Game Theory Foundations

22 numbered applied game-theory primitives for strategic decision systems, backed by a formal theory map. Numbering is kept for stable links: the shared-payoff primitives (#1, #2, #4, #8, #11, #13, #16-#20, #22: aggregation, Shapley credit assignment, coalition routing) now live in foundations-team-theory, because nobody in them gains by misreporting. 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 Index
• Formal Supporting Theory
• Expert Judgment: When the Model Helps vs Misleads
• Anti-Patterns
• Misuse Boundaries
• Decision Checklist
• Composition Recipes
• Workflow
• Navigation

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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
Auction-Based Routing	Ad placement, task delegation, resource allocation	Sealed-bid truthful auction; highest-value-per-cost wins
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	Evidence-synthesis protocol (no incentive guarantee); dissent is a required section
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
Negotiation ZOPA/BATNA	Pricing, partnership terms, resource contention	Map BATNA/ZOPA per party; target overlap zone; use interests not positions
Per-Claim Credibility Scoring	Misinformation detection, adversarial content, security	Evidence quality × corroboration weight per claim; isolate high-risk claims
Generative Social Choice	Multi-stakeholder policy, diverse-user product decisions	Maximin selection across candidate outputs; preserve minority-signal coverage
Attested Delegation Contracts	Cross-trust subagent routing, agent marketplaces, external tools	Route by verified capability and bounded contract, not self-claimed quality

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When to Apply

Apply game-theory primitives when:

• Multiple agents/teams/users with potentially divergent incentives
• Synthesis where participants could gain by misreporting their claim, confidence, or vote
• Auctions, bidding, mechanism design, or pricing where strategic behaviour exists
• Repeated interactions where reputation, cooperation, or trust evolves
• 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
• Shared-payoff aggregation (vote, debate, best-of-N, BMV/RCS) where nobody gains by misreporting — use foundations-team-theory
• 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

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Primitive Index

The Quick Reference table lists the primitives that stay here. Handled by foundations-team-theory (shared payoff; live playbooks in ../foundations-team-theory/assets/templates/team-theory/). Redirect stubs, listed once: #1, #2, #4, #8, #11, #13, #16, #17, #18, #19, #20, #22.

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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	#6, #9, #10, #12
Solution concepts	Need dominance, minimax, Nash, Bayesian Nash, subgame-perfect, perfect Bayesian, or correlated equilibrium	#6, #9, #10
Mechanism and auction design	Need incentive compatibility, individual rationality, revelation principle, VCG, Myerson, reserves, or bid shading	#3, #7, #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	#6, #15 (Shapley credit assignment and coalition routing: team-theory #4, #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
Strategic failure analysis	Need collusion, equilibrium selection, Goodharting, manipulation, or off-equilibrium threats	all primitives

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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:

1. 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.
2. 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.
3. 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."
4. Is value created cooperatively or contested? Cooperative-game tools (Shapley, core) fit attribution and surplus-sharing (Shapley, team-theory #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.
5. 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.
6. 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 a rising profit gain Δ = (π − π_Nash)/(π_monopoly − π_Nash) over many rounds, not a spot price or a fixed markup over cost (LLM pricing agents reach supracompetitive prices: Fish, Gonczarowski & Shorrer, arXiv:2404.00806)
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; In the standard bilateral-trade model with independent private values and overlapping support, Myerson & Satterthwaite (1983) rule out jointly Bayesian incentive-compatible, individually rational, ex-post efficient and budget-balanced mechanisms; state these assumptions and the property relaxed
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	Use approximate/greedy VCG variants and disclose the resulting efficiency loss, or restrict to single-item/separable settings where exact VCG is tractable
Rules stated only in the prompt	A policy the participants can read but nothing enforces is cheap talk; under optimization pressure agents route around it	LLM participants told "do not collude" in a system prompt, with no state machine, sanction, or audit log behind it	Enforce in the orchestration layer, not the prompt — declare legal states, transitions, and sanctions outside the agents and have a controller apply them (Institutional AI, arXiv:2601.11369, Jan 2026: prompt-only constitutional policy was ineffective; governance-graph enforcement cut severe-collusion incidence in its simulated Cournot markets only)

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. A related abstract-level 2026 result: two agents set to verify each other over repeated rounds drifted into mutual approval across most models tested, sooner in stronger ones, and less shared history or narrower scope reduced it (arXiv 2609.24967; one environment, unreplicated). Lesson for review loops: the verifier gets a fresh context with no shared log with the author, and verification is paid on outcome, not on agreement.

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Anti-Patterns

Anti-Pattern	Game Theory Diagnosis	Fix
Unweighted majority vote over a correlated pool in high-stakes aggregation	Correlated errors pass; LLMs share biases	Keep majority/self-consistency as the matched-budget baseline that debate must beat (Choi, Zhu & Li, arXiv:2508.17536); add a reasoning-tree audit (#13, team theory) to trace 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 (team-theory #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	Self-critique calibration step (CritiCal-inspired) 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	Shared payoff → Beyond Majority Voting (#18) in foundations-team-theory; conflicting incentives → model the game here
Open-ended generation scored by lexical overlap	Semantically equivalent answers split the vote	Shared payoff → Radial Consensus Score (#19) in foundations-team-theory; conflicting incentives → model the game here
Consensus treated as permission to act	Wrong agreement becomes automated harm	Validate the candidate and permission independently; a conformal singleton (#20) is only a selection result
Routing by self-claimed delegate quality	Strategic or misconfigured delegates attract work	Attested Delegation Contracts (#21) — verify identity/capability and bound authority
Large team run as one flat panel	Duplicate work, coalition instability, synthesis overload	Coalition Formation Routing (team-theory #22) — explicit subteams before final synthesis

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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 (team-theory #4) 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; without one, shared payoff → foundations-team-theory, conflicting incentives → game theory
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)
Encoding mechanism rules as prompt instructions	Declarative prohibitions have no binding force under optimization pressure	Enforce legal states, transitions, and sanctions in the orchestration layer with an audit log
Scoring a negotiation agent on deal rate	Frontier models saturate deal rate while diverging widely on surplus extraction, belief calibration, and constraint compliance	Score surplus captured, cue use, and compliance separately (TERMS-Bench)

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Decision Checklist

• Routing: Is the problem multi-option with measurable fit signal? → auction (#3)
• Aggregation: Does synthesis combine claims with varying quality? → reasoning-tree audit (#13) + mechanism-design synthesis (#7)
• Trust calibration: Are participants heterogeneous in track record? → reputation gating (#5)
• Tradeoff detection: Are there multiple incommensurable objectives? → Pareto-Nash (#9) or negotiation (#12)
• Adversarial context: Claims may be injected or manipulated? → per-claim credibility scoring (#14)
• Multi-stakeholder output: Multiple user types with different needs? → generative social choice (#15)
• Long-running coordination rule: Has a measurable quality signal over many runs? → evolutionary coordination search (#10)
• Shared payoff (attribution, debate, best-of-N, act/escalate, topology): use foundations-team-theory, not this checklist
• Cross-trust delegation: Delegate can self-claim quality or authority? → attested delegation contract (#21)

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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) + team-theory #4 (Shapley ROI attribution) + #11 (confidence-weighted forecast) Inputs: Bidder count, valuation distribution (private or correlated), bid visibility (sealed vs. open), budget constraints. Rules: A sealed second-price auction has truthful bidding as a weakly dominant strategy under the standard independent private-value, quasi-linear, no-budget-constraint model. Otherwise choose the format from private/common/interdependent values, information revelation, dynamics, risk and budget constraints, collusion/shill exposure, and seller objective. Public bid visibility alone does not select first price or imply overbidding. Set a reserve only from a value-distribution model or calibrated market evidence; attribute ROI and calibrate forecasts separately. Outputs: Mechanism choice with assumptions, reserve rationale if any, expected objective estimate and uncertainty, per-component attribution, and incentive/abuse risks.

  Worked example — marketplace comparing sealed-bid formats. Bidders have private values [10, 8, 6, 4]. In a second-price auction, truthful bidding makes the value-10 bidder win and pay 8. A first-price comparison cannot infer equilibrium bids or revenue from these four values alone: bid shading depends on the bidders' value distribution, risk preferences, information, and number of bidders. Estimate those primitives or simulate calibrated bidding behavior before comparing revenue. Publicly revealing live bids also changes the game and invalidates the sealed-bid proof.

• 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. Outputs: 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.

• Partnership design: #6 (cooperation-defection payoff test) + #5 (reputation gating) + #12 (ZOPA negotiation) Inputs: Partner track record (prior-interaction count n, defection incidents), payoff matrix cells for cooperation vs. defection on the proposed arrangement, each party's BATNA and stated interests. Rules: Infer cooperation risk from the payoff structure, observability of defection, error/noise, continuation probability, and recovery path; interaction count alone is not a trust threshold. Tit-for-tat is a benchmark strategy, not a sufficient contract: accidental defections can trigger retaliation cycles. Use proportionate safeguards (milestones, escrow, audit, cure periods, or clawbacks) when exposure warrants them; locate ZOPA from reservation values and flag when none exists. Outputs: Recommended safeguards with their trigger, reputation evidence and uncertainty, ZOPA range or no-deal flag, and payoff-scale test result. If an LLM runs the negotiation: do not accept deal rate as the success metric — it saturates across frontier models while surplus extraction, belief calibration, and constraint compliance still diverge sharply (TERMS-Bench, arXiv:2605.13909, 2026, 13 systems). Score surplus captured against the counterpart's latent payoff, and treat a high close rate with low surplus as conceding, not winning.

• Cross-trust delegation: #21 (attested delegation contracts) + #3 (auction routing) + #5 (reputation gating) Inputs: Delegate pool, attested capabilities, authority boundary, acceptance criteria, failure policy. Rules: Filter by verified capability before any bidding; never route by self-claimed quality; give the winner a bounded contract; update reputation only from verified outcomes. Outputs: Eligible delegate set, chosen delegate, delegation contract, typed failure/recovery path, reputation update.

Shared-payoff stacks (multi-party teams, best-of-N, act/escalate, high-frequency prompt evolution, large-team coalitions, full-stack multi-agent applications): see "When Multi-Agent Pays" in foundations-team-theory; add reputation gating (#5) and attested delegation (#21) here only where members can game their standing or self-claim quality.

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Navigation

• Evidence tiers, verified finite-game/Shapley artifacts and uncertainty contract

• Per-mechanism playbooks: assets/templates/game-theory/ (one file per primitive)

• Composition guide: assets/templates/game-theory/README.md

• Formal theory map: references/formal-theory-map.md

• Current patterns, scenarios, and traps: references/patterns-scenarios-traps.md

• Domain-agnostic primitives overview: references/primitives-overview.md

• Sources: data/sources.json. Source links and verified dates in each per-mechanism file are the canonical evidence tier.

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Workflow

1. Identify the strategic failure mode in your system (attribution, routing, synthesis, trust, negotiation, adversarial risk). If actors share a single payoff (no divergent incentives), use team theory instead of a game-theory primitive.
2. Use the Quick Reference table to map failure mode → primitive.
3. Open the per-mechanism playbook in assets/templates/game-theory/ for the full problem/solution/launch-prompt template.
4. For multi-failure scenarios, use the Composition Recipes or the full assets/templates/game-theory/README.md to stack primitives.
5. Check references/patterns-scenarios-traps.md for trap coverage before shipping the mechanism.
6. 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.
7. 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).

Related Skills

• agents-subagents — agent-team applied recipes: team.yaml manifest fields, agent-team anti-patterns, decision checklist
• marketing-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)

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Learnings Loop

When prior decisions or pitfalls are relevant, consult learnings.consolidated.md if present; use learnings.md only for needed history or as the available fallback. Otherwise skip both.

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.