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Tomer Ezra

Publications and source records attributed to Tomer Ezra.

At least 19 recordsLinked to original sources

Improved Impossibility Bounds for Maximin Share Allocations

The maximin share (MMS) is a central fairness benchmark for allocating indivisible items, but it need not be simultaneously attainable even under additive preferences. While extensive work has developed approximation guarantees, quantitative impossibility bounds have received comparatively little attention. We establish improved asymptotic and constant impossibility bounds for both goods and chores. For every sufficiently large number $n$ of agents, we construct additive goods instances in which every allocation gives some agent at most a $1-Ω((\log n)^{-2})$ fraction of her MMS. This strengthens the $1/n^4$ shortfall of Feige, Sapir, and Tauber (2021) to an inverse-polylogarithmic shortfall, an exponential improvement on the logarithmic scale of $n$. For chores, we construct instances in which every allocation gives some agent cost at least a $1+Ω((\log n)^{-2})$ factor of her MMS. Consequently, for every fixed $\varepsilon>0$, guarantees of $1-O(n^{-\varepsilon})$ for goods and $1+O(n^{-\varepsilon})$ for chores are impossible. We also give four-agent, eleven-item instances that improve the universal impossibility bounds from $39/40$ to $20/21$ for goods and from $44/43$ to $31/30$ for chores.

cs.GT

Welfare Approximation in Multilateral Trade

We introduce the study of \emph{multilateral trade}: a mechanism-design problem in which a single potential trade involves $k$ agents and can be executed only if all $k$ agents agree to participate. The classical case $k=2$ is the well-studied bilateral trade problem, where a seller and a buyer with private values must decide whether to trade an item initially held by the seller. Existing extensions of bilateral trade have largely focused on markets with many buyers and many sellers, but where each realized transaction is still bilateral, requiring agreement only between the matched buyer and seller. Our formulation captures settings in which the trade itself requires joint participation, coupling the agents' incentives and creating new challenges. We study welfare approximation in this setting under incentive compatibility, individual rationality, and budget balance. We give a DSIC mechanism with approximation ratio $O(k^2)$, and a BIC mechanism with approximation ratio $\widetilde O(k^{3/2})$. We prove matching lower bounds up to polylogarithmic factors. Finally, we extend the model to an $\ell$-out-of-$k$ partial-agreement setting, where the trade may occur once at least $\ell$ agents participate. In this relaxed model, the welfare guarantees improve smoothly as $k-\ell$, the number of agents whose participation is not required, grows, and we obtain matching upper and lower bounds up to polylogarithmic factors.

cs.GT

Optimal Prior-Free Mechanisms for Consumer Surplus

We settle the worst-case approximability of residual-surplus maximization in general multidimensional mechanism-design environments. For $n$ agents with arbitrary nonnegative valuations over a finite outcome space, we give a universally truthful and ex-post individually rational mechanism whose expected residual surplus is at least $W(N)/H_n$, where $W(N)$ is the optimal social welfare and $H_n$ is the $n$-th harmonic number. This guarantee is worst-case optimal, including its constant, even for a single-item auction with a known i.i.d. prior and under the weaker requirement of Bayesian incentive compatibility. Our result resolves the welfare-approximation aspect of the open question of [Hartline and Roughgarden 2008] on the power of money burning beyond $k$-unit auctions, as well as an open question of [Ezra et al. 2025] concerning optimal guarantees for broader valuation classes. It also replaces the outcome-dependent $O(\log|\mathcal{O}|)$ guarantee of [Fotakis et al. 2015] by the tight agent-dependent factor $H_n$, while strengthening truthfulness in expectation to universal truthfulness. The mechanism is polynomial-time whenever welfare-maximizing VCG is polynomial-time, yielding efficient mechanisms for gross-substitutes and multi-unit valuations and for several natural single-parameter feasibility constraints.

cs.GT

Identity-Truthful Online Decision-Making

In Bayesian online selection, a decision-maker observes a sequence of stochastic rewards and must immediately and irrevocably accept or reject each realized value. The rewards come from known distributions, referred to as their identities. Whereas classical prophet inequalities compare online algorithms to the offline optimum, recent work studies information gaps relative to the optimal online algorithm, which knows the identities' arrival order. We introduce a new restriction motivated by identity-independent decision-making: the decision-maker observes the current value but learns its identity only after deciding whether to accept it. We call such algorithms identity-truthful. We define the identity-truthfulness gap as the optimal worst-case approximation achievable by identity-truthful algorithms relative to the optimal online benchmark. This gap lies between the order-competitive ratio [Ezra et al. 2023] and the identity-blindness gap [Ezra et al. 2024]. We design an algorithm establishing that the identity-truthfulness gap is strictly greater than $0.5$, thereby separating it from the identity-blindness gap, which is exactly $0.5$. We complement this result with an upper bound of $0.81$ on the identity-truthfulness gap, which is strictly lower than the known upper bound of $0.829$ on the order-competitive ratio [Chen et al. 2024]. Finally, we study the relationship between identity-truthful algorithms and pricing mechanisms. Motivated by questions on the power of posted prices in prophet inequalities [Duetting et al. 2020], we show that identity-truthfulness breaks the usual connection between online algorithms and pricing: there exists an instance for which the optimal identity-truthful algorithm cannot be implemented by any pricing mechanism.

cs.GT

Multi-Agent Contracts

We study a natural combinatorial single-principal multi-agent contract design problem, in which a principal motivates a team of agents to exert effort toward a given task. At the heart of our model is a reward function, which maps the agent efforts to an expected reward of the principal. We seek to design computationally efficient algorithms for finding optimal (or near-optimal) linear contracts for reward functions that belong to the complement-free hierarchy. Our first main result gives constant-factor approximation algorithms for submodular and XOS reward functions, with value oracles for submodular reward functions and value and demand oracles for XOS reward functions. It relies on an unconventional use of ``prices'' and (approximate) demand queries for selecting the set of agents that the principal should contract with, and exploits a novel scaling property of XOS functions and their marginals, which may be of independent interest. As our second main result, we show that constant approximation is the best we can get for submodular reward functions, even with both value and demand oracles. For the larger class of subadditive reward functions, we establish an $Ω(\sqrt{n})$ impossibility for settings with $n$ agents. A striking feature of this impossibility is that it applies to subadditive functions that are constant-factor close to submodular. This rapid degradation presents a surprising departure from previous literature, e.g., on combinatorial auctions, where approximation guarantees tend to deteriorate more

cs.GT

Contract Design Beyond Hidden-Actions

In the classical principal-agent hidden-action contract model, a principal delegates the execution of a costly task to an agent. In order to complete the task, the agent chooses an action from a set of actions, where each potential action is associated with a cost and a success probability to accomplish the task. To incentivize the agent to exert effort, the principal can commit to a contract, which is the amount of payment based on the task's success but not on the hidden-action chosen by the agent. In this work, we study the contract design framework under binary outcomes where we relax the hidden-action assumption. We introduce new models where the principal is allowed to inspect subsets of actions at some cost that depends on the inspected subset. If the principal discovers that the agent did not select the agreed-upon action through the inspection, the principal can withhold payment. This relaxation of the model introduces a broader strategy space for the principal, who now faces a tradeoff between positive incentives (increasing payment) and negative incentives (increasing inspection). We devise algorithms for finding the best deterministic and randomized incentive-compatible inspection schemes for various assumptions on the inspection cost function. In particular, we show the tractability of the case of submodular inspection cost functions. We complement our results by showing that it is impossible to efficiently find the optimal randomized inspection scheme for the more general case of XOS inspection cost functions, and that there is no PTAS for the case of subadditive inspection cost functions.

cs.GT

Black-Box Lifting and Robustness Theorems for Multi-Agent Contracts

Multi-agent contract design has largely evaluated contracts through the lens of pure Nash equilibria (PNE). This focus, however, is not without loss: In general, the principal can strictly gain by recommending a complex, possibly correlated, distribution over actions, while preserving incentive compatibility. In this work, we extend the analysis of multi-agent contracts beyond pure Nash equilibria to encompass more general equilibrium notions, including mixed Nash equilibria as well as (coarse-)correlated equilibria (CCE). The latter, in particular, captures the limiting outcome of agents engaged in learning dynamics. Our main result shows that for submodular and, more generally, XOS rewards, such complex recommendations yield at most a constant-factor gain: there exists a contract and a PNE whose utility is within a constant factor of the best CCE achievable by any contract. This provides a black-box lifting: results established against the best PNE automatically apply with respect to the best CCE, with only a constant factor loss. For submodular rewards, we further show how to transform a contract and a PNE of that contract into a new contract such that any of its CCEs gives a constant approximation to the PNE. This yields black-box robustness: up to constant factors, guarantees established for a specific contract and PNE automatically extend to the modified contract and any of its CCEs. We thus expand prior guarantees for multi-agent contracts and lower the barrier to new ones. As an important corollary, we obtain poly-time algorithms for submodular rewards that achieve constant approximations in any CCE, against the best CCE under the best contract. Such worst-case guarantees are provably unattainable for XOS rewards. Finally, we bound the gap between different equilibrium notions for subadditive, supermodular, and general rewards.

cs.GT

Pandora's Box Problem With Time Constraints

The Pandora's Box problem models the search for the best alternative when evaluation is costly. In the simplest variant, a decision maker is presented with $n$ boxes, each associated with a cost of inspection and a hidden random reward. The decision maker inspects a subset of these boxes one after the other, in a possibly adaptive order, and gains the difference between the largest revealed reward and the sum of the inspection costs. Although this classic version is well understood (Weitzman 1979), there is a flourishing recent literature on variants of the problem. Here we introduce a general framework -- the Pandora's Box Over Time problem -- that captures a wide range of variants where time plays a role, e.g., by constraining the schedules of exploration and influencing costs and rewards. In our framework, boxes have time-dependent rewards and costs, whereas inspection may require a box-specific processing time. Moreover, once a box is inspected, its reward may deteriorate over time. Our main result is an efficient constant-factor approximation to the optimal strategy for the Pandora's Box Over Time problem, which is generally NP-hard to compute. We further obtain improved results for the natural special cases where boxes have no processing time, boxes are available only in specific time slots, or when costs and reward distributions are time-independent (but rewards may still deteriorate after inspection).

cs.GT

Combinatorial Contracts

We introduce a new model of combinatorial contracts in which a principal delegates the execution of a costly task to an agent. To complete the task, the agent can take any subset of a given set of unobservable actions, each of which has an associated cost. The cost of a set of actions is the sum of the costs of the individual actions, and the principal's reward as a function of the chosen actions satisfies some form of diminishing returns. The principal incentivizes the agents through a contract, based on the observed outcome. Our main results are for the case where the task delegated to the agent is a project, which can be successful or not. We show that if the success probability as a function of the set of actions is gross substitutes, then an optimal contract can be computed with polynomially many value queries, whereas if it is submodular, the optimal contract is NP-hard. All our results extend to linear contracts for higher-dimensional outcome spaces, which we show to be robustly optimal given first moment constraints. Our analysis uncovers a new property of gross substitutes functions, and reveals many interesting connections between combinatorial contracts and combinatorial auctions, where gross substitutes is known to be the frontier for efficient computation.

cs.GT

Multi-Project Contracts

We study a new class of contract design problems where a principal delegates the execution of multiple projects to a set of agents. The principal's expected reward from each project is a combinatorial function of the agents working on it. Each agent has limited capacity and can work on at most one project, and the agents are heterogeneous, with different costs and contributions for participating in different projects. The main challenge of the principal is to decide how to allocate the agents to projects when the number of projects grows in scale. We analyze this problem under different assumptions on the structure of the expected reward functions. As our main result, for XOS functions we show how to derive a constant approximation to the optimal multi-project contract in polynomial time, given access to value and demand oracles. Along the way (and of possible independent interest), we develop approximate demand queries for \emph{capped} subadditive functions, by reducing to demand queries for the original functions. Our work paves the way to combinatorial contract design in richer settings.

cs.GT

Contract Design for Sequential Actions

We introduce a novel model of contracts with combinatorial actions that accounts for sequential and adaptive agent behavior. As in the standard model, a principal delegates the execution of a costly project to an agent. There are $n$ actions, each one incurring a cost to the agent and inducing a probability distribution over $m$ outcomes; each outcome generates some reward for the principal. The principal incentivizes the agent through a contract that specifies a payment for each potential outcome. Unlike the standard model, the agent chooses actions sequentially. Following each action, the agent observes the realized outcome, and decides whether to stop or continue with another action. Upon halting, the agent chooses one of the realized outcomes, which determines both his payment and the principal's reward. This model captures common scenarios where the agent can make multiple attempts in the course of executing a project. We study the optimal contract problem in this new setting, namely the contract that maximizes the principal's utility. We first observe that the agent's problem - (adaptively) finding the sequence of actions that maximizes his utility for a given contract - is equivalent to the well-known Pandora's Box problem. Using this insight, we provide algorithms and hardness results for the optimal contract problem, under both independent and correlated actions, and for both linear and general contracts. For independent actions, we provide a poly-time algorithm for the optimal linear contract, and establish that finding the optimal general contract is NP-hard. In cases where the number of outcomes is constant, we devise a poly-time algorithm even for the optimal general contract. For correlated actions, we find that, for both linear and general contracts, approximating the optimal contract within any constant ratio is NP-hard.

cs.GT

Multi-Parameter Mechanisms for Consumer Surplus Maximization

We consider the problem of designing auctions which maximize consumer surplus (i.e., the social welfare minus the payments charged to the buyers). In the consumer surplus maximization problem, a seller with a set of goods faces a set of strategic buyers with private values, each of whom aims to maximize their own individual utility. The seller, in contrast, aims to allocate the goods in a way which maximizes the total buyer utility. The seller must then elicit the values of the buyers in order to decide what goods to award each buyer. The canonical approach in mechanism design to ensure truthful reporting of the private information is to find appropriate prices to charge each buyer in order to align their objective with the objective of the seller. Indeed, there are many celebrated results to this end when the seller's objective is welfare maximization [Clarke, 1971, Groves, 1973, Vickrey, 1961] or revenue maximization [Myerson, 1981]. However, in the case of consumer surplus maximization the picture is less clear -- using high payments to ensure the highest value bidders are served necessarily decreases their surplus utility, but using low payments may lead the seller into serving lower value bidders. Our main result in this paper is a framework for designing mechanisms which maximize consumer surplus. We instantiate our framework in a variety of canonical multi-parameter auction settings (i.e., unit-demand bidders with heterogeneous items, multi-unit auctions, and auctions with divisible goods) and use it to design auctions achieving consumer surplus with optimal approximation guarantees against the total social welfare. Along the way, we answer an open question posed by Hartline and Roughgarden [2008] for the two bidders single item setting.

cs.GT

The Competition Complexity of Prophet Secretary

We study the classic single-choice prophet secretary problem through a resource augmentation lens. Our goal is to bound the $(1-ε)$-competition complexity for different classes of online algorithms. This metric asks for the smallest $k$ such that the expected value of the online algorithm on $k$ copies of the original instance, is at least a $(1 - ε)$-approximation to the expected offline optimum on the original instance (without added copies). We consider four natural classes of online algorithms: single-threshold, time-based threshold, activation-based, and general algorithms. We show that for single-threshold algorithms the $(1-ε)$-competition complexity is $Θ(\ln(\frac{1}ε))$ (as in the i.i.d. case). Additionally, we demonstrate that time-based threshold and activation-based algorithms (which cover all previous approaches for obtaining competitive-ratios for the classic prophet secretary problem) yield a sub-optimal $(1-ε)$-competition complexity of $Θ\left(\frac{\ln(\frac{1}ε)}{\ln\ln(\frac{1}ε)}\right)$, which is strictly better than the class of single-threshold algorithms. Finally, we find that the $(1-ε)$-competition complexity of general adaptive algorithms is $Θ(\sqrt{\ln(\frac{1}ε)})$, which is in sharp contrast to $Θ(\ln\ln(\frac{1}ε))$ in the i.i.d. case.

cs.GT

The Competition Complexity of Prophet Inequalities with Correlations

We initiate the study of the prophet inequality problem through the resource augmentation framework in scenarios when the values of the rewards are correlated. Our goal is to determine the number of additional rewards an online algorithm requires to approximate the maximum value of the original instance. While the independent reward case is well understood, we extend this research to account for correlations among rewards. Our results demonstrate that, unlike in the independent case, the required number of additional rewards for approximation depends on the number of original rewards, and that block-threshold algorithms, which are optimal in the independent case, may require an infinite number of additional rewards when correlations are present. We develop asymptotically optimal algorithms for the following three scenarios: (1) where rewards arrive in blocks corresponding to the different copies of the original instance; (2) where rewards across all copies are arbitrarily shuffled; and (3) where rewards arrive in blocks corresponding to the different copies of the original instance, and values within each block are pairwise independent rather than fully correlated.

cs.LG

Prophet Inequality from Samples: Is the More the Merrier?

We study a variant of the single-choice prophet inequality problem where the decision-maker does not know the underlying distribution and has only access to a set of samples from the distributions. Rubinstein et al. [2020] showed that the optimal competitive-ratio of $\frac{1}{2}$ can surprisingly be obtained by observing a set of $n$ samples, one from each of the distributions. In this paper, we prove that this competitive-ratio of $\frac{1}{2}$ becomes unattainable when the decision-maker is provided with a set of more samples. We then examine the natural class of ordinal static threshold algorithms, where the algorithm selects the $i$-th highest ranked sample, sets this sample as a static threshold, and then chooses the first value that exceeds this threshold. We show that the best possible algorithm within this class achieves a competitive-ratio of $0.433$. Along the way, we utilize the tools developed in the paper and provide an alternative proof of the main result of Rubinstein et al. [2020].

cs.GT

Universal Optimization for Non-Clairvoyant Subadditive Joint Replenishment

The online joint replenishment problem (JRP) is a fundamental problem in the area of online problems with delay. Over the last decade, several works have studied generalizations of JRP with different cost functions for servicing requests. Most prior works on JRP and its generalizations have focused on the clairvoyant setting. Recently, Touitou [Tou23a] developed a non-clairvoyant framework that provided an $O(\sqrt{n \log n})$ upper bound for a wide class of generalized JRP, where $n$ is the number of request types. We advance the study of non-clairvoyant algorithms by providing a simpler, modular framework that matches the competitive ratio established by Touitou for the same class of generalized JRP. Our key insight is to leverage universal algorithms for Set Cover to approximate arbitrary monotone subadditive functions using a simple class of functions termed \textit{disjoint}. This allows us to reduce the problem to several independent instances of the TCP Acknowledgement problem, for which a simple 2-competitive non-clairvoyant algorithm is known. The modularity of our framework is a major advantage as it allows us to tailor the reduction to specific problems and obtain better competitive ratios. In particular, we obtain tight $O(\sqrt{n})$-competitive algorithms for two significant problems: Multi-Level Aggregation and Weighted Symmetric Subadditive Joint Replenishment. We also show that, in contrast, Touitou's algorithm is $Ω(\sqrt{n \log n})$-competitive for both of these problems.

cs.DS

Multi-Agent Combinatorial Contracts

Combinatorial contracts are emerging as a key paradigm in algorithmic contract design, paralleling the role of combinatorial auctions in algorithmic mechanism design. In this paper we study natural combinatorial contract settings involving teams of agents, each capable of performing multiple actions. This scenario extends two fundamental special cases previously examined in the literature, namely the single-agent combinatorial action model of [Duetting et al., 2021] and the multi-agent binary-action model of [Babaioff et al., 2012, Duetting et al., 2023]. We study the algorithmic and computational aspects of these settings, highlighting the unique challenges posed by the absence of certain monotonicity properties essential for analyzing the previous special cases. To navigate these complexities, we introduce a broad set of novel tools that deepen our understanding of combinatorial contracts environments and yield good approximation guarantees. Our main result is a constant-factor approximation for submodular multi-agent multi-action problems with value and demand oracles access. This result is tight: we show that this problem admits no PTAS (even under binary actions). As a side product of our main result, we devise an FPTAS, with value and demand oracles, for single-agent combinatorial action scenarios with general reward functions, which is of independent interest. We also provide bounds on the gap between the optimal welfare and the principal's utility. We show that, for subadditive rewards, perhaps surprisingly, this gap scales only logarithmically (rather than linearly) in the size of the action space.

cs.GT

Choosing Behind the Veil: Tight Bounds for Identity-Blind Online Algorithms

In Bayesian online settings, every element has a value that is drawn from a known underlying distribution, which we refer to as the element's identity. The elements arrive sequentially. Upon the arrival of an element, its value is revealed, and the decision maker needs to decide, immediately and irrevocably, whether to accept it or not. While most previous work has assumed that the decision maker, upon observing the element's value, also becomes aware of its identity -- namely, its distribution -- practical scenarios frequently demand that decisions be made based solely on the element's value, without considering its identity. This necessity arises either from the algorithm's ignorance of the element's identity or due to the pursuit of fairness. We call such algorithms identity-blind algorithms, and propose the identity-blindness gap as a metric to evaluate the performance loss caused by identity-blindness. This gap is defined as the maximum ratio between the expected performance of an identity-blind online algorithm and an optimal online algorithm that knows the arrival order, thus also the identities. We study the identity-blindness gap in the paradigmatic prophet inequality problem, under the two objectives of maximizing the expected value, and maximizing the probability to obtain the highest value. For the max-expectation objective, the celebrated prophet inequality establishes a single-threshold algorithm that gives at least 1/2 of the offline optimum, thus also an identity-blindness gap of at least 1/2. We show that this bound is tight. For the max-probability objective, while the competitive ratio is tightly 1/e, we provide a deterministic single-threshold algorithm that gives an identity-blindness gap of $\sim 0.562$ under the assumption that there are no large point masses. Moreover, we show that this bound is tight with respect to deterministic algorithms.

cs.GT