Help us improve
Share bugs, ideas, or general feedback.
From skills-for-humanity
Analyses bidding strategies and auction design using game theory. Helps determine optimal bids, avoid winner's curse, and design efficient or revenue-maximising auctions.
npx claudepluginhub human-avatar/skills-for-humanityHow this skill is triggered — by the user, by Claude, or both
Slash command
/skills-for-humanity:s4h-game-theory-auctionThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
William Vickrey's 1961 discovery is one of the cleanest results in economics: in a second-price sealed-bid auction, bidding your true value is a *dominant strategy* — the best move regardless of what others bid. The mechanism works because you pay the second-highest bid, not your own. Overbidding your true value doesn't help you (you might win but pay more than the item is worth); underbidding ...
Computes optimal shaded bid for first-price sealed-bid auctions using (N-1)/N equilibrium, with adjustments for value distributions, risk aversion, and budget caps.
Applies Nash equilibrium analysis to competitive strategy, pricing, auctions, contracts, or negotiations where multiple rational parties make interdependent decisions — identifies stable strategy combinations and predicts where unstable strategies will drift.
Designs rules and incentive systems that produce desired outcomes even when players are self-interested. Useful for aligning incentives, eliciting honest behavior, and solving misaligned system behavior.
Share bugs, ideas, or general feedback.
William Vickrey's 1961 discovery is one of the cleanest results in economics: in a second-price sealed-bid auction, bidding your true value is a dominant strategy — the best move regardless of what others bid. The mechanism works because you pay the second-highest bid, not your own. Overbidding your true value doesn't help you (you might win but pay more than the item is worth); underbidding doesn't help you either (you might lose an item worth more than you'd have paid). So you bid your true value and let the second-highest bid determine the price. Vickrey received the Nobel Prize in 1996 for this result and related work.
First-price auctions are strategically different: you pay what you bid, so optimal play requires shading your bid below your true value. The optimal shade depends on the number of competitors (shade more with more competitors) and the distribution of their valuations (shade more when competition is intense). In equilibrium, first-price and second-price auctions generate the same expected revenue — the revenue equivalence theorem — under standard conditions.
The winner's curse is the most common failure mode in common-value auctions (where the item has an underlying objective value everyone is trying to estimate, rather than a private personal value). Winning means you bid highest, which means your estimate was the most optimistic among all bidders. In expectation, if you bid your unconditional estimate and win, you've overpaid — because winning reveals that you were the most optimistic, not the most accurate. The correct bid is your estimate conditional on winning, which is lower than your unconditional estimate.
Paul Milgrom and Robert Wilson (Nobel 2020) developed the modern theory of auction design, including the simultaneous ascending auction used in FCC spectrum allocation — showing how auction design directly affects both revenue and efficient allocation.
Step 1: Auction type identification Identify the auction format:
Framing check: Confirm the auction situation before continuing. State what you've identified — the auction format, whether the user is a bidder or designer, and the key parameters (item, number of competitors, value structure if apparent) — in one sentence, then use AskUserQuestion:
Step 2: Private vs. common value Determine the value structure:
Step 3: Optimal bidding strategy by type
Second-price (Vickrey): Bid your true value. This is a dominant strategy — it is best regardless of what others bid. No adjustment needed.
First-price sealed bid: Shade your bid below your true value. As a rough rule with n symmetric bidders: bid approximately (n−1)/n × your true value. With 2 bidders, bid 50% of your value; with 4 bidders, 75%; with 10 bidders, 90%. In practice: bid higher when competition is intense (many bidders, strong demand) because the shading needs to be small to remain competitive.
Ascending (English): Stay in the auction until the price exceeds your true value, then drop out. Never bid beyond your valuation. The private-value dominant strategy is identical in structure to the Vickrey auction.
Descending (Dutch): Accept at the price that equals your true value. No advantage to waiting longer (you risk losing), and accepting earlier costs you money.
Step 4: Winner's curse adjustment (common value only) In common-value settings, adjust your bid downward to correct for the selection bias of winning. Procedure:
Step 5: Auction design (for designers) Apply the following principles:
Before proceeding, use the AskUserQuestion tool. State your interpretation of the situation in 1–2 sentences — what is being analyzed and what the core question is — then ask:
Proceed based on their selection. If the user reframes, incorporate the correction before running any analysis.
Auction Type [Format identified: first-price sealed bid / second-price / ascending / descending / other]
Value Structure [Private value / common value / affiliated values — and the implication for strategy]
Optimal Bidding Strategy [Specific recommended strategy for this auction type — dominant strategy or optimal shade with reasoning]
Winner's Curse Adjustment (common value only) [Revised estimate after conditioning on winning, and the magnitude of the adjustment]
Specific Bid Recommendation [If a bidder: the recommended bid with precise reasoning. If asked to evaluate a strategy: assessment of whether it is optimal]
Designer Recommendations (if applicable) [Reserve price, format choice, revenue vs. efficiency trade-offs, multi-unit design considerations]
The revenue equivalence theorem holds under strong assumptions: symmetric bidders, private values, independent valuations, risk-neutral bidders. When these fail — bidders are asymmetric, values are affiliated, bidders are risk-averse — the revenue equivalence breaks down and format choice matters for revenue.
The winner's curse is not irrational behaviour corrected by experience alone. It is a structural consequence of the selection process: winning an auction carries information about the item's value, and that information updates your estimate downward. Even sophisticated bidders overbid in novel common-value contexts.
Auction analysis is a specialised application of mechanism design. For the general framework of designing rules to produce desired behaviour from self-interested players, use /s4h-game-theory-mechanism-design. For the equilibrium analysis of the auction (confirming which strategy is actually optimal for each player), use /s4h-game-theory-equilibrium.
Pairs with: /s4h-game-theory-mechanism-design (the general framework for auction design), /s4h-game-theory-equilibrium (equilibrium analysis of specific auction formats), /s4h-decision-expected-value (when the bidding decision is primarily about expected value under uncertainty, not strategic interaction).
After delivering this output, use AskUserQuestion to offer the next move:
/s4h-game-theory-mechanism-design — Refine the auction mechanism based on findings/s4h-probability-expected-value-calculation — Calculate expected value of different bidding strategies/s4h-strategy-intelligence — Gather information to improve bidding position