| name | mechanism-design-planner |
| description | Designs auctions, matching mechanisms, pricing schemes, and incentive structures using mechanism-design principles - Incentive Compatibility (IC), Individual Rationality (IR), revenue equivalence, and budget balance. Use when designing a marketplace pricing model, a referral program, an internal resource allocation, a Request for Proposal (RFP) or procurement process, an auction format, or any allocation rule where participants act strategically. |
Mechanism Design Planner
Reverse-engineers the rules so that participants acting in their own interest produce the outcome you want.
What this skill is
A workflow that frames an allocation problem as a mechanism-design problem, surfaces the design objectives (efficiency, revenue, fairness, simplicity), picks the right mechanism class (first-price, second-price, Vickrey-Clarke-Groves (VCG), posted-price, matching, scoring auction), checks the canonical properties (Incentive Compatibility (IC), Individual Rationality (IR), budget balance, no-deficit), and stress-tests against collusion and gaming.
What it solves
- Mechanisms that are gamed because participants have private information and weak incentives to reveal it
- Auctions that maximize revenue in theory but kill participation in practice
- Pricing models that look optimal until customers stop adopting
- Allocation rules that produce inefficient outcomes (the wrong party wins)
- Programs (referrals, compensation plans, Requests for Proposal (RFPs)) that produce unintended behaviors
When to invoke
- Designing a marketplace pricing model (commissions, dynamic pricing, surge)
- Procurement or RFP design where bidders are strategic
- Auction format decisions (English, sealed-bid, second-price, combinatorial)
- Internal resource allocation (compute quota, headcount, budget)
- Matching problems (school choice, dorm assignment, kidney exchange, marketplace pairing)
- Incentive compensation, referral programs, bug bounties
Phase 1: State the problem formally
- Participants - buyers, sellers, candidates, applicants
- Private information - what does each participant know that others (and the designer) don't?
- Allocation outcome - what is being allocated and to whom?
- Transfers - what payments or rewards move between parties?
- Designer's objective - efficiency, revenue, fairness, participation, simplicity (pick 1-2 primary)
- Constraints - budget, legal, computational, fairness
Without explicit private information, mechanism design doesn't apply - it's just an allocation rule.
Phase 2: Specify desired properties
Choose which properties the mechanism must satisfy:
| Property | Definition | When critical |
|---|
| Incentive Compatibility (IC) | Truth-telling is a (dominant or Bayesian) best response | Always desirable; near-mandatory at scale |
| Individual Rationality (IR) | Participating beats opting out for each type | Required for voluntary participation |
| Pareto efficiency | No reallocation makes everyone weakly better off | Welfare-maximizing settings |
| Budget balance | Mechanism's payments sum to zero (or non-positive) | Self-sustaining without subsidy |
| No-deficit | Mechanism doesn't lose money | Weaker than budget balance |
| Strategy-proofness | Truth is a dominant strategy | When robust simplicity matters |
| Pareto optimality of matching | No coalition can improve via reassignment | Matching markets |
Standard tension: efficiency, IR, IC, and budget balance simultaneously is often impossible (the Myerson-Satterthwaite result for bilateral trade). State the trade-off explicitly.
Phase 3: Pick a mechanism class
| Class | Use case | Notes |
|---|
| English (open ascending) | Single-item, transparent, common | Familiar; reveals second-highest value |
| Sealed-bid first-price | Single-item, sealed, paid own bid | Shading optimal; complex strategy |
| Sealed-bid second-price (Vickrey) | Single-item, sealed, paid 2nd price | Truth-telling is a dominant strategy (IC) |
| Vickrey-Clarke-Groves (VCG) | Multi-item, combinatorial | IC plus efficient; revenue may be low; complex |
| Posted-price | Many small buyers, low-friction | Loses revenue from high-value buyers; simple |
| Scoring auction | Multi-attribute (price plus quality plus delivery) | Common in procurement |
| Combinatorial | Bundle allocations (spectrum, gates) | Computationally hard; needs careful design |
| Deferred-acceptance (Gale-Shapley) | Two-sided matching (schools, residencies) | Strategy-proof for proposing side |
| Top trading cycles | Pareto-efficient matching with priorities | Strategy-proof; efficient |
Pick the mechanism whose properties match your objectives.
Phase 4: Revenue equivalence and expected outcomes
The Revenue Equivalence Theorem: under standard assumptions (risk-neutral bidders, Independent Private Values (IPV), symmetric, no reserve), all efficient single-item auctions yield the same expected revenue to the seller.
Implication: format choice is often not about expected revenue - it's about variance, transparency, complexity, IC, and resistance to collusion.
Compute under the chosen mechanism:
- Expected efficiency (probability the highest-value participant wins)
- Expected revenue (closed-form for standard auctions; simulation for non-standard)
- Expected participation (do all types prefer joining?)
- Worst-case loss or regret
Phase 5: Reserve prices, fees, and quotas
- Reserve price - minimum price; trades efficiency for revenue (excludes low-value buyers)
- The Myerson optimal reserve: where marginal revenue equals zero; depends on value distribution
- Entry fee - screens out low-value participants; can reduce participation
- Quotas or caps - limit allocation per participant; useful in matching to prevent monopolization
- Subsidies - encourage participation on the thin side of the market
Each lever has efficiency, revenue, and participation trade-offs. Quantify each.
Phase 6: Collusion and gaming resistance
Stress-test the mechanism:
- Bid rotation - bidders take turns winning
- Sham bidding - phantom bidders inflating second-price auctions
- Coordination via signaling - early bids signaling intentions (open auctions susceptible)
- Sniping - last-second bids in soft-close auctions
- Misreporting - overstating need to get a larger allocation
- Reverse-engineering scores - gaming a scoring formula
Design defenses:
- Hard close versus soft close
- Anonymous bidders
- Sealed versus open
- Reserve plus proxy bids
- Audit plus penalty
- Periodic re-randomization
Phase 7: Simulate before launch
For any non-trivial mechanism, simulate:
- 1,000+ trials with realistic participant value distributions
- Stress with strategic and non-strategic mix
- Compute mean and worst-case allocation efficiency
- Compute revenue distribution, not just expected revenue
- Test edge cases: thin participation, lopsided value distributions, single dominant bidder
Output
- Problem statement: participants, private information, allocation, transfers, objective
- Property requirements with explicit trade-offs noted
- Recommended mechanism class with reasoning
- Parameter settings: reserve, fees, quotas
- Expected outcomes: efficiency, revenue, participation
- Collusion and gaming threat map with mitigations
- Simulation results across realistic scenarios
- Launch plan with monitoring metrics to detect manipulation
Operating rules
Always
- State the private information explicitly
- Choose 1-2 primary objectives (efficiency, revenue, fairness, simplicity)
- Pick a mechanism whose properties match the objectives
- Stress-test against collusion and gaming
- Simulate before launch
Never
- Use first-price when truthful revelation matters
- Promise efficiency, IR, IC, and budget balance simultaneously for bilateral trade
- Skip reserve-price tuning
- Launch a complex mechanism without participant simulation
- Ignore behavioral deviations from rational play
