB = b.overallScore ?? b.cohesionScore ?? 0;
return scoreB - scoreA;
});
}
/**
* Build an HTML section element for displaying intelligence items as a list.
* All title, className, and item strings are HTML-escaped to prevent XSS.
* Returns an empty string when the items array is empty.
*
* @param title - Section heading text (will be HTML-escaped)
* @param items - Array of text items to display as list entries (will be HTML-escaped)
* @param className - CSS class name for the section element (will be HTML-escaped)
* @returns HTML string for the intelligence section, or empty string if no items
*/
export function buildIntelligenceSection(
title: string,
items: string[],
className: string
): string {
if (items.length === 0) return '';
const safeClass = escapeHTML(className);
const safeTitle = escapeHTML(title);
const itemsHtml = items.map((item) => `<li>${escapeHTML(item)}</li>`).join('\n ');
return `<section class="${safeClass}">
<h2>${safeTitle}</h2>
<ul>
${itemsHtml}
</ul>
</section>`;
}
// ─── Stakeholder scoring functions ───────────────────────────────────────────
/**
* Derive a severity level from a numeric 0-1 importance score.
*
* @param score - Normalised importance score (0 = least important, 1 = most)
* @returns Severity level
*/
function severityFromScore(score: number): StakeholderPerspective['severity'] {
if (score >= 0.7) return 'high';
if (score >= 0.4) return 'medium';
return 'low';
}
/**
* Build a default set of stakeholder perspectives for a parliamentary action.
* Each perspective is seeded with a reasoning string and evidence items derived
* from the provided topic and impact scores. All six stakeholder groups receive
* a perspective entry.
*
* @param topic - Short description of the parliamentary action (e.g. vote title)
* @param scores - Optional per-stakeholder importance scores (0-1); defaults to 0.5
* @returns Array of six StakeholderPerspective objects, one per stakeholder group
*/
export function buildDefaultStakeholderPerspectives(
topic: string,
scores?: Partial<Record<AnalysisStakeholderType, number>>
): StakeholderPerspective[] {
return ALL_STAKEHOLDER_TYPES.map((stakeholder) => {
const score = scores?.[stakeholder] ?? 0.5;
const severity = severityFromScore(score);
return {
stakeholder,
impact:
score >= 0.6
? ('positive' as const)
: score <= 0.3
? ('negative' as const)
: ('neutral' as const),
severity,
reasoning: `Impact on this stakeholder group: ${severity} significance based on "${topic}".`,
evidence: [topic],
};
});
}
/**
* Score stakeholder influence from raw MCP data.
* Returns null for null, undefined, non-object, or missing stakeholder type.
*
* @param rawData - Raw stakeholder influence data (unknown shape)
* @returns Structured StakeholderPerspective or null if input is invalid
*/
export function scoreStakeholderInfluence(rawData: unknown): StakeholderPerspective | null {
const d = toRecord(rawData);
if (!d) return null;
const stakeholderRaw = asStr(d['stakeholder']);
if (!(ALL_STAKEHOLDER_TYPES as readonly string[]).includes(stakeholderRaw)) return null;
const stakeholder = stakeholderRaw as AnalysisStakeholderType;
const impactRaw = asStr(d['impact']).toLowerCase();
const validImpacts = ['positive', 'negative', 'neutral', 'mixed'] as const;
const impact = (validImpacts as readonly string[]).includes(impactRaw)
? (impactRaw as StakeholderPerspective['impact'])
: 'neutral';
const severityRaw = asStr(d['severity']).toLowerCase();
const validSeverities = ['high', 'medium', 'low'] as const;
const severity = (validSeverities as readonly string[]).includes(severityRaw)
? (severityRaw as StakeholderPerspective['severity'])
: 'medium';
return {
stakeholder,
impact,
severity,
reasoning: asStr(d['reasoning']),
evidence: asStrArr(d['evidence']),
};
}
/**
* Build a StakeholderOutcomeMatrix row for a single parliamentary action.
* Derives outcomes from per-stakeholder scores: score > 0.6 → winner,
* score < 0.4 → loser, otherwise neutral.
*
* @param action - The parliamentary action being assessed
* @param scores - Per-stakeholder importance scores (0-1); defaults to 0.5
* @param confidence - Confidence level for the outcome assessments
* @returns A StakeholderOutcomeMatrix row
*/
export function buildStakeholderOutcomeMatrix(
action: string,
scores: Partial<Record<AnalysisStakeholderType, number>> = {},
confidence: StakeholderOutcomeMatrix['confidence'] = 'medium'
): StakeholderOutcomeMatrix {
const outcomes = Object.fromEntries(
ALL_STAKEHOLDER_TYPES.map((stakeholder) => {
const score = scores[stakeholder] ?? 0.5;
const outcome: 'winner' | 'loser' | 'neutral' =
score > 0.6 ? 'winner' : score < 0.4 ? 'loser' : 'neutral';
return [stakeholder, outcome];
})
) as Record<AnalysisStakeholderType, 'winner' | 'loser' | 'neutral'>;
return { action, outcomes, confidence };
}
/**
* Map an array of StakeholderPerspective objects to a simple influence ranking.
* Returns stakeholder types sorted by severity (high → medium → low), then by
* impact direction (negative before positive, as negative impacts require more
* political attention).
*
* @param perspectives - Array of stakeholder perspectives to rank
* @returns Stakeholder types sorted by influence priority
*/
export function rankStakeholdersByInfluence(
perspectives: readonly StakeholderPerspective[]
): AnalysisStakeholderType[] {
const severityWeight: Record<StakeholderPerspective['severity'], number> = {
high: 3,
medium: 2,
low: 1,
};
const impactWeight: Record<StakeholderPerspective['impact'], number> = {
negative: 3,
mixed: 2,
positive: 1,
neutral: 0,
};
return [...perspectives]
.sort((a, b) => {
const sw = severityWeight[b.severity] - severityWeight[a.severity];
if (sw !== 0) return sw;
const iw = impactWeight[b.impact] - impactWeight[a.impact];
Eif (iw !== 0) return iw;
// Deterministic tie-breaker: canonical ALL_STAKEHOLDER_TYPES order
return (
ALL_STAKEHOLDER_TYPES.indexOf(a.stakeholder) - ALL_STAKEHOLDER_TYPES.indexOf(b.stakeholder)
);
})
.map((p) => p.stakeholder);
}
// ─── Advanced political intelligence functions ──────────────────────────────
/**
* Compute voting intensity metrics from a set of voting records.
* Analyses the distribution of for/against/abstain votes to determine
* unanimity, polarization, and margin characteristics.
*
* @param records - Voting records to analyse
* @returns VotingIntensity metrics, or null if the records array is empty or contains no
* valid vote counts (for example, when all records have a total vote count of 0)
*/
export function computeVotingIntensity(records: readonly VotingRecord[]): VotingIntensity | null {
if (records.length === 0) return null;
let totalUnanimity = 0;
let totalPolarization = 0;
let totalMargin = 0;
let closeVoteCount = 0;
let decisiveVoteCount = 0;
let validCount = 0;
let polarizationCount = 0;
for (const record of records) {
const total = record.votes.for + record.votes.against + record.votes.abstain;
if (total === 0) continue;
validCount++;
const forPct = record.votes.for / total;
const againstPct = record.votes.against / total;
const abstainPct = record.votes.abstain / total;
const margin = Math.abs(forPct - againstPct);
// Largest-position share: max share among for/against/abstain
const maxPct = Math.max(forPct, againstPct, abstainPct);
totalUnanimity += maxPct;
// Margin, close/decisive, and polarization only meaningful when for+against > 0
const forAgainstTotal = record.votes.for + record.votes.against;
if (forAgainstTotal > 0) {
polarizationCount++;
const balance = Math.min(record.votes.for, record.votes.against) / forAgainstTotal;
totalPolarization += balance * 2; // normalise: 0 = one-sided, 1 = perfectly split
totalMargin += margin;
if (margin < 0.1) closeVoteCount++;
if (margin > 0.6) decisiveVoteCount++;
}
}
if (validCount === 0) return null;
return {
unanimity: Math.round((totalUnanimity / validCount) * 100) / 100,
polarization:
polarizationCount > 0 ? Math.round((totalPolarization / polarizationCount) * 100) / 100 : 0,
averageMargin:
polarizationCount > 0 ? Math.round((totalMargin / polarizationCount) * 100) / 100 : 0,
closeVoteCount,
decisiveVoteCount,
};
}
/**
* Detect coalition shifts by comparing current cohesion patterns against
* a baseline. Stability threshold is ±5%; severity tiers are >5% (medium),
* >10% (high), and >20% (critical).
*
* @param currentPatterns - Current period voting patterns
* @param baselinePatterns - Previous period patterns (or estimated baseline)
* @returns Array of detected coalition shifts, sorted by significance
*/
export function detectCoalitionShifts(
currentPatterns: readonly VotingPattern[],
baselinePatterns: readonly VotingPattern[]
): CoalitionShift[] {
const baselineMap = new Map<string, number>();
for (const bp of baselinePatterns) {
baselineMap.set(bp.group, bp.cohesion);
}
const shifts: CoalitionShift[] = [];
for (const current of currentPatterns) {
const previous = baselineMap.get(current.group) ?? current.cohesion;
const delta = current.cohesion - previous;
const absDelta = Math.abs(delta);
let direction: CoalitionShift['direction'];
if (delta > 0.05) direction = 'strengthening';
else if (delta < -0.05) direction = 'weakening';
else direction = 'stable';
let significance: CoalitionShift['significance'];
if (absDelta > 0.2) significance = 'critical';
else Iif (absDelta > 0.1) significance = 'high';
else if (absDelta > 0.05) significance = 'medium';
else significance = 'low';
shifts.push({
group: current.group,
previousCohesion: Math.round(previous * 100) / 100,
currentCohesion: Math.round(current.cohesion * 100) / 100,
cohesionDelta: Math.round(delta * 100) / 100,
direction,
significance,
});
}
// Sort by significance (critical first), then by absolute delta descending
const sigOrder: Record<string, number> = { critical: 4, high: 3, medium: 2, low: 1 };
return shifts.sort((a, b) => {
const sigDiff = (sigOrder[b.significance] ?? 0) - (sigOrder[a.significance] ?? 0);
Eif (sigDiff !== 0) return sigDiff;
return Math.abs(b.cohesionDelta) - Math.abs(a.cohesionDelta);
});
}
/**
* Classify cohesion groups into high-cohesion and fragmented categories.
*
* @param patterns - Voting patterns
* @returns Tuple of [highCohesionGroups, fragmentedGroups]
*/
function classifyCohesionGroups(patterns: readonly VotingPattern[]): [string[], string[]] {
const high: string[] = [];
const fragmented: string[] = [];
for (const p of patterns) {
if (p.cohesion > 0.8) high.push(p.group);
if (p.cohesion < 0.5) fragmented.push(p.group);
}
return [high, fragmented];
}
/**
* Compute effective number of voting blocs using Laakso-Taagepera style.
*
* @param patterns - Voting patterns
* @returns Effective number of blocs
*/
function computeEffectiveBlocs(patterns: readonly VotingPattern[]): number {
let totalParticipation = 0;
for (const p of patterns) totalParticipation += p.participation;
Iif (totalParticipation <= 0) return patterns.length;
let sumSquares = 0;
for (const p of patterns) {
const share = p.participation / totalParticipation;
sumSquares += share * share;
}
return sumSquares > 0 ? 1 / sumSquares : patterns.length;
}
/**
* Map an overall index to a polarization assessment label.
*
* @param index - Polarization index (0-1)
* @returns Assessment label
*/
function assessPolarization(index: number): PolarizationIndex['assessment'] {
if (index >= 0.75) return 'highly-polarized';
if (index >= 0.5) return 'polarized';
Iif (index >= 0.25) return 'moderate';
return 'consensus';
}
/**
* Compute a polarization index for a parliamentary period based on
* voting pattern cohesion data. Uses a Laakso-Taagepera–inspired
* "effective number of blocs" calculation alongside cohesion analysis.
*
* @param patterns - Voting patterns for the period
* @returns PolarizationIndex assessment, or null if patterns are empty
*/
export function computePolarizationIndex(
patterns: readonly VotingPattern[]
): PolarizationIndex | null {
if (patterns.length === 0) return null;
const [highCohesionGroups, fragmentedGroups] = classifyCohesionGroups(patterns);
const effectiveBlocs = computeEffectiveBlocs(patterns);
const extremeCount = highCohesionGroups.length + fragmentedGroups.length;
const overallIndex = Math.round((extremeCount / patterns.length) * 100) / 100;
return {
overallIndex,
effectiveBlocs: Math.round(effectiveBlocs * 100) / 100,
highCohesionGroups,
fragmentedGroups,
assessment: assessPolarization(overallIndex),
};
}
// ─── Cross-session analysis functions ─────────────────────────────────────────
/**
* Compute average of a numeric array.
*
* @param values - Array of numbers
* @returns Arithmetic mean, or 0 for empty arrays
*/
function avg(values: readonly number[]): number {
return values.length > 0 ? values.reduce((s, v) => s + v, 0) / values.length : 0;
}
/**
* Extract valid vote margins and result tallies from voting records.
* Skips records with missing/malformed vote data, non-finite or negative
* vote counts, or where for + against is zero (abstain-only votes) to avoid
* skewing margin and polarization calculations. Only records where
* `votes.for` and `votes.against` are finite, non-negative numbers are used;
* numeric-string encodings and other non-numeric values are ignored.
*
* @param records - Voting records to process
* @returns Object containing margins array and per-record result classifications
*/
function extractMarginData(records: readonly VotingRecord[]): {
margins: number[];
results: Array<'adopted' | 'rejected' | 'other'>;
} {
const margins: number[] = [];
const results: Array<'adopted' | 'rejected' | 'other'> = [];
for (const r of records) {
const votes = r.votes;
if (!votes || typeof votes !== 'object') continue;
// Require actual finite numbers — asNum() would silently map non-numbers to 0,
// which would include malformed records and skew margins/polarization metrics.
const forCount = votes.for;
const againstCount = votes.against;
Iif (typeof forCount !== 'number' || !Number.isFinite(forCount) || forCount < 0) continue;
Iif (typeof againstCount !== 'number' || !Number.isFinite(againstCount) || againstCount < 0)
continue;
const forAgainstTotal = forCount + againstCount;
if (forAgainstTotal <= 0) continue;
margins.push(Math.abs(forCount - againstCount) / forAgainstTotal);
const result = asStr(r.result).toLowerCase();
if (result === 'adopted' || result === 'approved') {
results.push('adopted');
} else if (result === 'rejected') {
results.push('rejected');
} else E{
results.push('other');
}
}
return { margins, results };
}
/**
* Compute adoption rate from a results slice.
*
* @param results - Array of result classifications
* @returns Adoption rate (0-1), or 0 if no decided records
*/
function computeAdoptionRate(results: ReadonlyArray<'adopted' | 'rejected' | 'other'>): number {
const adopted = results.filter((r) => r === 'adopted').length;
const decided = results.filter((r) => r === 'adopted' || r === 'rejected').length;
return decided > 0 ? adopted / decided : 0;
}
/**
* Derive adoption-rate direction by comparing first-half and second-half rates.
*
* @param firstRate - Adoption rate of the first chronological half
* @param secondRate - Adoption rate of the second chronological half
* @returns Direction label based on delta between halves
*/
function adoptionDirection(firstRate: number, secondRate: number): VotingTrend['direction'] {
const delta = secondRate - firstRate;
if (delta > 0.05) return 'increasing';
if (delta < -0.05) return 'decreasing';
return 'stable';
}
/**
* Build a margin-shift trend if the delta exceeds 5%.
*
* @param firstHalf - Margins from the first half of records
* @param secondHalf - Margins from the second half of records
* @param total - Total number of valid records
* @returns VotingTrend or null if delta is within threshold
*/
function buildMarginTrend(
firstHalf: number[],
secondHalf: number[],
total: number
): VotingTrend | null {
const marginDelta = avg(secondHalf) - avg(firstHalf);
if (Math.abs(marginDelta) <= 0.05) return null;
const isIncreasing = marginDelta > 0;
return {
trendId: isIncreasing ? 'increasing-margins' : 'decreasing-margins',
description: isIncreasing
? 'Voting margins are widening — greater decisiveness'
: 'Voting margins are narrowing — increasing contention',
direction: isIncreasing ? 'increasing' : 'decreasing',
confidence: Math.min(1, Math.round(Math.abs(marginDelta) * 5 * 100) / 100),
recordCount: total,
metricValue: Math.round(marginDelta * 100) / 100,
};
}
/**
* Build a polarization trend if the close-vote frequency delta exceeds 10%.
*
* @param firstHalf - Margins from the first half of records
* @param secondHalf - Margins from the second half of records
* @param total - Total number of valid records
* @returns VotingTrend or null if delta is within threshold
*/
function buildPolarizationTrend(
firstHalf: number[],
secondHalf: number[],
total: number
): VotingTrend | null {
const closeFirst = firstHalf.filter((m) => m < 0.1).length / firstHalf.length;
const closeSecond = secondHalf.filter((m) => m < 0.1).length / secondHalf.length;
const closeDelta = closeSecond - closeFirst;
if (Math.abs(closeDelta) <= 0.1) return null;
const isIncreasing = closeDelta > 0;
return {
trendId: isIncreasing ? 'increasing-polarization' : 'decreasing-polarization',
description: isIncreasing
? 'More close votes detected — increasing polarization'
: 'Fewer close votes — declining polarization',
direction: isIncreasing ? 'increasing' : 'decreasing',
confidence: Math.min(1, Math.round(Math.abs(closeDelta) * 3 * 100) / 100),
recordCount: total,
metricValue: Math.round(closeDelta * 100) / 100,
};
}
/**
* Detect voting trends across multiple voting records by analysing
* margin distribution, polarization patterns, and result consistency.
* Records are sorted by date (ascending) before analysis to ensure
* chronological trend detection. Returns an array of detected trends
* sorted by confidence.
*
* @param records - Voting records to analyse across sessions
* @returns Array of detected VotingTrend objects (empty if fewer than 2 valid records)
*/
export function detectVotingTrends(records: readonly VotingRecord[]): VotingTrend[] {
if (records.length < 2) return [];
const toTimestamp = (d: string | undefined): number => {
const t = Date.parse(d ?? '');
return Number.isFinite(t) ? t : Infinity;
};
const sorted = [...records].sort((a, b) => {
const ta = toTimestamp(a.date);
const tb = toTimestamp(b.date);
if (ta === tb) return 0;
return ta < tb ? -1 : 1;
});
const { margins, results } = extractMarginData(sorted);
Iif (margins.length < 2) return [];
const mid = Math.floor(margins.length / 2);
const firstHalf = margins.slice(0, mid);
const secondHalf = margins.slice(mid);
const trends: VotingTrend[] = [];
const marginTrend = buildMarginTrend(firstHalf, secondHalf, margins.length);
if (marginTrend) trends.push(marginTrend);
const polTrend = buildPolarizationTrend(firstHalf, secondHalf, margins.length);
if (polTrend) trends.push(polTrend);
const firstResults = results.slice(0, mid);
const secondResults = results.slice(mid);
const totalDecided = results.filter((r) => r === 'adopted' || r === 'rejected').length;
Eif (totalDecided > 0) {
const overallRate = computeAdoptionRate(results);
const firstRate = computeAdoptionRate(firstResults);
const secondRate = computeAdoptionRate(secondResults);
trends.push({
trendId: 'adoption-rate',
description: `Adoption rate is ${Math.round(overallRate * 100)}% across ${totalDecided} decided votes`,
direction: adoptionDirection(firstRate, secondRate),
confidence: Math.min(1, Math.round((totalDecided / margins.length) * 100) / 100),
recordCount: totalDecided,
metricValue: Math.round(overallRate * 100) / 100,
});
}
return trends.sort((a, b) => b.confidence - a.confidence);
}
/**
* Compute cross-session coalition stability by analysing average cohesion
* across a set of voting patterns. Groups with high cohesion are reported
* as stable; those with low cohesion are flagged.
*
* @param patterns - Voting patterns from multiple sessions
* @returns CoalitionStabilityReport (empty report if no patterns)
*/
export function computeCrossSessionCoalitionStability(
patterns: readonly VotingPattern[]
): CoalitionStabilityReport {
if (patterns.length === 0) {
return {
overallStability: 0,
patternCount: 0,
stableGroups: [],
decliningGroups: [],
forecast: 'volatile',
};
}
// Aggregate cohesion per group, coercing and clamping to [0, 1]
const groupCohesions = new Map<string, number[]>();
let includedPatterns = 0;
for (const p of patterns) {
const groupKey = asStr(p.group).trim();
if (groupKey.length === 0) continue;
includedPatterns++;
const raw = asNum(p.cohesion);
const clamped = Math.max(0, Math.min(1, raw));
const existing = groupCohesions.get(groupKey);
if (existing) {
existing.push(clamped);
} else {
groupCohesions.set(groupKey, [clamped]);
}
}
const stableGroups: string[] = [];
const decliningGroups: string[] = [];
let totalAvgCohesion = 0;
let groupCount = 0;
for (const [group, cohesions] of groupCohesions) {
const avgCohesion = cohesions.reduce((s, v) => s + v, 0) / cohesions.length;
totalAvgCohesion += avgCohesion;
groupCount++;
if (avgCohesion >= 0.7) {
stableGroups.push(group);
} else if (avgCohesion < 0.5) {
decliningGroups.push(group);
}
}
const overallStability =
groupCount > 0 ? Math.round((totalAvgCohesion / groupCount) * 100) / 100 : 0;
let forecast: CoalitionStabilityReport['forecast'];
if (overallStability >= 0.7) forecast = 'stable';
else if (overallStability >= 0.5) forecast = 'at-risk';
else forecast = 'volatile';
return {
overallStability,
patternCount: includedPatterns,
stableGroups,
decliningGroups,
forecast,
};
}
/**
* Rank MEP influence scores filtered by topic relevance.
* Matches scores whose mepName, mepId, or rank contains the topic substring
* (case-insensitive). Returns the filtered list sorted by overallScore
* descending. If topic is empty or no matches found, returns all scores
* sorted by overallScore.
*
* @param scores - MEP influence scores to rank
* @param topic - Topic keyword to filter by; if null/undefined/empty, all
* scores are returned sorted by overallScore
* @returns Sorted array of matching MEPInfluenceScore entries
*/
export function rankMEPInfluenceByTopic(
scores: readonly MEPInfluenceScore[],
topic: string | null | undefined
): MEPInfluenceScore[] {
if (scores.length === 0) return [];
const lowerTopic = String(topic ?? '')
.toLowerCase()
.trim();
const getSafeScore = (entry: MEPInfluenceScore): number => {
const raw = asNum(entry.overallScore);
return Number.isFinite(raw) ? raw : 0;
};
// If topic is empty, return all sorted by score
if (lowerTopic.length === 0) {
return [...scores].sort((a, b) => getSafeScore(b) - getSafeScore(a));
}
const matched = scores.filter((s) => {
const safeName = typeof s.mepName === 'string' ? s.mepName.toLowerCase() : '';
const safeRank = typeof s.rank === 'string' ? s.rank.toLowerCase() : '';
const safeId = typeof s.mepId === 'string' ? s.mepId.toLowerCase() : '';
return (
safeName.includes(lowerTopic) || safeRank.includes(lowerTopic) || safeId.includes(lowerTopic)
);
});
// If no matches, return all sorted
const pool = matched.length > 0 ? matched : [...scores];
return pool.sort((a, b) => getSafeScore(b) - getSafeScore(a));
}
/**
* Count stages whose document count exceeds 1.5× the average (bottlenecks).
*
* @param stageValues - Array of per-stage document counts
* @returns Number of bottleneck stages
*/
function countBottleneckStages(stageValues: readonly number[]): number {
Iif (stageValues.length === 0) return 0;
const avgPerStage = stageValues.reduce((s, v) => s + v, 0) / stageValues.length;
let count = 0;
for (const val of stageValues) {
if (val > avgPerStage * 1.5 && val > 1) count++;
}
return count;
}
/**
* Compute average days per stage from a set of valid timestamps and the
* number of stages. Returns 0 when fewer than 2 dates are available.
*
* @param dates - Array of valid timestamp numbers (ms since epoch)
* @param stageCount - Number of distinct stages
* @returns Estimated average days per stage (rounded)
*/
function computeDaysPerStage(dates: readonly number[], stageCount: number): number {
if (dates.length < 2 || stageCount <= 0) return 0;
let minDate = dates[0] as number;
let maxDate = dates[0] as number;
for (let i = 1; i < dates.length; i++) {
const current = dates[i] as number;
Iif (current < minDate) minDate = current;
Eif (current > maxDate) maxDate = current;
}
const spanDays = (maxDate - minDate) / (1000 * 60 * 60 * 24);
return Math.round(spanDays / stageCount);
}
/**
* Determine throughput assessment label based on date availability and
* average days per stage.
*
* @param hasDateData - Whether sufficient date data was available
* @param avgDays - Average days per stage
* @returns Throughput label: 'fast', 'normal', or 'slow'
*/
function assessThroughput(
hasDateData: boolean,
avgDays: number
): LegislativeVelocityReport['throughputAssessment'] {
if (!hasDateData) return 'normal';
if (avgDays <= 30) return 'fast';
if (avgDays <= 90) return 'normal';
return 'slow';
}
/**
* Build a legislative velocity report with stage-by-stage breakdown from
* a set of legislative documents. Analyses document status distribution
* and identifies potential bottlenecks.
*
* @param docs - Legislative documents to analyse
* @returns LegislativeVelocityReport summary
*/
export function buildLegislativeVelocityReport(
docs: readonly LegislativeDocument[]
): LegislativeVelocityReport {
if (docs.length === 0) {
return {
documentCount: 0,
stageBreakdown: Object.create(null) as Record<string, number>,
averageDaysPerStage: 0,
bottleneckCount: 0,
throughputAssessment: assessThroughput(false, 0),
};
}
const stageBreakdown: Record<string, number> = Object.create(null) as Record<string, number>;
for (const doc of docs) {
const status = typeof doc.status === 'string' ? doc.status.trim() : '';
const type = typeof doc.type === 'string' ? doc.type.trim() : '';
const rawStage = status || type || 'Unknown';
const stage =
rawStage === '__proto__' || rawStage === 'constructor' || rawStage === 'prototype'
? 'Unknown'
: rawStage;
stageBreakdown[stage] = (stageBreakdown[stage] ?? 0) + 1;
}
const bottleneckCount = countBottleneckStages(Object.values(stageBreakdown));
const dates = docs
.map((d) => (d.date ? new Date(d.date).getTime() : NaN))
.filter((t) => !Number.isNaN(t));
const hasDateData = dates.length >= 2;
const averageDaysPerStage = computeDaysPerStage(dates, Object.keys(stageBreakdown).length);
return {
documentCount: docs.length,
stageBreakdown,
averageDaysPerStage,
bottleneckCount,
throughputAssessment: assessThroughput(hasDateData, averageDaysPerStage),
};
}
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