We study the communication complexity of welfare maximization in combinatorial auctions with bidders from either a standard valuation class (which require exponential communication to state, such as subadditive or XOS), or arbitrary succinct valuations (which can be fully described in polynomial communication, such as single-minded). Although succinct valuations can be efficiently communicated, we show that additional succinct bidders have a nontrivial impact on communication complexity of classical combinatorial auctions.

We investigate prophet inequalities with competitive ratios approaching 1, seeking to generalize k-uniform matroids. We first show that large girth does not suffice: for all k, there exists a matroid of girth ≥ k and a prophet inequality instance on that matroid whose optimal competitive ratio is 1/2. Next, we show k-fold matroid unions do suffice: we provide a prophet inequality with competitive ratio 1-O(√{(log k)/k}) for any k-fold matroid union. Our prophet inequality follows from an online contention resolution scheme. The key technical ingredient in our online contention resolution scheme is a novel bicriterion concentration inequality for arbitrary monotone 1-Lipschitz functions over independent items which may be of independent interest. Applied to our particular setting, our bicriterion concentration inequality yields "Chernoff-strength" concentration for a 1-Lipschitz function that is not (approximately) self-bounding.

We consider a revenue-optimizing auctioneer in single-dimensional environments with matroid feasibility constraints. Akbarpour and Li [2020] argue that any revenue-optimal, truthful, and credible mechanism requires unbounded communication. Recent works [Chitra et al., 2023, Essaidi et al., 2022, Ferreira and Weinberg, 2020] circumvent their impossibility for single-items setting through the use of cryptographic commitments and blockchains. We extend their results to matroid feasibility constraints.

We study the communication complexity of truthful combinatorial auctions, and in particular the case where valuations are either subadditive or single-minded, which we denote with SubAddUSingleM. We show that for three bidders with valuations in SubAddUSingleM, any deterministic truthful mechanism that achieves at least a 0.366-approximation requires$\exp(m)$communication. In contrast, a natural extension of [Fei09] yields a non-truthful$\text{poly}(m)-\mathbf{communication}$protocol that achieves a$\frac{1}{2}-\mathbf{approximation}$, demonstrating a gap between the power of truthful mechanisms and non-truthful protocols for this problem. Our approach follows the taxation complexity framework laid out in [Dob16b], but applies this framework in a setting not encompassed by the techniques used in past work. In particular, the only successful prior application of this framework uses a reduction to simultaneous protocols which only applies for two bidders [AKSW20], whereas our three-player lower bounds are stronger than what can possibly arise from a two-player construction (since a trivial truthful auction guarantees a$\frac{1}{2}- \mathbf{approximation}$for two players).

The classical prophet inequalities problem introduced by Krengel and Sucheston [1977, 1978] assumed complete knowledge of distributions. However, such an assumption may be unrealistic both in practice and for some applications.

Approximate Competitive Equilibrium from Equal Incomes (A-CEEI) is an equilibrium-based solution concept for fair division of discrete items to agents with combinatorial demands. In theory, it is known that in asymptotically large markets:

With a wide range of applications, stochastic matching problems have been studied in different models, including prophet inequality, Query-Commit, and Price-of-Information. While there have been recent breakthroughs in all these settings for bipartite graphs, few non-trivial results are known for general graphs. In this paper, we study the random order vertex arrival contention resolution scheme for matching in general graphs, which is inspired by the recent work of Ezra et al. (EC 2020). We design an 8/15-selectable batched RCRS for matching and apply it to achieve 8/15-competitive/approximate algorithms for all the three models. Our results are the first non-trivial results for random order prophet matching and Price-of-Information matching in general graphs. For the Query-Commit model, our result substantially improves upon the 0.501 approximation ratio by Tang et al. (STOC 2020). We also show that no batched RCRS for matching can be better than 1/2+1/(2e²) ≈ 0.567-selectable.

Understanding search intents behind queries is of vital importance for improving search performance or designing better evaluation metrics. Although there exist many efforts in Web search user intent taxonomies and investigating how users' interaction behaviors vary with the intent types, only a few of them have been made specifically for the image search scenario. Different from previous works which investigate image search user behavior and task characteristics based on either lab studies or large scale log analysis, we conducted a field study which lasts one month and involves 2,040 search queries from 555 search tasks. By this means, we collected relatively large amount of practical search behavior data with extensive first-tier annotation from users. With this data set, we investigate how various image search intents affect users' search behavior, and try to adopt different signals to predict search satisfaction under the certain intent. Meanwhile, external assessors were also employed to categorize each search task using four orthogonal intent taxonomies. Based on the hypothesis that behavior is dependent of task type, we analyze user search behavior on the field study data, examining characteristics of the session, click and mouse patterns. We also link the search satisfaction prediction to image search intent, which shows that different types of signals play different roles in satisfaction prediction as intent varies. Our findings indicate the importance of considering search intent in user behavior analysis and satisfaction prediction in image search.