We provide an exact analytical solution of the Nash equilibrium for - price auctions. We also introduce a new type of auction and demonstrate that it has fair solutions other than the second price auctions, therefore paving the way for replacing second price auctions.
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29 results for “second-price”
Efficient algorithms for second-price auctions with action-dependent censoring.
problem Sequential bidding strategies for repeated auctions with incomplete information.
method Proposed novel UCB-like algorithms for second-price auctions in a stochastic setting.
result Significant improvement in worst-case regret, especially for low item values.
Optimizes bidding strategy for Maker Protocol auctions.
problem Minimizing costs in Maker Protocol auctions.
method Developed and optimized a bidding function using historical data.
result Most auctions end at higher prices than optimal recommendations.
Study learns optimal bidding strategy in auctions with dynamic values and aggregated feedback.
problem Optimizing bidding in auctions with time-dependent values and limited feedback.
method Combines plug-in estimators with differential-equation characterization of optimal policy.
result Achieves near optimal regret bounds for learning optimal policy.
We investigate contextual online learning with nonparametric (Lipschitz) comparison classes under different assumptions on losses and feedback information. For full information feedback and Lipschitz losses, we design the first explicit algorithm achieving the minimax regret rate (up to log factors). In a partial feedb…
We study revenue optimization learning algorithms for repeated second-price auctions with reserve where a seller interacts with multiple strategic bidders each of which holds a fixed private valuation for a good and seeks to maximize his expected future cumulative discounted surplus. We propose a novel algorithm that h…
Objective Variables for Probabilistic Revenue Maximization in Second-Price Auctions with Reservestat.ML
Many online companies sell advertisement space in second-price auctions with reserve. In this paper, we develop a probabilistic method to learn a profitable strategy to set the reserve price. We use historical auction data with features to fit a predictor of the best reserve price. This problem is delicate - the struct…
A new RL approach optimizes reserve prices in multi-phase auctions, reducing revenue regret.
problem Optimizing reserve prices in multi-phase second-price auctions with noisy and potentially untruthful bidders.
method Combines RL techniques with buffer periods, a novel algorithm, and LSVI-UCB extension.
result Achieves optimal revenue regret under known and unknown noise conditions.
In this paper, we study the non-stationary online second price auction problem. We assume that the seller is selling the same type of items in rounds by the second price auction, and she can set the reserve price in each round. In each round, the bidders draw their private values from a joint distribution unknown t…
Some online advertising offers pay only when an ad elicits a response. Randomness and uncertainty about response rates make showing those ads a risky investment for online publishers. Like financial investors, publishers can use portfolio allocation over multiple advertising offers to pursue revenue while controlling r…
Study optimal auction formats for maximizing MEV on Ethereum.
problem Maximizing extractable value from Ethereum auctions.
method Empirical analysis of 2.2 million transactions, modeling affiliation among bidders.
result English and second-price sealed-bid auctions dominate other formats, with significant revenue losses.
Optimistic Mirror Descent framework improves bidding strategies in non-stationary first-price auctions.
problem Optimizing bidding strategies in non-stationary first-price auctions.
method Introducing Optimistic Mirror Descent (OMD) framework with novel optimism configuration.
result Minimax-optimal dynamic regret rates achieved for non-stationary first-price auctions.
Study shows priority access in ELA auctions is less valuable due to volatility risks.
problem Impact of volatility on time-based transaction ordering policies.
method Single-round model with risk-averse bidders, tested using ETH price data.
result Priority access is discounted due to volatility and risk aversion.
This paper tackles optimal bidding strategies in adversarial first-price auctions.
problem How to bid optimally and efficiently in adversarial first-price auctions.
method Developed a minimax optimal online bidding algorithm leveraging expert-chaining structure and exploiting product structure.
result Achieved an regret, superior to existing algorithms.
We present an original theorem in auction theory: it specifies general conditions under which the sum of the payments of all bidders is necessarily not identically zero, and more generally not constant. Moreover, it explicitly supplies a construction for a finite minimal set of possible bids on which such a sum is not …
Real-time bidding (RTB) systems, which utilize auctions to allocate user impressions to competing advertisers, continue to enjoy success in digital advertising. Assessing the effectiveness of such advertising remains a challenge in research and practice. This paper proposes a new approach to perform causal inference on…
Efficient methods estimate bid and value distributions in auctions.
problem Estimating bid and value distributions in auctions with limited information.
method Non-parametric estimation algorithms for first- and second-price auctions.
result Uniform estimation bounds for bid and value distributions, independent of distributions being estimated.
In recent years, RTB(Real Time Bidding) becomes a popular online advertisement trading method. During the auction, each DSP(Demand Side Platform) is supposed to evaluate current opportunity and respond with an ad and corresponding bid price. It's essential for DSP to find an optimal ad selection and bid price determina…
In this paper, we study the stochastic version of the one-sided full information bandit problem, where we have arms , and playing arm would gain reward from an unknown distribution for arm while obtaining reward feedback for all arms . One-sided full information bandit ca…
We consider a dynamic pricing problem for repeated contextual second-price auctions with multiple strategic buyers who aim to maximize their long-term time discounted utility. The seller has limited information on buyers' overall demand curves which depends on a non-parametric market-noise distribution, and buyers may …
Deep network optimizes ad bidding for first-price auctions.
problem Optimizing bid prices for first-price auctions in online advertising.
method Introduced a deep distribution network for optimal bidding.
result Algorithm outperforms previous methods in terms of surplus and eCPX metrics.
Extends PoS proof-of-stake transaction fee mechanism with miner utility model.
problem Designing a transaction fee mechanism for PoS protocol that incorporates miner utility.
method Introduced a new mechanism (BSP(θ)) incorporating a parameter θ to ensure user and miner incentives.
result The new mechanism (BSP(θ)) satisfies user and miner incentives and contract proofness.
New model bridges pricing and reserving for insurance claims.
problem Incomplete claim data due to reporting and settlement delays.
method Develops an occurrence and development model to estimate both claims and premiums.
result Effective resolution of pricing and reserving inconsistencies.
Ad exchanges use CORP to set reserve prices against strategic buyers.
problem Setting optimal reserve prices in ad exchanges with strategic buyers.
method Proposes CORP policy to learn and set reserve prices robustly.
result Achieves sublinear regret in unknown noise distribution.
In programmatic advertising, ad slots are usually sold using second-price (SP) auctions in real-time. The highest bidding advertiser wins but pays only the second-highest bid (known as the winning price). In SP, for a single item, the dominant strategy of each bidder is to bid the true value from the bidder's perspecti…
Optimizes web publisher revenues from RTB auctions.
problem Maximizing revenue from RTB auctions with limited information.
method Incremental time-weighted matrix factorization for user and placement profiles; Aalen's Additive model for censored bid predictions.
result Significant revenue increase for web publishers.
We consider the problem of a single seller repeatedly selling a single item to a single buyer (specifically, the buyer has a value drawn fresh from known distribution in every round). Prior work assumes that the buyer is fully rational and will perfectly reason about how their bids today affect the seller's decisio…
New algorithm tackles self-selection bias in estimating linear regressors.
problem Estimating linear regressors with self-selection bias in dimensions.
method First local convergence algorithm for self-selection, reducing to coarsening problem.
result Improves running time of previous algorithms by a poly(d, k, 1/ε) factor.
Over the last decade, digital media (web or app publishers) generalized the use of real time ad auctions to sell their ad spaces. Multiple auction platforms, also called Supply-Side Platforms (SSP), were created. Because of this multiplicity, publishers started to create competition between SSPs. In this setting, there…