ALT We study an intermediary that buys nonperishing items from arriving sellers and sells them to arriving buyers, subject to limited storage. Sellers and buyers arrive according to independent Poisson processes. The intermediary pays and charges the same fixed item prices, aiming to maximize long-run gains from trade: buyer value minus seller cost. Our benchmark may choose allocations directly, has unlimited storage, and must purchase each unit before allocating it. Its optimal long-run gain rate equals the value of a linear program of average purchase and allocation rates, with or without knowledge of future arrivals. For buyers whose bundle values are maxima of nonnegative additive valuations (XOS), we compute static common prices that guarantee half of this benchmark for arbitrary finite item capacities, even one storage slot per item, regardless of how ties are resolved without using future arrivals. The prices are computable in polynomial time using demand and supporting-value querie

ALT We study task assignment under adverse selection and moral hazard. In the single-task setting, agents privately know their types, which determine their success probabilities and effort costs. Based on the agents' reported types, the principal selects an agent to perform the task and offers a success-contingent payment. Although optimal Bayesian contract design is generally computationally intractable and Myerson's characterization does not directly extend to settings with hidden actions and multidimensional private information, we show that the single-task problem admits a simple Myerson-style characterization. Our key insight is that incentive constraints depend only on the ratio between effort cost and success probability. We characterize an optimal mechanism and show that it can be computed in polynomial time. We also establish a tight Θ(1/(NK)) worst-case bound on the ratio between the optimal profit and the full-information benchmark, where N is the number of agents and K is the n

ALT Generative AI assistants are becoming an advertising medium, embedding sponsored content in their answers. This setting departs from classical position auctions in two ways: an ad's click-through rate depends on the passage that hosts it, and because users stop at their first click, earlier ads take clicks from later ones. This competition for clicks makes truthfulness delicate: a higher bid can reshape the allocation and put a competitor ahead of the bidder, reducing the bidder's clicks. Existing mechanisms either attain a constant fraction of the optimal welfare (advertisers' total click value) but are not known to be truthful, or are truthful but their welfare guarantee degrades as the number of passages grows. We achieve both even when the answer and its editorial order are fixed before bidding. Assuming each ad's click-through rate when reached is known and unaffected by omitting other ads, we give a computationally efficient mechanism that is truthful in expectation and attains a

ALT We propose a market-making game played among N ≥ 2 strategic market makers and a single non-strategic representative trader. In a continuous quote space, standard finite-game arguments do not apply, while non-convex and discontinuous payoffs rule out classical continuous existence results and complicate learning. Nonetheless, we characterize pure-strategy Nash equilibria (PSNEs) and show that at least one PSNE always exists under minimal assumptions on the underlying asset value distribution, and that the equilibrium bid-ask spread widens strictly with asset value volatility. To obtain a tractable learning framework, we discretize prices and, under an additional regularity condition, show that the time-averaged play of no-internal-regret learners approaches the continuous Nash price envelope as the tick size vanishes. Finally, we empirically validate our theoretical results through numerical simulations.

ALT We study finite many-to-one matching markets with indifferences on both sides and publicly known firm preferences over sets of workers. A standard approach breaks ties uniformly at random and runs deferred acceptance, which is strategy-proof for workers but may select a weakly stable matching that is Pareto dominated under the agents' original preferences. For the broad class of markets in which each firm's preferences admit a gross substitutes (GS) representation, we construct a deterministic generalized deferred acceptance mechanism that is weakly stable, Pareto efficient with respect to both sides, worker weakly group strategy-proof, and implementable in strongly polynomial time given value oracles for the GS representations. This domain includes nonresponsive firm preferences, such as additive scores under matroid constraints and assignment values. Our analysis bridges ordinal matching with indifferences and competitive equilibria under imperfectly transferable utility. Although th

ALT Iterative combinatorial auctions are used in spectrum allocation, transportation, and industrial procurement, but rounds are costly. We introduce FastCombo, a deep reinforcement learning approach to sequential price discovery. Bayesian pricing has sharply reduced auction rounds even against subgradient benchmarks tuned with hindsight for each instance, but relies on a one-step objective and repeated online inference and optimization. We prove that greedily maximizing immediate clearing probability can require arbitrarily more rounds in expectation than an informative policy, even with the true valuation distribution known. FastCombo learns price policies offline, assigning credit to queries for the future decisions they enable and producing each online price update with a single neural-network forward pass. We formulate the objective of minimizing expected rounds to clearing in single-minded auctions as a partially observable Markov decision process and train a recurrent bidder-item gr

ALT Transactions differ in how their value changes with execution time and relative position. Ethereum's EIP-1559 lets them compete through fee fields, but a fee bid gives no enforceable claim to an earlier position in the block, and no way to express willingness to accept a later one. Its base fee is a hard admission floor. We introduce temporal liquidity markets (TLM), a framework for crossing complementary temporal requirements: transactions seeking early-region execution (consumers) can fund transactions that opt into exclusion from that region (providers). Selected consumers complete within the early region and are ordered among themselves by authorization density, both validator-checked. In our block-local EIP-1559 extension, providers may be admitted below the base-fee floor. Under TLM regulation, funding-conditioned and capacity-capped provider gas is excluded from the ordinary EIP-1559 controller input. Counting it there instead raises the base fee and displaces ordinary demand at

ALT We give a bounded envy-free cake-cutting protocol for any number n of agents in the Robertson–Webb model, with a short, self-contained, and elementary correctness proof. The protocol uses 2ᴼ⁽ⁿˡᵒᵍⁿ) queries. Most ingredients are already known and well understood in the fair division literature; our main contribution is a simpler organization of these ideas into a protocol whose complete description and proof fit in a few pages.

ALT We give an algorithm for computing an \eps-approximate fixed point of any d-dimensional nonexpansion with respect to the ℓ∞ norm in time log(1/\eps){ᴼ⁽ˡᵒᵍ²ᵈ)}. At its core, our method uses a boosting argument to geometrically shrink a search region. Among other consequences, our algorithm implies that simple stochastic games can be solved in quasipolynomial time.

ALT The nucleon is the multiplicative counterpart of the nucleolus: it lexicographically maximizes the fraction of its stand-alone value that each coalition receives. We study exact nucleon computation in matching games and in two extensions: b-matching games, in which players may have several partners, and partitioned matching games, in which one player may own several vertices. We resolve three open problems from a recent survey. For ordinary matching games with nonnegative rational weights, we give a polynomial-time combinatorial algorithm using maximum-weight matching, shortest paths, and minimum-cost flow,; it returns both a nucleon allocation and a compact description of the full nucleon. In contrast, computing any nucleon allocation is NP-hard for unit-weight b-matching games with capacities at most three and for unit-weight partitioned matching games of width at most three. Finally, for the unresolved capacity-two case, we show that exact nucleon computation is polynomial-time Turi

ALT We present an AI-assisted mathematical addendum to Strategic Trip Planning: Striking a Balance Between Competition and Cooperation (Amar, 2018). We reconstruct selected relationships between strategic choice, cooperative stability, participant approval and enforcement. The thesis's Nash-to-core proposition is under-specified until outsider opportunities are stated. We verify two-person implications under fixed outsiders and explicit efficiency conditions. Under isolation, the original example has an empty TU core; its funded counterpart uses an explicit external subsidy. For a monotone resource model we characterize the exact financing gap. A finite negotiation procedure implemented in Lean is proved to terminate with participant approval, joint-cost non-worsening and local optimality against permitted approved moves. Supporting formalizations address bargaining geometry and regret-based efficiency. A resource-game counterinstance shows why monotonicity alone provides no uniform price-

ALT In sponsored-shopping auctions, one advertiser may win several positions, and a product that is never displayed may determine the prices of several winners. Under generalized second-price (GSP) pricing without self-pricing, these interactions couple allocation and payment through a single global ranking. We prove that exact autobidding equilibria under budget and return-on-investment (ROI) constraints can be computed in polynomial time whenever either the number n of advertisers or the number K of display slots is fixed. Our algorithms rely on two structural reductions. With few advertisers, the payment polytopes of tied bid layers combine by a Minkowski sum, giving a fixed-dimensional representation of exponentially many rankings. With few slots, budget equilibria admit threshold recovery and an exact submodular description of attainable payments at the display boundary. For ROI, we prove that every group-consecutive equilibrium has a representative with at most K+2 nonunit multiplier

ALT Heuristic solvers for general-sum extensive-form games produce a stream of iterates, and practice reports the NashGap of the returned profile. We ask what a solver can know about its own iterates from the regret accumulators and the terminal-distribution accumulators its updates already maintain. In two-player games the NashGap of a weighted average strategy equals, at the maximizing player, the weighted external regret plus a signed value correction. Bounding the regret by the running counterfactual-regret accumulator gives an online certificate whose cost per checkpoint is linear in the number of terminal histories. The certificate stays sound under alternating updates and in games with three or more players. The mismatch between what alternation evaluates and what the averages record enters as one signed comparator term, maximized in a single backward pass per player whose comparator weight is nonzero. The bound is read off the regret state, with no best response against the average

ALT We consider the problem of allocating indivisible goods to agents with additive valuations, under two classic notions of fairness and efficiency: envy-freeness up to any good (EFX) and Pareto optimality. While some of the earliest work on EFX showed that Pareto optimal EFX allocations may not exist if agents can have zero value for some goods, it has remained open whether the same is true when all values are positive. We present a simple instance in which every good has positive value to every agent, but no Pareto optimal EFX allocation exists. We then generalize to a family of instances showing that Pareto optimal α-EFX allocations may not exist for any α> 2/3.

ALT We study the best-response dynamics in games with random payoffs. We consider two-player bimatrix games, with n strategies each. We assume that payoffs come from a continuous distribution, and study the best-response dynamics of these games, after imposing some structure on their payoff matrices. In particular, we focus on symmetric games, antisymmetric games, common-interest games, zero-sum games, and games with independent payoffs. When two players take turns in playing the best response to the opponent's action, they eventually end up cycling through the same sequence of actions. The main focus of the paper is the cycle structure of large random games. In particular, we study the number of cycles, the number of cycles of a certain length, the length of the longest cycle, and related quantities. We show that the structure of the game has an important impact on the typical cycle structure. Our arguments are based on a representation of the best-response dynamics of these games as dire

ALT The modeling of the behavior of agents in strategic environments and the prediction of the resulting outcomes has long been a challenging problem; this has been the focus of game theory. The introduction of AI agents as human-aids or as sole decision-makers expands the underlying question: Can we model behavior and predict outcomes of strategic games that involve a mix of AI agents and human players? As an initial answer, we propose Correlated Quantal Response Equilibrium (CQRE) that combines and generalizes classical solution concepts such as Quantal Response Equilibrium and Correlated Equilibrium. We answer questions of existence and propose a method for computing the CQRE that is a best fit to a given joint action distribution. We demonstrate the empirical effectiveness of CQRE predictions using a diverse collection of complex general-sum games, including high-rank and large-action two-player games, challenging multiplayer games, and a large-scale extension of the Selten–Chmura sett

ALT Envy-free cake cutting asks how to divide a resource so that no agent prefers another agent's share. Finding such a division with few queries about preferences has been a longstanding challenge. Aziz and Mackenzie gave the first query bound depending only on the number n of agents: a tower of six n's. Subsequent improvements culminated in Ye and Bai's single-exponential bound of nᴼ⁽¹⁾2ⁿ. A large gap remains between this deterministic upper bound and Procaccia's general lower bound of Ω(n²). Whether polynomially many queries suffice is a central open question in fair division. We show that a polynomial expected query bound is possible. Our randomised protocol uses only independent, uniformly sampled points from the cake as its source of randomness. For every profile of n agents with nonnegative, additive, nonatomic valuations, the protocol has the following guarantees: (1) It terminates with probability one. (2) Every allocation it returns is complete and exactly envy-free. (3) Its expe

ALT We study the parameterized complexity of finding an EF1 allocation of indivisible items in the group-sharing setting, which has received increasing attention in recent years. There, items are not allocated to individual agents but rather to one of two (or possibly more) groups, and agents within that group all share the assigned items. We consider both the "fixed" (EF1-Fix) and "variable" (EF1-Var) group cases, where in the former the assignments of agents to groups are prescribed. While both are NP-hard in the classical sense, we show that their algorithmic behavior differs significantly from the parameterized perspective. As our main results, we: (1) prove that both cases are NP-hard even when the number of agents is a small constant; (2) circumvent this hardness by establishing fixed-parameter tractability for EF1-Var w.r.t. the number of "subjective" items, that is, items which are viewed as chores or goods by different agents—but rule out an analogous result for EF1-Fix; and (3) e

ALT We study randomized committee elections with additive preferences, where we seek best-of-both-worlds proportionality. That is, we strive for strong guarantees for the selection probabilities ex ante and each realized committee ex post. We focus on structured preferences and introduce antichain single-peakedness, a restricted domain generalizing single-peaked additive preferences as well as several well-studied approval domains. Our approach first computes a fractional committee and then decomposes it through a rounding rule called systematic rounding. Among profile-oblivious rounding rules, we characterize systematic rounding as the unique rule satisfying natural quota guarantees, and thereby the only promising rule for our two-step approach. We show that ex-ante core stability is incompatible with ex-post justified representation JR, even under single-peaked preferences. However, applying systematic rounding to certain fractional allocations that form so-called Lindahl equilibria yiel

ALT We show that computing an approximate correlated equilibrium of a polytope game is PPAD-complete, and thus polynomial-time equivalent to finding a Nash equilibrium. This challenges the conventional view in game theory that correlated equilibria are more tractable objects than Nash equilibria. From a conceptual standpoint, we connect correlated equilibria to a natural generalization of fixed points, which we call fixed sets. A fixed set of a continuous function f is a nonempty finite set S such that S ⊆ conv f(S). Our main technical contribution is to prove that computing a fixed set is PPAD-hard even at inverse polynomial precision.