Game balance audit: wire research effects, rework capability formula, fix dead systems

- 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>
2026-04-25 09:36:31 -04:00
parent 8d650fefae
commit 00e790591e
14 changed files with 205 additions and 54 deletions
@@ -0,0 +1,80 @@
+import { TECH_TREE } from '../data/techTree';
+
+export interface ResearchBonuses {
+  energyCostReduction: number;
+  pipelineSpeedBonus: number;
+  trainingSpeedBonus: number;
+  inferenceEfficiencyBonus: number;
+  tokensPerFlopBonus: number;
+  dataQualityBonus: number;
+  sdkCoverageBonus: number;
+
+  globalCapabilityBonus: number;
+  reasoningBonus: number;
+  codingBonus: number;
+  creativeBonus: number;
+  multimodalBonus: number;
+  agentsBonus: number;
+  reputationBonus: number;
+
+  safetyBonus: number;
+}
+
+export function getResearchBonuses(completedResearch: string[]): ResearchBonuses {
+  const bonuses: ResearchBonuses = {
+    energyCostReduction: 0,
+    pipelineSpeedBonus: 0,
+    trainingSpeedBonus: 0,
+    inferenceEfficiencyBonus: 0,
+    tokensPerFlopBonus: 0,
+    dataQualityBonus: 0,
+    sdkCoverageBonus: 0,
+    globalCapabilityBonus: 0,
+    reasoningBonus: 0,
+    codingBonus: 0,
+    creativeBonus: 0,
+    multimodalBonus: 0,
+    agentsBonus: 0,
+    reputationBonus: 0,
+    safetyBonus: 0,
+  };
+
+  for (const id of completedResearch) {
+    const node = TECH_TREE.find(n => n.id === id);
+    if (!node) continue;
+
+    for (const effect of node.effects) {
+      switch (effect.type) {
+        case 'efficiency_boost':
+          switch (effect.target) {
+            case 'training_speed': bonuses.trainingSpeedBonus += effect.value; break;
+            case 'inference': bonuses.inferenceEfficiencyBonus += effect.value; break;
+            case 'tokens_per_flop': bonuses.tokensPerFlopBonus += effect.value; break;
+            case 'pipeline_speed': bonuses.pipelineSpeedBonus += effect.value; break;
+            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;
+}