| name | foundations-game-theory |
| description | Game-theory primitives for strategic decision systems, auctions, mechanism design, incentives, attribution, negotiation, debate, and trust. Use when modeling strategic play. |
| compatibility | Claude Code + Codex. Portable core — primitives apply across domains. |
| version | 1.2 |
| last_validated | 2026-08-14T00:00:00.000Z |
Game Theory Foundations
22 applied game-theory primitives for strategic decision systems, backed by a formal theory map. Each applied primitive solves a specific incentive or coordination failure. Primitives are domain-agnostic: the same mechanism that prevents free-riding in agent teams prevents cost-shifting in partnership contracts; the same auction that routes tasks routes ad placements.
For the agent-team applied recipe layer (team.yaml manifest fields, agent-team anti-patterns, agent-team decision checklist, composition recipes for typical agent-team scenarios), see agents-subagents/references/game-theory-agent-teams.md.
Contents
Quick Reference
| Primitive | Domain | Recipe Stub |
|---|
| Belief-Driven Coordination (ECON) | Multi-party teams, distributed analysis, agent teams | Members optimize against beliefs about co-members; reduces redundant work and inter-member chat |
| Adversarial Debate | Content moderation, risk review, audit | Two heterogeneous evaluators + reasoning-tree synthesis; no majority vote |
| Auction-Based Routing | Ad placement, task delegation, resource allocation | Sealed-bid truthful auction; highest-value-per-cost wins |
| Shapley Contribution | Attribution, revenue sharing, team composition | Marginal-contribution average across subsets |
| Reputation-Gated Autonomy | Supplier qualification, agent oversight, fraud gating | Tiered trust: proven → standard → probationary; oversight inversely proportional |
| Cooperation and Defection | Partnership design, incentive alignment, compliance | Iterated PD structure; payoff-scale to detect defection tendency |
| Mechanism Design for Synthesis | Decision aggregation, voting, policy-making | Vickrey truthful-revelation; dissent is a required section |
| Courtroom-Style Debate | Legal review, risk go/no-go, claim verification | Plaintiff/defense/court structure + progressive RAG + role-switching |
| Pareto-Nash Multi-Objective | Product tradeoffs, regulatory vs growth, pricing tiers | Map Pareto frontier; pick dominant options; flag non-dominated set |
| Evolutionary Coordination Search | Algorithm selection, prompt tuning, rule evolution | LLM-mutated program + fitness signal; ShinkaEvolve for sample efficiency |
| Prediction Market Confidence | Forecasting, risk calibration, hiring decisions | Stake-weighted confidence; CritiCal calibration step before stake |
| Negotiation ZOPA/BATNA |
When to Apply
Apply game-theory primitives when:
- Multiple agents/teams/users with potentially divergent incentives
- Synthesis where minority-correct outcomes matter (high-stakes, irreversible)
- Auctions, bidding, mechanism design, or pricing where strategic behaviour exists
- Repeated interactions where reputation, cooperation, or trust evolves
- Best-of-N selection across 5+ candidates (BMV/RCS)
- Cross-trust delegation, dynamic agent pools, or high-stakes act/escalate decisions
Skip and use simpler alternatives when:
- Single agent / single-shot task — game theory is about interactions, not solo work
- Routine task with high majority-correct rate — a deterministic check or oracle is cheaper
- Hard verification exists (test suite, schema, calculator) — use the oracle, not voting
- Team < 3 members on a low-stakes call — overhead exceeds diversity gain
- Information-only retrieval / pure compression — use foundations-information-theory instead
- Single-system reliability/SLO question — use foundations-reliability-theory or queueing-theory
Primitive Index
Each primitive has a full playbook (problem, solution, how-it-works, launch-prompt template, domain applications, citations).
Formal Supporting Theory
The 22 primitives are the applied layer, not the whole field. Use references/formal-theory-map.md when the task needs formal assumptions, proof obligations, or classical theory coverage.
| Theory Area | Use When | Applied Primitives It Grounds |
|---|
| Game forms | Need to classify normal-form, extensive-form, Bayesian, repeated, stochastic, or cooperative structure | #1, #6, #8, #9, #10, #12 |
| Solution concepts | Need dominance, minimax, Nash, Bayesian Nash, subgame-perfect, perfect Bayesian, or correlated equilibrium | #1, #2, #6, #8, #9, #10, #18 |
| Mechanism and auction design | Need incentive compatibility, individual rationality, revelation principle, VCG, Myerson, reserves, or bid shading | #3, #7, #11, #20, #21 |
| Information economics | Need signaling, screening, adverse selection, moral hazard, principal-agent framing, or attestation | #5, #7, #12, #14, #21 |
| Cooperative game theory | Need Shapley, core, nucleolus, Banzhaf, coalition formation, or surplus sharing | #4, #6, #15, #17, #22 |
| Market design and matching | Need stable matching, deferred acceptance, matching with contracts, or allocation without prices | #3, #7, #12, #15 |
| Bargaining theory | Need Nash bargaining, Rubinstein bargaining, BATNA/ZOPA, outside options, or alternating offers | #12 |
| Learning in games | Need no-regret, fictitious play, CFR, PSRO, self-play, or empirical game-theoretic analysis — including no-regret Nash policy convergence in RLHF (INPO, ICLR 2025 Oral) and smooth RM+ last-iterate convergence [NeurIPS 2025] | #6, #10, #11, #17 |
| Strategic failure analysis | Need collusion, equilibrium selection, Goodharting, manipulation, or off-equilibrium threats | all primitives |
Expert Judgment: When the Model Helps vs Misleads
Applying a primitive correctly is mechanical. Knowing whether the game-theoretic frame is the right frame at all — and which game — is the actual expert skill. This section is judgment, not a lookup table.
The equilibrium selection problem
Most interesting games (repeated games especially — see the Folk Theorem in formal-theory-map.md) have many equilibria, not one. A non-expert computes an equilibrium and reports it as "the" prediction. An expert checks multiplicity first and asks what actually selects among the candidates in this specific situation — precedent, an explicit contract, a public commitment, a focal point, or repeated-play reputation. Reporting "the Nash equilibrium is X" without naming the selection mechanism is a tell that the analysis stopped one step too early.
Common-knowledge assumptions failing in practice
Nash equilibrium, Bayesian Nash equilibrium, and most mechanism-design proofs assume common knowledge of rationality, of payoffs (or their distribution), and of the rules of the game itself. Real organizations violate all three routinely:
- A "competitor" may be a satisficer bound by an internal OKR or a legacy contract, not a profit-maximizing best-responder — modeling them as rational invites a confidently wrong prediction.
- Bidders or negotiating parties often do not share a common prior on value — private information about downstream use, not risk attitude, is driving the gap.
- LLM agents do not reliably best-respond at all: pro-social bias, framing sensitivity, and authority compliance are documented, repeated deviations from Nash play (see the LLM rationality trap in
patterns-scenarios-traps.md). Any incentive-compatibility argument built on "agents best-respond" needs a held-out behavioral check before it is trusted for LLM participants.
- The deviation runs in both directions, and over-truthfulness breaks proofs the same way strategic misreporting does. LLM agents in matching markets reveal preferences truthfully at higher rates than human subjects, but truth-telling does not track strategy-proofness — a strategy-proof mechanism did not elicit more truthful reports than a manipulable one (Hoshino, Kitadai & Nishino, arXiv:2606.03030, June 2026). Mechanism-based markets still beat free negotiation on stability and efficiency; the conclusion is that matching theory is a useful but incomplete guide for LLM-agent institutions, not that the guarantees transfer.
Self-assessment is the binding constraint on agent markets. Auctions, task routing (#3), and confidence staking (#11) all consume agent self-reports of cost and success probability. MarketBench (Fradkin & Krishnan, arXiv:2604.23897, April 2026) measured six recent models on 93 SWE-bench Lite tasks and found them poorly calibrated on both success rate and token consumption; auctions built from those self-reports diverged from the full-information allocation, and supplying prior-capability context improved calibration only modestly. Before routing real work by agent bids, measure calibration on held-out tasks — an incentive-compatible mechanism fed miscalibrated valuations allocates badly without anyone misreporting strategically.
Habit: before invoking a solution concept, ask "would every party recognize this as the same game I do?" If not, either model it explicitly as a game of incomplete information (Bayesian game) or drop equilibrium language and use the frame as a heuristic only.
Mapping a business situation to the right game
Non-experts reach for "prisoner's dilemma" or "Nash equilibrium" as a generic label for any tense multi-party situation. An expert asks a short sequence of diagnostic questions before naming a game form or picking a primitive:
- Who are the real strategic actors? Not every interested party is a strategic player — a regulator reacting on a multi-year lag is closer to an exogenous constraint than a player in a weekly pricing game.
- One-shot or repeated — do the players expect to meet again? A single vendor negotiation is a bargaining problem (#12); an ongoing supplier relationship is a repeated game where reputation and folk-theorem-style cooperation are available — analyzing it as one-shot recommends defection that is actually irrational given the relationship's shadow of the future.
- Simultaneous or sequential, and who commits first? Prices set quarterly and observed by competitors before they respond is closer to Stackelberg (sequential, first-mover) than Cournot/Bertrand (simultaneous) — the right model changes the recommendation from "best-respond" to "commit and signal."
- Is value created cooperatively or contested? Cooperative-game tools (Shapley, core) fit attribution and surplus-sharing (#4); competitive tools (auctions, Nash) fit contested allocation (#3, #9). Applying auction logic to a joint-venture split, or Shapley logic to a zero-sum negotiation, produces answers that are precise and wrong.
- Is there a credible commitment device? A threat or promise only constrains behavior if the counterparty believes it will be carried out even against the threatener's own later interest. A pricing "war" threat with no sunk cost or public commitment behind it is cheap talk — treat it as information about intent, not as a binding constraint on the game tree.
- Is this actually a game, or an oracle-verifiable fact? The most common non-expert error in this whole domain is running a debate, auction, or negotiation protocol over a question that has a deterministic answer — a test suite, a contract clause, a calculator. See Misuse Boundaries.
Mechanism-design failure modes that only surface in production
Textbook mechanism design proves existence of a truthful, efficient, individually rational mechanism under an idealized participant model. Each row below is a normal way real deployments break that idealization — not an edge case to footnote.
| Failure Mode | What Breaks | Real-World Trigger | Mitigation |
|---|
| Collusion / bidder rings | Dominant-strategy truthfulness assumes independent bidders; a ring that agrees off-mechanism to suppress bids and split the surplus defeats VCG and second-price auctions alike | Repeated auctions with a small, stable, identifiable bidder pool | Reserve prices, bidder-pool rotation, anti-collusion monitoring (AntiCollusionAI); detect via markup-over-marginal-cost drift over many rounds, not spot price |
| False-name bids | A single bidder submits multiple identities; VCG is provably not false-name-proof in combinatorial auctions, and no false-name-proof mechanism is Pareto efficient in general (Yokoo, Sakurai & Matsubara, Games and Economic Behavior, 2004) | Any auction where identity is cheap to fabricate — email-based registration, sybil-able agent pools, unverified marketplace accounts | Require attested identity before bidding (mirrors #21 Attested Delegation Contracts) — price identity verification into the mechanism, not as an afterthought |
| Participation constraints failing | Individual rationality assumes the average outside option; when the highest-value participants have the best outside options, they opt out first and adversely select the remaining pool | A mechanism designed around expected participants, not the marginal one who is deciding whether to walk | Check IR against the highest-value participant's outside option; Myerson & Satterthwaite (1983) show no mechanism for private-value bilateral trade can be simultaneously efficient, budget-balanced, and individually rational — some efficiency loss or subsidy is structurally unavoidable |
| Budget imbalance | VCG is efficient and truthful but generally runs a deficit or surplus that must land somewhere | Multi-sided mechanisms with no natural residual claimant | Decide upfront who absorbs the imbalance (platform take-rate, budget-neutral variant, or accept the inefficiency) rather than discovering it at settlement |
| Computational infeasibility | Exact VCG for combinatorial allocation requires solving an often NP-hard optimization for the winning allocation and every counterfactual-without-bidder-i allocation | Task/resource routing over bundles, not single-item slots |
Practical tell: if a mechanism is called "truthful" or "incentive-compatible" but nobody can name (a) the participation constraint being satisfied, (b) how false identities are prevented, (c) who absorbs budget imbalance, and (d) what enforces the rules other than the prompt, the claim has not actually been checked.
Communication channels are a collusion dial, not a neutral feature. Direct seller-to-seller messaging raises collusive tendency in simulated continuous double auctions, with the effect varying by model and modulated by oversight and authority pressure (Agrawal et al., arXiv:2507.01413, 2025). The same channel that reduces conflict in coordination games raises coordinated overpricing in market games — decide which game you are actually running before granting agents a side channel.
Anti-Patterns
| Anti-Pattern | Game Theory Diagnosis | Fix |
|---|
| Majority vote in high-stakes aggregation | Correlated errors pass; LLMs share biases | Reasoning-tree audit (#13) traces each claim to evidence |
| Single-objective optimization on a tradeoff decision | Pareto-dominant alternatives go unexamined | Map Pareto frontier (#9) before committing |
| Attribution by seniority or loudness | Free-riding goes undetected; poor performers stay | Shapley marginal-contribution scoring (#4) |
| Flat trust applied uniformly | High-risk counterparties get same autonomy as proven ones | Reputation-gated tiers (#5) calibrate oversight to track record |
| Adversarial debate forced on genuine compromises | Positions harden; ZOPA never located | Switch to negotiation protocol (#12) when there is a continuous tradeoff |
| Confidence staking without calibration | Overconfident participants dominate synthesis | CritiCal calibration step before prediction market (#11) staking |
| Synthesis suppresses dissent | Minority-correct signal is erased | Dissent required as a section in mechanism-design synthesis (#7) |
| Uniform cooperation assumed in partnerships | Defection undetected until costly | Iterated payoff-scale test (#6) surfaces defection tendency early |
| All members read same context, produce overlapping analysis | Pooling equilibrium — no belief differentiation | Belief-driven coordination (#1) gives each member a unique lane |
| Static debate role assignment regardless of question | Wrong-specialist assignment dominates outcome | Meta-debate role routing (#16) — propose + peer-review picks plaintiff/defense/judge |
| Best-of-N collapsed by majority vote | Calibration and minority-correct signal lost | Beyond Majority Voting (#18) — Optimal Weight + Inverse Surprising Popularity |
| Open-ended generation scored by lexical overlap | Semantically equivalent answers split the vote | Radial Consensus Score (#19) — embedding-centroid selector |
| Consensus treated as permission to act | Wrong agreement becomes automated harm | Conformal Social Choice (#20) — act only on singleton calibrated set |
| Routing by self-claimed delegate quality | Strategic or misconfigured delegates attract work |
Misuse Boundaries
| Misuse | Why It Is Wrong | Required Correction |
|---|
| Applying game theory when a deterministic validator exists | Hard oracles beat strategic synthesis | Run tests, compilers, schema checks, SQL, or calculators first |
| Calling a workflow incentive-compatible without payoffs | Truth-telling is not a label; it requires a payoff structure | State the mechanism, utility model, and best-response argument |
| Treating LLM agents as economic agents with stable preferences | Models follow prompts and context, not durable utility functions | Reframe as an operational heuristic unless preferences are explicit |
| Using Shapley when contribution is not measurable | Attribution becomes story-telling | Define the value function and approximation before scoring |
| Using debate when disagreement is caused by missing data | Debate amplifies uncertainty instead of resolving it | Retrieve, measure, or ask for missing evidence first |
| Using RCS/BMV when a hard oracle exists | Selection mechanisms can suppress the verifiable answer | Use the oracle, then optionally synthesize explanations |
| Using reputation as proof of claim truth | Strong participants can make local errors | Run per-claim credibility scoring |
| Using equilibrium language without checking equilibrium selection | Multiple equilibria can imply opposite recommendations | List candidate equilibria and the selection assumption |
| Optimizing one metric in a multi-party mechanism | Goodharting shifts harm to unmeasured parties | Add Pareto and stakeholder checks before launch |
| Hiding minority evidence in synthesis | Minority-correct answers are a common failure case | Preserve dissent, runner-up, and outlier evidence |
| Calling a multi-principal synthesis incentive-compatible without designing a payment scheme | Truthful reporting is strictly dominated without payments in multi-stakeholder settings (NeurIPS 2024 proof) | Add affine maximizer (weighted VCG) payment or explicitly scope to a single-principal setting |
| Running an auction over uncalibrated agent self-reports | Allocation quality is bounded by valuation accuracy, not by mechanism truthfulness — miscalibrated bids misallocate even under honest reporting | Measure bid calibration on held-out tasks before routing real work (MarketBench) |
Decision Checklist
Composition Recipes
See assets/templates/game-theory/README.md for full domain-scenario stacks.
Quick stacks:
-
Pricing / monetization: #9 (Pareto-Nash for objective mapping) + #12 (BATNA/ZOPA for negotiation range) + #7 (synthesis dissent required)
Inputs: Competitor price points, own marginal cost, demand elasticity estimate, switching cost for buyer.
Rules: Map Pareto frontier across price/margin/volume objectives (#9); compute BATNA floor and ZOPA ceiling per party (#12); run Bertrand floor check — if product is undifferentiated, price collapses to marginal cost; differentiation (feature, brand, lock-in) is required to hold above floor; synthesis must surface dissenting price band (#7).
Outputs: Price band (floor = marginal cost or BATNA, ceiling = ZOPA upper bound), differentiation requirement to sustain above-floor pricing, dissent note if any Pareto-dominated option was preferred by a stakeholder.
-
Ad bidding / task routing: #3 (auction routing) + #4 (Shapley ROI attribution) + #11 (confidence-weighted forecast)
Inputs: Bidder count, valuation distribution (private or correlated), bid visibility (sealed vs. open), budget constraints.
Rules: If private values and bids sealed → 2nd-price (Vickrey) dominant; if bids are publicly visible → 1st-price + reserve (visible bids flip incentive to overbid for signalling, collapsing the 2nd-price guarantee); attribute ROI across winning bidder's components via Shapley marginal contribution; calibrate confidence forecasts via CritiCal step before staking.
Outputs: Mechanism choice (1st-price + reserve vs. 2nd-price), expected revenue estimate, per-component Shapley ROI attribution, calibrated confidence interval on forecast.
Worked example — marketplace switching from 1st-price to 2nd-price (Vickrey) auction. Bidders: 4, valuations [10, 8, 6, 4]. 1st-price equilibrium: rational bid shading produces bids ≈ [7.5, 6, 4.5, 3] → revenue = 7.5 (winner pays own bid). 2nd-price truthful: bids = [10, 8, 6, 4] → revenue = 8 (winner pays 2nd-highest). Truthfulness gain: +6.7% revenue, plus zero bid-shading complexity → fewer abandoned bids and lower ops cost. Anti-pattern: don't run 2nd-price with publicly visible bids — incentive flips to overbid for signalling and the dominant-strategy guarantee collapses.
-
Security / adversarial context: #14 (per-claim credibility) + #13 (reasoning-tree audit) + #8 (courtroom for go/no-go)
Inputs: Claim set under review, evidence sources per claim, adversarial threat model (injection vector, attacker capability), go/no-go decision stakes.
Rules: Score each claim independently on evidence quality × corroboration weight (#14); trace every claim to its First Point of Disagreement in the reasoning tree (#13); run courtroom plaintiff/defense/judge only after per-claim scoring — debate over unsupported claims amplifies uncertainty rather than resolving it.
Per-claim credibility score, reasoning-tree audit trail, go/no-go recommendation with dissent preserved, list of claims that failed credibility threshold and require retrieval before re-evaluation.
Navigation
Current Pattern Review
Use references/patterns-scenarios-traps.md before applying a primitive to production or agent-team routing. It distinguishes durable game-theory mechanisms from fast-moving LLM-agent papers, lists scenario-specific stacks, and calls out traps such as majority-vote collapse, uncalibrated confidence, static role assignment, overusing debate, and source claims that have not been rechecked against primary papers.
Workflow
- Identify the strategic failure mode in your system (attribution, routing, synthesis, trust, negotiation, adversarial risk).
- Use the Quick Reference table to map failure mode → primitive.
- Open the per-mechanism playbook in
assets/templates/game-theory/ for the full problem/solution/launch-prompt template.
- For multi-failure scenarios, use the Composition Recipes or the full
assets/templates/game-theory/README.md to stack primitives.
- Check
references/patterns-scenarios-traps.md for trap coverage before shipping the mechanism.
- For agent-team applied recipes (team.yaml manifest fields, agent-team anti-patterns, decision checklist for team launches), load
agents-subagents/references/game-theory-agent-teams.md.
- For other domain applied recipes — pricing, paid advertising, CRO, security, market intel — see each domain skill's
references/game-theory-applied.md (or game-theory-pricing.md for startup-business-models).
ASCII Flow
Strategic interaction or incentive failure
-> Identify actors, payoffs, information, and repeatedness
-> Classify failure: attribution, routing, synthesis, trust, negotiation, adversarial risk
-> Select applied mechanism
+-- shared payoff only -> consider team theory instead
+-- divergent incentives -> continue with game-theory primitive
-> Check formal assumptions and pattern traps
-> Produce mechanism, launch rule, evidence requirement, and fallback
Related Skills
agents-subagents — agent-team applied recipes: team.yaml manifest fields, agent-team anti-patterns, decision checklistmarketing-paid-advertising — paid auction recipes (GSP/VCG, bid shading, budget pacing)startup-business-models — pricing recipes (VCG, Hotelling, Folk Theorem, signaling)marketing-cro — experimentation recipes (Thompson, MAB, sequential testing)software-security-appsec and qa-security-testing — defender/attacker recipes (Stackelberg, honeypots)startup-market-intel — competitive recipes (Stackelberg, Bertrand, Cournot, Hotelling positioning)
Fact-Checking
- Verify paper results (accuracy deltas, token counts) against primary arxiv sources before treating them as benchmarks.
- Mechanism effectiveness is task- and domain-specific. Test on a held-out sample before deploying at scale.
- Source links and verified dates in each per-mechanism file are the canonical evidence tier.
- If web access is unavailable, mark runtime-specific claims as unverified.
Learnings Loop
Before applying this skill on a non-trivial task, read learnings.consolidated.md in this directory (and learnings.md if present).
After applying it, if you encountered a pattern worth remembering, a mistake worth preventing, or a domain fact that surprised you, append one dated bullet to learnings.md via agents-skills-feedback-loop/scripts/append_learning.py. Do not modify SKILL.md itself.
