Game balance audit: wire research effects, rework capability formula, fix dead systems
CI / build-and-push (push) Successful in 32s
CI / build-and-push (push) Successful in 32s
- Create researchBonuses utility to aggregate tech tree effects into all game systems (infrastructure energy costs, compute efficiency, training speed, model capability, reputation) - Rework model capability from sqrt(compute) to 4-pillar formula (params + compute + data + research) - Make context window affect benchmarks and inference speed - Add MoE tradeoffs: 1.5x VRAM, 0.8x training speed - Enforce research point costs as a gate for unlocking research - Add real consequences to data contamination events (reputation hit, legal costs) - Scale talent costs from $0.03 to $5/tick per headcount - Scale compliance costs 100x to be meaningful - Rework competitor acquisition: cheaper but grants headcount, RP, and reputation - Remove dead code: sfxVolume, autoSaveInterval, notificationsEnabled, FAST_FORWARD_BATCH_SIZE, CHINCHILLA_OPTIMAL_RATIO Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
@@ -2,6 +2,8 @@ export { GameEngine } from './engine';
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export { processTick, setAchievementDefinitions } from './tick';
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export type { TickNotification } from './tick';
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export { getAvailableResearch, getResearchNode } from './systems/researchSystem';
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export { getResearchBonuses } from './systems/researchBonuses';
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export type { ResearchBonuses } from './systems/researchBonuses';
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export { emptyDCNetworkSummary, emptyCampusNetworkSummary, emptyClusterNetworkSummary } from './systems/infrastructureSystem';
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export { canRaiseFunding, getNextFundingRound, computeValuation } from './systems/fundingSystem';
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export { TECH_TREE } from './data/techTree';
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@@ -1,5 +1,6 @@
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import type { GameState, ComputeState, InfrastructureState } from '@ai-tycoon/shared';
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import { FLOPS_TO_TOKENS_MULTIPLIER } from '@ai-tycoon/shared';
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import type { ResearchBonuses } from './researchBonuses';
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export interface CapacityResult {
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totalFlops: number;
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@@ -13,20 +14,19 @@ export interface CapacityResult {
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tokensPerSecondCapacity: number;
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}
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export function computeCapacity(state: GameState, infrastructure: InfrastructureState): CapacityResult {
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export function computeCapacity(state: GameState, infrastructure: InfrastructureState, researchBonuses?: ResearchBonuses): CapacityResult {
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const { totalTrainingFlops, totalInferenceFlops, totalVramGB } = infrastructure;
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const trainingAllocation = state.compute.trainingAllocation;
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const inferenceAllocation = 1 - trainingAllocation;
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// Training hardware can do inference at ~50% efficiency
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// Inference hardware can do training at ~30% efficiency (no NVLink, poor scaling)
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const effectiveTrainingFlops =
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totalTrainingFlops * trainingAllocation +
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totalInferenceFlops * trainingAllocation * 0.3;
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const inferenceBoost = 1 + (researchBonuses?.tokensPerFlopBonus ?? 0) + (researchBonuses?.inferenceEfficiencyBonus ?? 0);
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const effectiveInferenceFlops =
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totalInferenceFlops * inferenceAllocation +
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totalTrainingFlops * inferenceAllocation * 0.5;
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(totalInferenceFlops * inferenceAllocation +
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totalTrainingFlops * inferenceAllocation * 0.5) * inferenceBoost;
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const tokensPerSecondCapacity = effectiveInferenceFlops * FLOPS_TO_TOKENS_MULTIPLIER;
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@@ -24,6 +24,7 @@ import {
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estimateNetworkSlots,
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} from '@ai-tycoon/shared';
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import type { TickNotification } from '../tick';
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import type { ResearchBonuses } from './researchBonuses';
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export interface InfraTickResult {
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infrastructure: InfrastructureState;
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@@ -463,7 +464,7 @@ function computeInterconnectMultiplier(
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// --- Main Infrastructure Tick ---
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export function processInfrastructure(state: GameState): InfraTickResult {
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export function processInfrastructure(state: GameState, researchBonuses?: ResearchBonuses): InfraTickResult {
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const notifications: TickNotification[] = [];
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let repairCosts = 0;
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@@ -587,7 +588,9 @@ export function processInfrastructure(state: GameState): InfraTickResult {
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continue;
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}
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const speed = stageSpeed(cohort.stage, engEff, opsEff);
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const baseSpeed = stageSpeed(cohort.stage, engEff, opsEff);
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const pipelineBonus = cohort.stage !== 'repair' ? (researchBonuses?.pipelineSpeedBonus ?? 0) : 0;
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const speed = baseSpeed * (1 + pipelineBonus);
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const newProgress = cohort.stageProgress + speed;
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if (newProgress < cohort.stageTotal) {
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@@ -728,8 +731,9 @@ export function processInfrastructure(state: GameState): InfraTickResult {
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}
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const pue = COOLING_TYPE_CONFIGS[dc.coolingType].pueMultiplier;
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const energyReduction = researchBonuses?.energyCostReduction ?? 0;
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const energyCostPerTick = (tierConfig.baseEnergyCostPerTick + usedPowerKW * BASE_ENERGY_COST_PER_FLOP)
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* location.energyCostMultiplier * pue;
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* location.energyCostMultiplier * pue * (1 - energyReduction);
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const maintenanceCostPerTick = totalRacksInDc * BASE_MAINTENANCE_PER_RACK;
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const currentUptime = totalRacksInDc > 0 ? effectiveComputeRacks / totalRacksInDc : 1;
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@@ -21,16 +21,21 @@ import {
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DISTILLATION_BASE_RETENTION,
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QUANTIZATION_TICKS,
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} from '@ai-tycoon/shared';
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import type { ResearchBonuses } from './researchBonuses';
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export interface ModelTickResult {
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modelsState: ModelsState;
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completedModels: BaseModel[];
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notifications: { title: string; message: string; type: 'success' | 'warning' | 'info' }[];
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notifications: { title: string; message: string; type: 'success' | 'warning' | 'info' | 'danger' }[];
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reputationHit: number;
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legalCosts: number;
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}
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export function processModels(state: GameState): ModelTickResult {
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export function processModels(state: GameState, researchBonuses?: ResearchBonuses): ModelTickResult {
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const completedModels: BaseModel[] = [];
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const notifications: ModelTickResult['notifications'] = [];
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let totalReputationHit = 0;
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let totalLegalCosts = 0;
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let baseModels = [...state.models.baseModels];
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let families = [...state.models.families];
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@@ -40,7 +45,8 @@ export function processModels(state: GameState): ModelTickResult {
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state.talent.departments.research.effectiveness;
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const engineerBoost = state.talent.departments.engineering.headcount *
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state.talent.departments.engineering.effectiveness;
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const speedMultiplier = 1 + (researcherBoost + engineerBoost) * 0.05;
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const trainingResearchBonus = researchBonuses?.trainingSpeedBonus ?? 0;
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const speedMultiplier = (1 + (researcherBoost + engineerBoost) * 0.05) * (1 + trainingResearchBonus);
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const updatedPipelines: TrainingPipeline[] = [];
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@@ -51,7 +57,8 @@ export function processModels(state: GameState): ModelTickResult {
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}
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const generation = families.find(f => f.id === pipeline.familyId)?.generation ?? 1;
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const requiredVram = VRAM_REQUIREMENTS_BY_GENERATION[generation] ?? 0;
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const moeVramMultiplier = pipeline.architecture.type === 'moe' ? 1.5 : 1.0;
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const requiredVram = (VRAM_REQUIREMENTS_BY_GENERATION[generation] ?? 0) * moeVramMultiplier;
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if (requiredVram > 0 && state.compute.totalVramGB < requiredVram) {
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updatedPipelines.push({ ...pipeline, status: 'stalled' });
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continue;
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@@ -62,7 +69,8 @@ export function processModels(state: GameState): ModelTickResult {
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if (pipeline.currentStage === 'pretraining') {
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const stage = { ...pipeline.stages.pretraining };
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const newProgress = stage.progressTicks + speedMultiplier;
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const moeSpeedPenalty = pipeline.architecture.type === 'moe' ? 0.8 : 1.0;
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const newProgress = stage.progressTicks + speedMultiplier * moeSpeedPenalty;
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const events = generateTrainingEvents(pipeline, state);
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let tickDelay = 0;
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@@ -81,7 +89,10 @@ export function processModels(state: GameState): ModelTickResult {
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tickDelay += event.impact.ticksDelayed ?? 0;
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notifications.push({ title: 'Hardware Failure', message: `${pipeline.modelName}: GPU failure during training. Recovering from checkpoint.`, type: 'warning' });
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} else if (event.type === 'data_contamination') {
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notifications.push({ title: 'Data Contamination', message: `${pipeline.modelName}: Copyright concerns detected in training data.`, type: 'warning' });
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totalReputationHit += event.impact.reputationHit ?? 0;
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totalLegalCosts += event.impact.legalCost ?? 0;
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const costStr = event.impact.legalCost ? ` Legal costs: $${(event.impact.legalCost).toLocaleString()}` : '';
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notifications.push({ title: 'Data Contamination', message: `${pipeline.modelName}: Copyright concerns detected in training data.${costStr}`, type: 'danger' });
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}
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}
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@@ -101,7 +112,7 @@ export function processModels(state: GameState): ModelTickResult {
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updated.currentStage = 'alignment';
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notifications.push({ title: 'Pre-training Complete', message: `${pipeline.modelName}: Moving to alignment.`, type: 'info' });
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} else {
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const model = createBaseModel(updated, state);
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const model = createBaseModel(updated, state, researchBonuses);
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baseModels = [...baseModels, model];
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families = families.map(f =>
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f.id === pipeline.familyId ? { ...f, baseModelId: model.id } : f,
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@@ -123,7 +134,7 @@ export function processModels(state: GameState): ModelTickResult {
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updated.currentStage = 'alignment';
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notifications.push({ title: 'SFT Complete', message: `${pipeline.modelName}: Moving to alignment.`, type: 'info' });
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} else {
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const model = createBaseModel(updated, state);
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const model = createBaseModel(updated, state, researchBonuses);
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baseModels = [...baseModels, model];
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families = families.map(f =>
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f.id === pipeline.familyId ? { ...f, baseModelId: model.id } : f,
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@@ -141,7 +152,7 @@ export function processModels(state: GameState): ModelTickResult {
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stage.isComplete = true;
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stage.progressTicks = stage.totalTicks;
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const model = createBaseModel(updated, state);
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const model = createBaseModel(updated, state, researchBonuses);
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baseModels = [...baseModels, model];
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families = families.map(f =>
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f.id === pipeline.familyId ? { ...f, baseModelId: model.id } : f,
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@@ -195,6 +206,8 @@ export function processModels(state: GameState): ModelTickResult {
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},
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completedModels,
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notifications,
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reputationHit: totalReputationHit,
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legalCosts: totalLegalCosts,
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};
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}
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@@ -262,12 +275,13 @@ function generateTrainingEvents(pipeline: TrainingPipeline, state: GameState): T
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? state.data.ownedDatasets.reduce((sum, d) => sum + d.legalRisk, 0) / state.data.ownedDatasets.length
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: 0;
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if (Math.random() < baseProbability * (hasDataPipeline ? 0.25 : 0.5) * avgLegalRisk) {
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const legalCost = 5000 + Math.floor(Math.random() * 15000);
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events.push({
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id: uuid(), type: 'data_contamination', tick: state.meta.tickCount,
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severity: 'moderate',
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severity: avgLegalRisk > 0.4 ? 'major' : 'moderate',
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description: 'Copyright holders identified content in training data.',
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resolved: true,
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impact: {},
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impact: { reputationHit: 5, legalCost },
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});
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}
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@@ -288,36 +302,61 @@ function generateTrainingEvents(pipeline: TrainingPipeline, state: GameState): T
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function createBaseModel(
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pipeline: TrainingPipeline,
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state: GameState,
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researchBonuses?: ResearchBonuses,
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): BaseModel {
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const { architecture, dataMix } = pipeline;
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const compute = pipeline.stages.pretraining.computeAllocated;
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const dataTokens = pipeline.stages.pretraining.targetTokens;
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const params = architecture.totalParameters;
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const computeFactor = Math.sqrt(compute) * 5;
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const dataFactor = Math.log10(1 + dataTokens / 1e8) * 10;
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const researchBonus = state.research.completedResearch.length * 3;
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const efficiencyBonus = state.research.completedResearch.filter(r => r.includes('efficiency')).length * 5;
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// Pillar 1: Parameters (0-30) — larger models have higher ceiling
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const paramFactor = Math.min(30, Math.log2(1 + params) * 4.5);
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let rawCapability = Math.min(95, computeFactor + dataFactor + researchBonus + efficiencyBonus);
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// Pillar 2: Compute (0-25) — compute relative to parameter count (Chinchilla scaling)
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const computePerParam = compute / Math.max(1, params);
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const computeFactor = Math.min(25, Math.sqrt(computePerParam) * 8);
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// Pillar 3: Data (0-20) — token count with quality multiplier
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const dataQualityMultiplier = 1 + (researchBonuses?.dataQualityBonus ?? 0);
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const dataFactor = Math.min(20, Math.log10(1 + dataTokens / 1e8) * 8 * dataQualityMultiplier);
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// Pillar 4: Research (0-20) — accumulated research knowledge
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const capabilityResearchBonus = researchBonuses?.globalCapabilityBonus ?? 0;
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const researchFactor = Math.min(20, capabilityResearchBonus + state.research.completedResearch.length * 0.5);
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let rawCapability = Math.min(95, paramFactor + computeFactor + dataFactor + researchFactor);
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if (architecture.type === 'moe') {
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rawCapability = Math.min(98, rawCapability * MOE_CAPABILITY_MULTIPLIER);
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}
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// MoE tradeoff: total params need full VRAM even though only active params run
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// This is enforced in the UI/store when checking VRAM requirements
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const researcherQuality = state.talent.departments.research.effectiveness;
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const contextBonus = Math.log2(Math.max(1, architecture.contextWindow / 4)) * 3;
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const contextPenalty = Math.max(0, Math.log2(architecture.contextWindow / 8)) * 2;
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const capabilities: ModelCapabilities = {
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reasoning: clamp(rawCapability * (0.6 + dataMix.scientific * 0.5 + dataMix.code * 0.3) * (1 + researcherQuality * 0.2)),
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coding: clamp(rawCapability * (0.5 + dataMix.code * 1.0)),
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creative: clamp(rawCapability * (0.4 + dataMix.books * 0.6 + dataMix.conversation * 0.3)),
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math: clamp(rawCapability * (0.3 + dataMix.scientific * 0.7 + dataMix.code * 0.2)),
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knowledge: clamp(rawCapability * (0.5 + dataMix.web * 0.3 + dataMix.books * 0.3)),
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knowledge: clamp(rawCapability * (0.5 + dataMix.web * 0.3 + dataMix.books * 0.3) + contextBonus * 0.3),
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multimodal: clamp(rawCapability * (dataMix.images * 0.5 + dataMix.video * 0.4 + dataMix.audio * 0.2)),
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agents: clamp(rawCapability * (0.2 + dataMix.code * 0.3 + dataMix.conversation * 0.2)),
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speed: Math.max(1, 100 - architecture.totalParameters * 0.3 + efficiencyBonus * 2 + (architecture.type === 'moe' ? MOE_SPEED_MULTIPLIER * 10 : 0)),
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agents: clamp(rawCapability * (0.2 + dataMix.code * 0.3 + dataMix.conversation * 0.2) + contextBonus * 0.5),
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speed: Math.max(1, 100 - params * 0.3 - contextPenalty + (researchBonuses?.inferenceEfficiencyBonus ?? 0) * 20 + (architecture.type === 'moe' ? MOE_SPEED_MULTIPLIER * 10 : 0)),
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contextUtilization: Math.min(100, architecture.contextWindow * 0.4),
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};
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if (researchBonuses) {
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capabilities.reasoning = clamp(capabilities.reasoning + researchBonuses.reasoningBonus);
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capabilities.coding = clamp(capabilities.coding + researchBonuses.codingBonus);
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capabilities.creative = clamp(capabilities.creative + researchBonuses.creativeBonus);
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capabilities.multimodal = clamp(capabilities.multimodal + researchBonuses.multimodalBonus);
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capabilities.agents = clamp(capabilities.agents + researchBonuses.agentsBonus);
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}
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const breakthroughBonuses: Partial<Record<keyof ModelCapabilities, number>> = {};
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for (const event of pipeline.events) {
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if ((event.type === 'breakthrough' || event.type === 'emergent_capability') && event.impact.capabilityDomain && event.impact.capabilityBonus) {
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@@ -347,10 +386,8 @@ function createBaseModel(
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}
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}
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const safetyResearch = state.research.completedResearch.filter(
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r => r.includes('alignment') || r.includes('interpretability') || r.includes('constitutional'),
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).length;
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let overallSafety = Math.min(100, 30 + safetyResearch * 15 + Math.random() * 10);
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const safetyResearchBonus = researchBonuses?.safetyBonus ?? 0;
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let overallSafety = Math.min(100, 30 + safetyResearchBonus + Math.random() * 10);
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let refusalRate = overallSafety > 60 ? 0.1 : 0.03;
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if (pipeline.stages.alignment?.isComplete) {
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@@ -5,13 +5,14 @@ import {
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SAFETY_INCIDENT_REPUTATION_HIT,
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LOW_SAFETY_THRESHOLD,
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} from '@ai-tycoon/shared';
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import type { ResearchBonuses } from './researchBonuses';
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export interface ReputationTickResult {
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reputation: ReputationState;
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safetyIncident: boolean;
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}
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export function processReputation(state: GameState): ReputationState & { _safetyIncident?: boolean } {
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export function processReputation(state: GameState, researchBonuses?: ResearchBonuses): ReputationState & { _safetyIncident?: boolean } {
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let { safetyRecord, publicPerception, employeeSatisfaction, regulatoryStanding } = state.reputation;
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let safetyIncident = false;
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@@ -40,6 +41,9 @@ export function processReputation(state: GameState): ReputationState & { _safety
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.reduce((sum, d) => sum + d.morale, 0) / 4;
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employeeSatisfaction = talentMorale;
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const reputationResearchBonus = researchBonuses?.reputationBonus ?? 0;
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publicPerception = Math.min(100, publicPerception + reputationResearchBonus * 0.1);
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const score = Math.round(
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safetyRecord * 0.3 +
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publicPerception * 0.3 +
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@@ -0,0 +1,80 @@
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import { TECH_TREE } from '../data/techTree';
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export interface ResearchBonuses {
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energyCostReduction: number;
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pipelineSpeedBonus: number;
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trainingSpeedBonus: number;
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inferenceEfficiencyBonus: number;
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tokensPerFlopBonus: number;
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dataQualityBonus: number;
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sdkCoverageBonus: number;
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globalCapabilityBonus: number;
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reasoningBonus: number;
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codingBonus: number;
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creativeBonus: number;
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multimodalBonus: number;
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agentsBonus: number;
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reputationBonus: number;
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safetyBonus: number;
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}
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export function getResearchBonuses(completedResearch: string[]): ResearchBonuses {
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const bonuses: ResearchBonuses = {
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energyCostReduction: 0,
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pipelineSpeedBonus: 0,
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trainingSpeedBonus: 0,
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inferenceEfficiencyBonus: 0,
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tokensPerFlopBonus: 0,
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dataQualityBonus: 0,
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sdkCoverageBonus: 0,
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globalCapabilityBonus: 0,
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reasoningBonus: 0,
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codingBonus: 0,
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creativeBonus: 0,
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multimodalBonus: 0,
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agentsBonus: 0,
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reputationBonus: 0,
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safetyBonus: 0,
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};
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for (const id of completedResearch) {
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const node = TECH_TREE.find(n => n.id === id);
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if (!node) continue;
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for (const effect of node.effects) {
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switch (effect.type) {
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case 'efficiency_boost':
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switch (effect.target) {
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case 'training_speed': bonuses.trainingSpeedBonus += effect.value; break;
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case 'inference': bonuses.inferenceEfficiencyBonus += effect.value; break;
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case 'tokens_per_flop': bonuses.tokensPerFlopBonus += effect.value; break;
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case 'pipeline_speed': bonuses.pipelineSpeedBonus += effect.value; break;
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case 'data_quality': bonuses.dataQualityBonus += effect.value; break;
|
||||
case 'sdk_coverage': bonuses.sdkCoverageBonus += effect.value; break;
|
||||
}
|
||||
break;
|
||||
case 'capability_boost':
|
||||
switch (effect.target) {
|
||||
case 'all': bonuses.globalCapabilityBonus += effect.value; break;
|
||||
case 'reasoning': bonuses.reasoningBonus += effect.value; break;
|
||||
case 'coding': bonuses.codingBonus += effect.value; break;
|
||||
case 'creative': bonuses.creativeBonus += effect.value; break;
|
||||
case 'multimodal': bonuses.multimodalBonus += effect.value; break;
|
||||
case 'agents': bonuses.agentsBonus += effect.value; break;
|
||||
case 'reputation': bonuses.reputationBonus += effect.value; break;
|
||||
}
|
||||
break;
|
||||
case 'cost_reduction':
|
||||
if (effect.target === 'energy') bonuses.energyCostReduction += effect.value;
|
||||
break;
|
||||
case 'safety_boost':
|
||||
bonuses.safetyBonus += effect.value;
|
||||
break;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return bonuses;
|
||||
}
|
||||
@@ -46,6 +46,7 @@ export function getAvailableResearch(state: GameState): typeof TECH_TREE {
|
||||
if (state.research.activeResearch?.researchId === node.id) return false;
|
||||
if (eraOrder.indexOf(node.era) > currentEraIdx) return false;
|
||||
if (node.prerequisites.some(p => !state.research.completedResearch.includes(p))) return false;
|
||||
if (node.cost.researchPoints > state.research.researchPoints) return false;
|
||||
return true;
|
||||
});
|
||||
}
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
import type { GameState, TalentState } from '@ai-tycoon/shared';
|
||||
|
||||
const SALARY_PER_HEADCOUNT_PER_TICK = 0.03;
|
||||
const SALARY_PER_HEADCOUNT_PER_TICK = 5;
|
||||
|
||||
export function processTalent(state: GameState): TalentState {
|
||||
const departments = { ...state.talent.departments };
|
||||
@@ -8,11 +8,11 @@ export function processTalent(state: GameState): TalentState {
|
||||
let totalSalary = 0;
|
||||
for (const [id, dept] of Object.entries(departments)) {
|
||||
totalSalary += dept.headcount * SALARY_PER_HEADCOUNT_PER_TICK;
|
||||
totalSalary += dept.budget / 2592000;
|
||||
totalSalary += dept.budget * 0.01;
|
||||
}
|
||||
|
||||
for (const hire of state.talent.keyHires) {
|
||||
totalSalary += hire.salary / 2592000;
|
||||
totalSalary += hire.salary;
|
||||
}
|
||||
|
||||
return {
|
||||
|
||||
@@ -12,6 +12,7 @@ import { processData } from './systems/dataSystem';
|
||||
import { checkEraTransition } from './systems/eraSystem';
|
||||
import { processAchievements } from './systems/achievementSystem';
|
||||
import { computeValuation } from './systems/fundingSystem';
|
||||
import { getResearchBonuses } from './systems/researchBonuses';
|
||||
|
||||
export interface TickResult {
|
||||
state: Partial<GameState>;
|
||||
@@ -32,13 +33,14 @@ export function setAchievementDefinitions(defs: AchievementDefinition[]) {
|
||||
|
||||
export function processTick(state: GameState): Partial<GameState> {
|
||||
const notifications: TickNotification[] = [];
|
||||
const researchBonuses = getResearchBonuses(state.research.completedResearch);
|
||||
|
||||
const infraResult = processInfrastructure(state);
|
||||
const infraResult = processInfrastructure(state, researchBonuses);
|
||||
const infrastructure = infraResult.infrastructure;
|
||||
notifications.push(...infraResult.notifications);
|
||||
|
||||
const stateWithInfra = { ...state, infrastructure };
|
||||
const modelResult = processModels(stateWithInfra);
|
||||
const modelResult = processModels(stateWithInfra, researchBonuses);
|
||||
|
||||
for (const completed of modelResult.completedModels) {
|
||||
notifications.push({
|
||||
@@ -51,7 +53,7 @@ export function processTick(state: GameState): Partial<GameState> {
|
||||
|
||||
const stateWithModels = { ...stateWithInfra, models: modelResult.modelsState };
|
||||
|
||||
const capacity = computeCapacity(state, infrastructure);
|
||||
const capacity = computeCapacity(state, infrastructure, researchBonuses);
|
||||
const market = processMarket(stateWithModels, capacity.tokensPerSecondCapacity);
|
||||
const compute = finalizeCompute(capacity, market.totalTokenDemand);
|
||||
|
||||
@@ -67,7 +69,7 @@ export function processTick(state: GameState): Partial<GameState> {
|
||||
});
|
||||
}
|
||||
|
||||
const reputationResult = processReputation(stateWithTalent);
|
||||
const reputationResult = processReputation(stateWithTalent, researchBonuses);
|
||||
const { _safetyIncident, ...reputation } = reputationResult;
|
||||
if (_safetyIncident) {
|
||||
notifications.push({
|
||||
@@ -76,7 +78,17 @@ export function processTick(state: GameState): Partial<GameState> {
|
||||
type: 'danger',
|
||||
});
|
||||
}
|
||||
const economy = processEconomy(stateWithTalent, market, infrastructure, infraResult.repairCosts);
|
||||
if (modelResult.reputationHit > 0) {
|
||||
reputation.publicPerception = Math.max(0, reputation.publicPerception - modelResult.reputationHit);
|
||||
reputation.score = Math.round(
|
||||
reputation.safetyRecord * 0.3 +
|
||||
reputation.publicPerception * 0.3 +
|
||||
reputation.employeeSatisfaction * 0.2 +
|
||||
reputation.regulatoryStanding * 0.2,
|
||||
);
|
||||
}
|
||||
const extraCosts = infraResult.repairCosts + modelResult.legalCosts;
|
||||
const economy = processEconomy(stateWithTalent, market, infrastructure, extraCosts);
|
||||
const data = processData(stateWithTalent);
|
||||
const competitors = processCompetitors(stateWithTalent);
|
||||
|
||||
|
||||
Reference in New Issue
Block a user