arXiv research

Study optimizes auction pricing for strategic bidders in repeated auctions.

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…

COAD maximizes online auction revenue by quantifying uncertainty without known distributions.

Real time bidding (RTB) enables demand side platforms (bidders) to scale ad campaigns across multiple publishers affiliated to an RTB ad exchange. While driving multiple campaigns for mobile app install ads via RTB, the bidder typically has to: (i) maintain each campaign's efficiency (i.e., meet advertiser's target cos…

Study learns optimal auctions from corrupted or perturbed bidder valuation samples.

Study learns optimal bidding strategy in auctions with dynamic values and aggregated feedback.

Study shows priority access in ELA auctions is less valuable due to volatility risks.

Algorithm learns to bid optimally in repeated first-price auctions with censored feedback.

A new RL approach optimizes reserve prices in multi-phase auctions, reducing revenue regret.

The private car license plates issued in Shanghai are bestowed the title of "the most expensive sheet iron all over the world", more expensive than gold. A citizen has to bid in an monthly auction to obtain a license plate for his new private car. We perform statistical analysis to investigate the influence of the mini…

Bidders in day-ahead electricity markets want to sell/buy electricity when their bids generate positive surplus and not to take an action when the reverse holds. However, non-convexities in these markets cause conflicts between the actions that the bidders want to take and the actual market results. In this work, we in…

Study non-stationary online auctions with semi-bandit feedback.

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 …

For the first time ever, we analyze a unique public procurement database, which includes information about a number of bidders for a contract, a final price, an identification of a winner and an identification of a contracting authority for each of more than 40,000 public procurements in the Czech Republic between 2006…

We develop extensions to auction theory results that are useful in real life scenarios. 1. Since valuations are generally positive we first develop approximations using the log-normal distribution. This would be useful for many finance related auction settings since asset prices are usually non-negative. 2. We formulat…

This paper studies an environment of simultaneous, separate, first-price auctions for complementary goods. Agents observe private values of each good before making bids, and the complementarity between goods is explicitly incorporated in their utility. For simplicity, a model is presented with two first-price auctions …

This paper tackles optimal bidding strategies in adversarial first-price auctions.

New framework for robust uncertainty quantification in strategic settings.

Permutation-equivariant neural networks improve auction mechanisms by reducing regret and sample complexity.

Paper generalizes strategic classification framework and introduces SVC for PAC-learning.

Study strategic dynamic pricing for buyers with unknown manipulation costs.

New framework reduces strategic manipulation cost for minority groups in fair classification.

New algorithms minimize regret in repeated auctions by estimating values and optimizing bids.

Study compares employers with and without anticipating strategic labor force responses.

New framework shows strategic behavior is actually a form of causal modeling.

New algorithm learns optimal policies in strategic MDPs with private types.

The paper develops a mathematical model for strategic shifts.

New findings show strategic interactions can undermine model expressiveness in machine learning.

Consequential decision-making typically incentivizes individuals to behave strategically, tailoring their behavior to the specifics of the decision rule. A long line of work has therefore sought to counteract strategic behavior by designing more conservative decision boundaries in an effort to increase robustness to th…

Paper presents a defense framework against adversarial examples.

Combinatorial auctions are formulated as frustrated lattice gases on sparse random graphs, allowing the determination of the optimal revenue by methods of statistical physics. Transitions between computationally easy and hard regimes are found and interpreted in terms of the geometric structure of the space of solution…

Strategic feature selection in high-stakes domains like healthcare.

Study allocates resources to strategic agents while balancing cost and incentives.

The paper analyzes trading strategies in a competitive market with incomplete information.

Transformer model forecasts electricity price spread for virtual bidding.

Algorithm learns optimal coordination for strategic agents in uncertain settings.

Randomised classifiers outperform deterministic ones in strategic classification.

Study optimal auction formats for maximizing MEV on Ethereum.

Study one-shot strategic classification under unknown costs, improving worst-case accuracy.

We consider a symmetric multi-players zero-sum game with two strategic variables. There are $n$ players, $n\geq 3$. Each player is denoted by $i$. Two strategic variables are $t_i$ and $s_i$, $i\in \{1, \dots, n\}$. They are related by invertible functions. Using the minimax theorem by \cite{sion} we will show that Nas…

We study a strategic version of the multi-armed bandit problem, where each arm is an individual strategic agent and we, the principal, pull one arm each round. When pulled, the arm receives some private reward $v_a$ and can choose an amount $x_a$ to pass on to the principal (keeping $v_a-x_a$ for itself). All non-pulle…

COBRA addresses strategic behavior in online platforms by ensuring truthful reporting without monetary incentives.

Strategic feature manipulation helps learners identify meaningful variables in online regression settings.

The paper analyzes strategic behavior in reinsurance transactions leading to Nash equilibria.

The results of a learning process depend on the input data. There are cases in which an adversary can strategically tamper with the input data to affect the outcome of the learning process. While some datasets are difficult to attack, many others are susceptible to manipulation. A resourceful attacker can tamper with l…

Paper analyzes bidding strategies in smart grid PDAs, proposing a new method that outperforms existing strategies.

Market-maker optimizes quotes based on strategic market-takers' behavior.

Optimizes decisions under strategic individual behavior.