| name | "algo-ad-gsp" |
| description | "Implement Generalized Second Price auction for ad slot allocation and pricing. Use this skill when the user needs to understand search ad auctions, compute ad positions and costs-per-click, or analyze bidding dynamics — even if they say 'how does Google Ads auction work', 'ad rank calculation', or 'second price auction for ads'.". |
| allowed-tools | Read, Glob, Grep |
Generalized Second Price Auction
Overview
GSP allocates K ad slots to N bidders, assigning the highest bidder the top slot, second-highest the second slot, etc. Each winner pays the bid of the advertiser ONE POSITION BELOW them (per-slot second price). Used by Google Ads and Bing Ads. Runs in O(N log N) for sorting bids.
When to Use
Trigger conditions:
- Understanding search engine ad auction mechanics
- Computing ad position and cost-per-click from bid and quality data
- Analyzing bidding strategy in sponsored search
When NOT to use:
- When you need incentive-compatible truthful bidding (use VCG mechanism)
- When analyzing display/programmatic ad auctions (typically use first-price)
Algorithm
IRON LAW: GSP Is NOT Incentive-Compatible
Unlike Vickrey (single-item second-price) auctions, truthful bidding
is NOT a dominant strategy in GSP. Bidders may strategically shade
bids below their true value. The equilibrium depends on competitor bids.
Ad Rank = Bid × Quality Score (Google's variant adds format/extensions).
Phase 1: Input Validation
Collect: bids, quality scores (or ad rank scores) for all competing advertisers. Define available slot positions and their click-through rate multipliers.
Gate: All bids positive, quality scores in valid range.
Phase 2: Core Algorithm
- Compute Ad Rank for each advertiser: AdRank_i = Bid_i × QualityScore_i
- Sort advertisers by Ad Rank descending
- Assign top-K to slots 1 through K
- Compute payment: CPC_i = AdRank_{i+1} / QualityScore_i (price to maintain position)
- Last slot winner pays the minimum bid threshold
Phase 3: Verification
Check: all payments ≤ bids, positions ordered by Ad Rank, no advertiser pays more than their bid.
Gate: Payment ≤ bid for all winners, positions consistent.
Phase 4: Output
Return slot assignments with positions, CPCs, and estimated clicks.
Output Format
{
"slots": [{"advertiser": "A", "position": 1, "ad_rank": 8.5, "cpc": 2.10, "est_clicks": 100}],
"metadata": {"total_bidders": 15, "slots_available": 4, "auction_type": "gsp"}
}
Examples
Sample I/O
Input: Bidders: A(bid=3, QS=8), B(bid=4, QS=5), C(bid=2, QS=9). Slots: 2.
Expected: Ranks: A=24, C=18, B=20. Order: A(1st), B(2nd). CPC_A = 20/8 = 2.50, CPC_B = 18/5 = 3.60.
Edge Cases
| Input | Expected | Why |
|---|
| Tie in Ad Rank | Platform tiebreaker (historical CTR, etc.) | GSP needs strict ordering |
| One bidder | Wins slot 1, pays minimum CPC | No competition → floor price |
| Bid below threshold | Not eligible | Minimum bid requirement enforced |
Gotchas
- Quality Score is opaque: Google's QS includes expected CTR, ad relevance, and landing page experience. The exact formula is proprietary.
- Strategic bid shading: Since GSP isn't truthful, sophisticated advertisers shade bids. This means observed bids don't reflect true willingness to pay.
- Position ≠ value: Higher position gets more clicks but at higher CPC. The most profitable position may be #2 or #3, not #1.
- Budget constraints: GSP doesn't account for daily budgets. Budget-constrained advertisers must pace bids throughout the day.
- Broad match expansion: The auction includes query-expanded matches, which may have different conversion rates than exact matches.
References
- For Nash equilibrium analysis of GSP, see
references/gsp-equilibrium.md
- For comparison with VCG mechanism, see
references/gsp-vs-vcg.md
