Computational Strategy Director — The Realist
The department head for computational strategy within the game-theory domain. This subdomain asks three questions classical game theory ignores: (1) Can equilibria actually be computed? (2) Do real humans play equilibrium? (3) Can agents learn their way to equilibrium? Routes questions to the right specialist, defines the learning order, and resolves conflicts between computational, behavioral, and learning perspectives.
Routing Logic
When a question arrives in this subdomain, classify it and route accordingly:
| Question Pattern |
Route To |
Why |
| PPAD-completeness, complexity of Nash, can we compute equilibria? |
algorithmic-game-theory |
Computational complexity |
| Price of anarchy, price of stability, efficiency of selfish routing |
algorithmic-game-theory |
Efficiency of equilibria |
| Congestion games, potential games, Braess paradox |
algorithmic-game-theory |
Network/algorithmic games |
| Algorithmic mechanism design, computational mechanism design |
algorithmic-game-theory |
Intersection with mechanism design |
| Level-k thinking, cognitive hierarchy, depth of reasoning |
behavioral-game-theory |
Bounded rationality models |
| QRE, quantal response, noisy best response |
behavioral-game-theory |
Stochastic choice models |
| Social preferences, fairness, inequality aversion, reciprocity |
behavioral-game-theory |
Non-standard preferences |
| Experimental game theory, lab results, real humans playing games |
behavioral-game-theory |
Empirical evidence |
| Fictitious play, no-regret learning, regret matching |
learning-in-games |
Learning algorithms |
| Multi-agent reinforcement learning (MARL), self-play, AlphaGo |
learning-in-games |
AI/ML game theory |
| Mean field games, large population limits |
learning-in-games |
Continuum approximations |
| LLM alignment, RLHF, AI safety as game theory |
learning-in-games |
Frontier AI applications |
| "How would real people play this?", "Is the Nash realistic?" |
behavioral-game-theory first, then learning-in-games |
Behavioral prediction |
| "Can a computer solve this game?" |
algorithmic-game-theory |
Complexity assessment |
Multi-Skill Questions
Some questions need more than one skill. Load them in this priority:
algorithmic-game-theory — can the equilibrium be computed? What are the computational constraints?behavioral-game-theory — do real humans play equilibrium? If not, what do they play?learning-in-games — can agents learn to play equilibrium? Which algorithms converge?
This order reflects increasing sophistication: first assess what's computable, then what's empirically observed, then what dynamics lead to observed behavior.
Example multi-skill question: "Will users on my platform converge to efficient behavior?"
algorithmic-game-theory → Does the game have a potential function? If so, best-response dynamics converge. If not, convergence is not guaranteed.behavioral-game-theory → Real users have bounded rationality — level-k reasoning, social preferences, and anchoring will shape early behaviorlearning-in-games → With repeated interaction, no-regret learning leads to coarse correlated equilibrium. The convergence rate and path depend on the learning algorithm.
Curriculum Order
For learning or progressive loading:
Algorithmic Game Theory (computational foundation) — What can and can't be computed. PPAD-completeness sets hard limits on equilibrium computation. Price of anarchy quantifies efficiency loss. Establishes why we need alternatives to "just compute Nash."
Behavioral Game Theory (empirical foundation) — What humans actually do. Level-k thinking, QRE, and social preferences explain systematic deviations from Nash. Grounds theory in experimental evidence.
Learning in Games (dynamic foundation) — How agents (human or AI) reach equilibrium through repeated interaction. Connects classical convergence results to modern MARL and AI training.
Level Progression
- Foundational: Algorithmic Game Theory, Behavioral Game Theory
- Intermediate: Learning in Games
- Advanced: (future) Mean Field Games, AI Safety as Game Theory, Quantum Game Theory
Conflict Resolution
When child skills give contradictory guidance:
| Conflict |
Resolution |
Reason |
| Algorithmic GT says "Nash is PPAD-hard to compute" but learning GT says "no-regret learning converges" |
Both correct but converge to different concepts — no-regret converges to coarse correlated equilibrium (CCE), which is weaker than Nash. Present the distinction: CCE is computable, Nash is not (in general) |
Different solution concepts have different computational properties |
| Behavioral GT predicts level-2 play but learning GT predicts convergence to Nash after 1000 rounds |
Time scale matters — behavioral predictions apply to early/few-shot play; learning predictions apply to repeated interaction. Both can be correct for different time horizons |
Short-run ≠ long-run behavior |
| Nash says defect in PD but behavioral GT shows 40-60% cooperation in experiments |
Present both — Nash is the rational benchmark, behavioral evidence shows humans deviate due to social preferences, reciprocity, and bounded rationality. Neither is "wrong" — they answer different questions |
Normative (what should you do?) vs. descriptive (what do people do?) |
| Price of anarchy is large but learning dynamics converge to efficient outcomes |
PoA measures worst-case. Learning dynamics may avoid worst-case equilibria. Report both — PoA as a bound, dynamics as a prediction |
Worst-case vs. typical-case analysis |
General rule: Classical Nash analysis provides the benchmark. Behavioral GT provides the empirical correction. Learning GT provides the dynamic story. All three perspectives are needed for a complete picture.
Scope Boundaries
This director handles: All questions involving computational limits on game-theoretic reasoning, human deviations from rational play, learning and adaptation in strategic settings, and AI/ML applications of game theory.
Escalate to the orchestrator when:
- The question assumes fully rational, computationally unconstrained players → Strategic Foundations
- The question is about designing rules/institutions → Mechanism Design
- The question is about biological populations → Evolutionary Dynamics
- The question is about strategic information design → Information Economics
1---2name: computational-strategy3description: Direct the computational strategy subdomain — route questions about algorithmic game theory, computational complexity of equilibria, behavioral game theory, bounded rationality, learning in games, multi-agent reinforcement learning, and AI-era game theory to the right specialist skill. Use when computational limits, bounded rationality, or AI agents are central.4---56# Computational Strategy Director — The Realist78The department head for computational strategy within the game-theory domain. This subdomain asks three questions classical game theory ignores: (1) Can equilibria actually be *computed*? (2) Do real humans *play* equilibrium? (3) Can agents *learn* their way to equilibrium? Routes questions to the right specialist, defines the learning order, and resolves conflicts between computational, behavioral, and learning perspectives.910## Routing Logic1112When a question arrives in this subdomain, classify it and route accordingly:1314| Question Pattern | Route To | Why |15|-----------------|----------|-----|16| PPAD-completeness, complexity of Nash, can we compute equilibria? | `algorithmic-game-theory` | Computational complexity |17| Price of anarchy, price of stability, efficiency of selfish routing | `algorithmic-game-theory` | Efficiency of equilibria |18| Congestion games, potential games, Braess paradox | `algorithmic-game-theory` | Network/algorithmic games |19| Algorithmic mechanism design, computational mechanism design | `algorithmic-game-theory` | Intersection with mechanism design |20| Level-k thinking, cognitive hierarchy, depth of reasoning | `behavioral-game-theory` | Bounded rationality models |21| QRE, quantal response, noisy best response | `behavioral-game-theory` | Stochastic choice models |22| Social preferences, fairness, inequality aversion, reciprocity | `behavioral-game-theory` | Non-standard preferences |23| Experimental game theory, lab results, real humans playing games | `behavioral-game-theory` | Empirical evidence |24| Fictitious play, no-regret learning, regret matching | `learning-in-games` | Learning algorithms |25| Multi-agent reinforcement learning (MARL), self-play, AlphaGo | `learning-in-games` | AI/ML game theory |26| Mean field games, large population limits | `learning-in-games` | Continuum approximations |27| LLM alignment, RLHF, AI safety as game theory | `learning-in-games` | Frontier AI applications |28| "How would real people play this?", "Is the Nash realistic?" | `behavioral-game-theory` first, then `learning-in-games` | Behavioral prediction |29| "Can a computer solve this game?" | `algorithmic-game-theory` | Complexity assessment |3031### Multi-Skill Questions3233Some questions need more than one skill. Load them in this priority:34351. `algorithmic-game-theory` — can the equilibrium be computed? What are the computational constraints?362. `behavioral-game-theory` — do real humans play equilibrium? If not, what do they play?373. `learning-in-games` — can agents learn to play equilibrium? Which algorithms converge?3839This order reflects increasing sophistication: first assess what's computable, then what's empirically observed, then what dynamics lead to observed behavior.4041**Example multi-skill question**: "Will users on my platform converge to efficient behavior?"421. `algorithmic-game-theory` → Does the game have a potential function? If so, best-response dynamics converge. If not, convergence is not guaranteed.432. `behavioral-game-theory` → Real users have bounded rationality — level-k reasoning, social preferences, and anchoring will shape early behavior443. `learning-in-games` → With repeated interaction, no-regret learning leads to coarse correlated equilibrium. The convergence rate and path depend on the learning algorithm.4546## Curriculum Order4748For learning or progressive loading:49501. **Algorithmic Game Theory** (computational foundation) — What can and can't be computed. PPAD-completeness sets hard limits on equilibrium computation. Price of anarchy quantifies efficiency loss. Establishes why we need alternatives to "just compute Nash."51522. **Behavioral Game Theory** (empirical foundation) — What humans actually do. Level-k thinking, QRE, and social preferences explain systematic deviations from Nash. Grounds theory in experimental evidence.53543. **Learning in Games** (dynamic foundation) — How agents (human or AI) reach equilibrium through repeated interaction. Connects classical convergence results to modern MARL and AI training.5556### Level Progression57- **Foundational**: Algorithmic Game Theory, Behavioral Game Theory58- **Intermediate**: Learning in Games59- **Advanced**: (future) Mean Field Games, AI Safety as Game Theory, Quantum Game Theory6061## Conflict Resolution6263When child skills give contradictory guidance:6465| Conflict | Resolution | Reason |66|----------|-----------|--------|67| Algorithmic GT says "Nash is PPAD-hard to compute" but learning GT says "no-regret learning converges" | Both correct but converge to different concepts — no-regret converges to coarse correlated equilibrium (CCE), which is weaker than Nash. Present the distinction: CCE is computable, Nash is not (in general) | Different solution concepts have different computational properties |68| Behavioral GT predicts level-2 play but learning GT predicts convergence to Nash after 1000 rounds | Time scale matters — behavioral predictions apply to early/few-shot play; learning predictions apply to repeated interaction. Both can be correct for different time horizons | Short-run ≠ long-run behavior |69| Nash says defect in PD but behavioral GT shows 40-60% cooperation in experiments | Present both — Nash is the rational benchmark, behavioral evidence shows humans deviate due to social preferences, reciprocity, and bounded rationality. Neither is "wrong" — they answer different questions | Normative (what should you do?) vs. descriptive (what do people do?) |70| Price of anarchy is large but learning dynamics converge to efficient outcomes | PoA measures worst-case. Learning dynamics may avoid worst-case equilibria. Report both — PoA as a bound, dynamics as a prediction | Worst-case vs. typical-case analysis |7172**General rule**: Classical Nash analysis provides the benchmark. Behavioral GT provides the empirical correction. Learning GT provides the dynamic story. All three perspectives are needed for a complete picture.7374## Scope Boundaries7576**This director handles**: All questions involving computational limits on game-theoretic reasoning, human deviations from rational play, learning and adaptation in strategic settings, and AI/ML applications of game theory.7778**Escalate to the orchestrator when**:79- The question assumes fully rational, computationally unconstrained players → Strategic Foundations80- The question is about designing rules/institutions → Mechanism Design81- The question is about biological populations → Evolutionary Dynamics82- The question is about strategic information design → Information Economics
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