{"query":"auction math","results":[{"url":"https://www.june.kim/reading/economics/econ-24/","title":"Econ Ch.24 — Auctions","snippet":"june.kim: Power Diagrams in Ad Auctions — geometric view of auction equilibria","license":"CC BY-SA","compilable":false,"semantic_score":0.7948479933855804,"rank_score":0.00003561516906774328},{"url":"https://en.wikipedia.org/wiki/Auction_theory","title":"Auction theory","snippet":"Auction theory is a branch of applied economics that deals with how bidders act in auctions and researches how the features of auctions incentivise predictable outcomes. Auction theory is a tool used to inform the design of real-world auctions. Sellers use auction theory to raise higher revenues while allowing buyers to procure at a lower cost. The confluence of the price between the buyer and seller is an economic equilibrium. Auction theorists design rules for auctions to address issues that can lead to market failure. The design of these rulesets encourages optimal bidding strategies in a variety of informational settings.[1] The 2020 Nobel Prize for Economics was awarded to Paul R. Milgrom and Robert B. Wilson \"for improvements to auction theory and inventions of new auction formats.\"[2]","license":"CC BY-SA","compilable":false,"semantic_score":0.7865178786027169,"rank_score":0.00003993986819159665},{"url":"https://june.kim/reading/economics/econ-24/","title":"Econ Ch.24 — Auctions","snippet":"june.kim: Power Diagrams in Ad Auctions — geometric view of auction equilibria","license":"CC BY-SA","compilable":false,"semantic_score":0.7948479933855804,"rank_score":0.00003561516906774328},{"url":"https://june.kim/letter-to-cloudx/","title":"An Open Letter to CloudX | june.kim","snippet":"I’ve spent the past few weeks writing about vector-space ad auctions, exploring how embedding geometry can replace keyword matching for LLM conversation inventory. The series kept circling back to an enforcement problem: the auction math only works if every participant can verify the exchange is running the published scoring function unmodified.","license":"CC BY-SA","compilable":false,"semantic_score":0.789938995273583,"rank_score":0.00003561516906774328},{"url":"https://www.june.kim/letter-to-cloudx","title":"An Open Letter to CloudX | June Kim","snippet":"I’ve spent the past few weeks writing about vector-space ad auctions, exploring how embedding geometry can replace keyword matching for LLM conversation inventory. The series kept circling back to an enforcement problem: the auction math only works if every participant can verify the exchange is running the published scoring function unmodified.","license":"CC BY-SA","compilable":false,"semantic_score":0.7899389671035105,"rank_score":0.00029446757764549017},{"url":"https://june.kim/letter-to-cloudx","title":"An Open Letter to CloudX | June Kim","snippet":"I’ve spent the past few weeks writing about vector-space ad auctions, exploring how embedding geometry can replace keyword matching for LLM conversation inventory. The series kept circling back to an enforcement problem: the auction math only works if every participant can verify the exchange is running the published scoring function unmodified.","license":"CC BY-SA","compilable":false,"semantic_score":0.7899389671035105,"rank_score":0.00011372854848006496},{"url":"https://www.june.kim/who-builds-it","title":"Who Builds It? | June Kim","snippet":"An embedding auction with an open scoring function, score = log(bid) - distance² / σ², eliminates this lever entirely. Every advertiser can compute their own score and every competitor’s score for any query point. The geometry is public, so there’s no hidden bid manipulation. The auction is transparent by construction.","license":"CC BY-SA","compilable":false,"semantic_score":0.7833247840895883,"rank_score":0.00016126248120157822},{"url":"https://june.kim/who-builds-it","title":"Who Builds It? | June Kim","snippet":"An embedding auction with an open scoring function, score = log(bid) - distance² / σ², eliminates this lever entirely. Every advertiser can compute their own score and every competitor’s score for any query point. The geometry is public, so there’s no hidden bid manipulation. The auction is transparent by construction.","license":"CC BY-SA","compilable":false,"semantic_score":0.7833247840895883,"rank_score":0.000036383700339612306},{"url":"https://june.kim/who-builds-it/","title":"Who Builds It? | june.kim","snippet":"An embedding auction with an open scoring function, score = log(bid) - distance² / σ², eliminates this lever entirely. Every advertiser can compute their own score and every competitor’s score for any query point. The geometry is public, so there’s no hidden bid manipulation. The auction is transparent by construction.","license":"CC BY-SA","compilable":false,"semantic_score":0.7833247840895883,"rank_score":0.00003561516906774328},{"url":"https://en.wikipedia.org/wiki/Winner%27s_curse","title":"Winner's curse","snippet":"^ McAfee, R. Preston; McMillan, John (1987), \"Auctions and Bidding\", Journal of Economic Literature, 25 (2): 699–738, JSTOR 2726107","license":"CC BY-SA","compilable":false,"semantic_score":0.782907157996032,"rank_score":0.00005075161600123006},{"url":"https://www.june.kim/power-diagrams-ad-auctions","title":"Power Diagrams for Ad Auctions | June Kim","snippet":"Everyone’s treating this as a product problem: ad formats, placement, user experience. The harder problem is mathematical: how do you run an auction when the thing being sold is a region of continuous, high-dimensional space?","license":"CC BY-SA","compilable":false,"semantic_score":0.7805837640127129,"rank_score":0.0018147545147881765},{"url":"https://june.kim/power-diagrams-ad-auctions","title":"Power Diagrams for Ad Auctions | June Kim","snippet":"Everyone’s treating this as a product problem: ad formats, placement, user experience. The harder problem is mathematical: how do you run an auction when the thing being sold is a region of continuous, high-dimensional space?","license":"CC BY-SA","compilable":false,"semantic_score":0.7805837640127129,"rank_score":0.0009412451507160435},{"url":"https://june.kim/power-diagrams-ad-auctions/","title":"Power Diagrams for Ad Auctions | june.kim","snippet":"Everyone’s treating this as a product problem: ad formats, placement, user experience. The harder problem is mathematical: how do you run an auction when the thing being sold is a region of continuous, high-dimensional space?","license":"CC BY-SA","compilable":false,"semantic_score":0.7805837640127129,"rank_score":0.00003561516906774328},{"url":"https://june.kim/set-it-and-forget-it/","title":"Set It and Forget It | june.kim","snippet":"In a power-diagram auction, advertisers declare three numbers: center, sigma, and bid. The bid is the hard part. Guessing margin × P(conversion) requires knowing your conversion rate, which requires running ads, which requires a bid. Circular.","license":"CC BY-SA","compilable":false,"semantic_score":0.7775256858743507,"rank_score":0.00003561516906774328},{"url":"https://june.kim/set-it-and-forget-it","title":"Set It and Forget It | June Kim","snippet":"In a power-diagram auction, advertisers declare three numbers: center, sigma, and bid. The bid is the hard part. Guessing margin × P(conversion) requires knowing your conversion rate, which requires running ads, which requires a bid. Circular.","license":"CC BY-SA","compilable":false,"semantic_score":0.7775256858743507,"rank_score":0.00006429600877293382},{"url":"https://www.june.kim/set-it-and-forget-it","title":"Set It and Forget It | June Kim","snippet":"In a power-diagram auction, advertisers declare three numbers: center, sigma, and bid. The bid is the hard part. Guessing margin × P(conversion) requires knowing your conversion rate, which requires running ads, which requires a bid. Circular.","license":"CC BY-SA","compilable":false,"semantic_score":0.7775256858743507,"rank_score":0.00024792205156786096},{"url":"https://en.wikipedia.org/wiki/Vickrey%E2%80%93Clarke%E2%80%93Groves_auction","title":"Vickrey–Clarke–Groves auction","snippet":"First, the outcome of the auction is determined by maximizing bids: the apples go to bidder A and bidder B, since their combined bid of $5 + $2 = $7 is greater than the bid for two apples by bidder C who is willing to pay only $6. Thus, after the auction, the value achieved by bidder A is $5, by bidder B is $2, and by bidder C is $0 (since bidder C gets nothing). Note that the determination of winners is essentially a knapsack problem.","license":"CC BY-SA","compilable":false,"semantic_score":0.7749680600351325,"rank_score":0.000047508091658340036},{"url":"https://www.june.kim/relocation-fee-dividend","title":"Relocation Fee Dividend | June Kim","snippet":"The auction clears using the embedding-space scoring function: score = log(price) - distance² / σ²","license":"CC BY-SA","compilable":false,"semantic_score":0.7702688792954081,"rank_score":0.00017839664430719637},{"url":"https://june.kim/relocation-fee-dividend/","title":"Relocation Fee Dividend | june.kim","snippet":"The auction clears using the embedding-space scoring function: score = log(price) - distance² / σ²","license":"CC BY-SA","compilable":false,"semantic_score":0.7702688792954081,"rank_score":0.00003561516906774328},{"url":"https://june.kim/relocation-fee-dividend","title":"Relocation Fee Dividend | June Kim","snippet":"The auction clears using the embedding-space scoring function: score = log(price) - distance² / σ²","license":"CC BY-SA","compilable":false,"semantic_score":0.7702688792954081,"rank_score":0.00004658354022615498}],"total":20}