Improved learning algorithm for first-price auctions reduces regret significantly.
problem Challenges in learning optimal bidding strategies for first-price auctions.
method Introduced novel ideas to achieve lower regret in sequential learning.
result Achieved l o g 2 ( T ) log^2(T) l o g 2 ( T ) regret when opponents' bid distribution is known, and T 1 / 3 + ε T^{1/3+ ε} T 1/3 + ε regret in learning case. Motivated by online advertising auctions, we consider repeated Vickrey auctions where goods of unknown value are sold sequentially and bidders only learn (potentially noisy) information about a good's value once it is purchased. We adopt an online learning approach with bandit feedback to model this problem and derive …
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.
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.
Optimizes SSP's header bidding strategy using Thompson Sampling.
problem Maximizing ad revenue in a competitive SSP market.
method Thompson Sampling algorithm with particle filter for correlated contexts.
result Significantly outperforms classical approaches in real datasets.
Study analyzes how timely information affects market efficiency and profit allocation.
problem Effects of differential information and mutual learning on market efficiency and price discovery.
method Interactive market setup with sequential auctions, differential signals, and dynamic programming.
result Evidence supports exploiting new information and market efficiency, with risk-adjusted gains and risk-averse agents.
Exact solution found for k-price auctions, introducing a new fair auction type.
problem Finding fair solutions for auction mechanisms.
method Exact analytical solution for k-price auctions and introduction of a new auction type.
result New auction type provides fair solutions.
A new mechanism optimizes data marketplace pricing efficiently.
problem Designing fair and efficient pricing mechanisms for data marketplaces.
method Two-stage approach: auctions to estimate value distributions, then optimal posted prices.
result MAPP achieves optimal revenue with minimal price discrimination.
The paper extends a learning heuristic to high-dimensional contexts, reducing the risk of unusual actions.
problem Sequential learning problems in high dimensions, especially in dynamic pricing and auctions.
method Introducing a conservative ε t ε_t ε t -greedy rule that limits the adoption of new actions to a focused set of promising actions. result Reasonable bounds for cumulative regret and improved regret bound for conservative version compared to non-conservative.
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.
Equity auctions show linear price impact up to a large volume, then non-linear.
problem Understanding price impact in equity auctions.
method High-quality data analysis of Paris stock exchange auctions.
result Price impact is linear up to a large volume, then becomes non-linear.
Paper tackles NP-hard multi-agent planning with reinforcement learning.
problem Solving NP-hard multi-agent, multi-task planning problems with time-dependent rewards.
method Developed a reinforcement learning framework using mean-field inference and auction-based selection.
result Achieved near-optimality and transferability in solving MRRC and IPMS problems.
Study reveals patterns in US stock opening and closing auctions.
problem Investigating the dynamics of US equities opening and closing auctions.
method Analysis of historical data and conditional analysis of auction prices.
result Opening and closing auctions have distinct price reactions to order placements/cancellations.
The paper tackles auction market design flaws by randomizing closing times and optimizing transaction fees.
problem Strategic traders exploit accumulated information to delay their orders, distorting auction efficiency.
method Randomizing auction closing times and designing optimal transaction fees policies.
result Policies encourage strategic traders to send orders earlier, improving auction market efficiency.
The study compares uniform-price and discriminatory auctions in terms of learning difficulty.
problem Comparing the learning difficulty of uniform-price and discriminatory multi-unit auctions.
method Characterization of learning difficulty through regret minimization in both full-information and bandit feedback settings.
result Regret scales similarly for both auction formats under full-information, but uniform-price auctions can achieve faster learning rates.
Study creates a dataset for fraud detection in online auctions.
problem Difficulty in detecting Shill Bidding (SB) due to similarity with normal bidding.
method Scraped eBay auctions, preprocessed data, created SB dataset.
result Shared preprocessed auction dataset for fraud detection.
AHEAD improves financial market efficiency through ad-hoc auctions.
problem Improving financial market efficiency and reducing transaction costs.
method Introducing a new matching design (AHEAD) for electronic markets where participants can trade at a fixed price and trigger auctions when unsatisfied.
result A Nash equilibrium is achieved in the market, and ad-hoc auctions are more relevant and efficient than periodic auctions and continuous limit order books.
Study on heavy tails in closing auction returns, explaining imbalance through limit order submission.
problem Understanding heavy tails in closing auction return distributions.
method Used the stochastic call auction model of Derksen et al. (2020a) to derive and verify a relation between tail exponents.
result Large closing price fluctuations are not caused by large market orders, but by imbalance in limit orders.
Study proposes a machine learning method for bid shading in first-price auctions.
problem Maintaining strategy equilibrium in first-price auctions.
method Machine learning approach to model optimal bid shading.
result Demonstrates superiority and robustness of new approach across various metrics.
Strategic traders adjust indicative prices near auctions to achieve nearly diffusive outcomes.
problem Achieving diffusive price behavior in Paris Stock Exchange auctions.
method Analyzing the diffusive properties of indicative auction prices and the strategic behavior of traders.
result Strategic traders adjust their order submission times to achieve nearly diffusive price behavior.
Study on efficiency of Dutch auctions on blockchains considering various parameters.
problem Efficiency and fairness in Dutch auctions on blockchains.
method Modeling Dutch auctions with Poisson process and geometric Brownian motion, computing expected losses and time-to-fill.
result Tradeoff between speed and quality in Dutch auctions, useful for setting parameters.
Develops auction theory for real-life applications with positive valuations.
problem Real-life auction settings with positive valuations and interdependent bidders.
method Approximations using log-normal distribution, positive symmetric discrete distribution, and interdependent valuations.
result New auction theory results applicable to finance and procurement.
New method for causal inference in RTB advertising auctions.
problem Measuring the effectiveness of online advertising in RTB systems.
method Adapted Thompson sampling algorithm for causal inference.
result The method outperforms existing methods in estimating advertising effects.
Market makers minimize regret by strategically setting bid and ask prices.
problem Minimizing regret in sequential market making decisions.
method Characterizes market making regret under various assumptions and distributions.
result Unveils a connection to first-price auctions and dynamic pricing.
Optimal bidding strategy for multi-platform ad auctions under budget constraints.
problem Optimizing ad placements for budget-constrained advertisers across multiple platforms.
method Developed an optimal bidding strategy for non-incentive-compatible auctions with budget constraints.
result Maximized total utility across auctions while satisfying budget constraints in expectation.
Paper addresses selection bias in online advertising auctions.
problem Selection bias affects auction truthfulness and advertiser profits.
method Theoretical analysis combined with multi-task learning.
result Selection bias can be significantly reduced using multi-task learning.
MiFID II impacts European stock liquidity and price formation.
problem Impact of MiFID II on European stock liquidity and price formation.
method Analyzed effects of MiFID II on European stock markets, focusing on intraday and closing auction liquidity and tick size changes.
result Closing auction volumes increased and price formation became more efficient after MiFID II.
New algorithm optimizes auction prices in real-time.
problem Maximizing revenue in online auctions with high frequency data.
method First real-time algorithm for online learning of monopoly prices.
result Achieves constant time and memory complexity for updates.
This thesis improves OCO algorithms for dynamic data environments.
problem Sequential, changing data in big data environments.
method Designing algorithms to adapt to changing environments.
result Improved algorithms for online resource allocation.
Study adapts liquidity model to equity auctions, revealing accelerated event rates and reduced price impact.
problem Understanding and predicting price dynamics in equity auctions.
method Adapted latent/revealed order book framework to equity auctions, measuring order submissions, cancellations, and diffusion rates.
result Equity auctions exhibit accelerated event rates leading to reduced price impact and decreased volatility.
Proposes a framework for modeling RTB auctions using point processes.
problem Modeling and optimizing repeated auctions in the RTB ecosystem.
method Develops a stochastic framework using point processes to model and optimize RTB auctions.
result The proposed framework can be approximated to a Poisson point process, enabling the use of established properties.
The paper examines how builders in Ethereum auctions can defect and replicate winning MEV opportunities, affecting searchers' bidding strategies.
problem Commitment problem in Ethereum auctions where builders can defect and replicate winning MEV opportunities.
method Modeling and analysis of searchers' bidding strategies and the resulting equilibrium, using libMEV dataset.
result The equilibrium is piecewise, with the cost of imperfect commitment depending on replicability and competition. There is sharp heterogeneity across MEV types.
Paper proposes auction method for smart derivatives to avoid disputes.
problem Disputes over derivative liquidation processes in smart contracts.
method Defines an auction type resolution for smart derivatives.
result Proposes a beneficial method for smart derivatives participants.
Optimizes auction mechanisms in e-commerce search ads to balance revenue and user experience.
problem Optimizing auction mechanisms in e-commerce search ads while maintaining quality users and ROI.
method Developed a practical convex optimization formulation and auction simulation system to estimate business indicators.
result Proper entropy regularization can maximize revenue while constraining other business indicators.
We consider a class of auctions (Lowest Unique Bid Auctions) that have achieved a considerable success on the Internet. Bids are made in cents (of euro) and every bidder can bid as many numbers as she wants. The lowest unique bid wins the auction. Every bid has a fixed cost, and once a participant makes a bid, she gets…
Study builds labeled shill bidding dataset for auction fraud detection.
problem Difficulty in detecting shill bidding in auctions.
method Hierarchical clustering CURE for systematic labeling of fraud data.
result CURE approach effectively labels shill bidding instances with multidimensional features.
Optimal auction duration affects price formation in markets.
problem Improving price formation in auction markets.
method Derived the optimal auction duration and analyzed its impact on price formation.
result Optimal auction durations are from 2 to 10 minutes, improving price formation.
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.
Optimizes reserve prices for first-price auctions to maximize revenue.
problem Optimizing reserve prices for first-price auctions in display advertising.
method Gradient-based algorithm to adaptively update and optimize reserve prices based on bidder responsiveness to experimental shocks.
result Revenue optimization in first-price auctions can be decomposed into demand and bidding components, and techniques are introduced to reduce variance of each.
Algorithm learns to bid optimally in repeated first-price auctions with censored feedback.
problem Learning to bid optimally in repeated first-price auctions with incomplete feedback.
method Developed an algorithm exploiting the specific feedback structure and payoff function of first-price auctions.
result Achieved a near-optimal O ~ ( T ) \widetilde{O}(\sqrt{T}) O ( T ) regret bound for first-price auctions. Permutation-equivariant neural networks improve auction mechanisms by reducing regret and sample complexity.
problem Designing optimal auction mechanisms that balance revenue and bidders' regret.
method Introduced permutation-equivariant neural networks to auction mechanisms.
result Permutation-equivariant neural networks decrease expected ex-post regret and improve model generalizability.
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.
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.
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 O ~ ( T ) \widetilde{O}(\sqrt{T}) O ( T ) regret, superior to existing algorithms. Formal methods verify continuous auctions at exchanges.
problem Ensuring fairness and correctness in continuous auctions.
method Formal specification, design, and verification of continuous double auctions.
result A verified algorithm satisfies natural properties of auctions.
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.
New neural network architecture for auction design exploiting permutation symmetry.
problem Designing incentive-compatible auctions that maximize expected revenue.
method Constructed a permutation-equivariant neural network architecture.
result Permutation-equivariant architectures can perfectly recover optimal mechanisms.
Neural networks can learn optimal auction mechanisms and satisfy mode connectivity.
problem Optimal auction design in complex settings.
method Generalized RochetNet and affine maximizer auctions.
result Neural networks (RochetNet and generalized version) satisfy mode connectivity.