marketplace platform engineers

Abstract: Automated mechanism design has enabled the learning of complex auction mechanisms using neural networks. However, designing non-truthful auctions remains challenging because bidders' equilibrium strategies depend on the auction mechanism and thus evolve as the mechanism is optimized. In this work, we formulate auction design as a Stackelberg game with multiple strategic followers and develop a fully first-order algorithm for revenue optimization in non-truthful auctions. We establish non-asymptotic guarantees for hypergradient approximation and show that, under standard smoothness and strong-monotonicity assumptions, our method reaches an $O(ε)$-stationary point using $\tilde{\mathcal{O}}(ε^{-2})$ first-order oracle calls. Experiments on symmetric and asymmetric single-item auctions, as well as multi-item auctions, show that our method converges faster than second- and zeroth-order baselines and scales better as the number of bidders increases. Our method also discovers auctions that achieve revenue close to truthful baselines. These results provide a scalable framework for automated mechanism design beyond truthful auctions.

Abstract: Hospitality service in online marketplaces has become increasingly popular over the past few years, not only because it helps people easily rent short-term lodging but also because it empowers travelers to connect with locals and feel belonging. A typical pattern of these industries is a central platform mediating the economic interactions between guests and hosts, where a successful booking experience is the key to user satisfaction. However, one of the biggest challenges is the possible delay of response and chance of rejection when guests try to book a request-to-book listing. Previously, we enabled the instant book feature to allow hosts to instantly accept qualified guests and guests to confirm bookings immediately. Yet the symptom has not been alleviated given mixed request-to-book and instant book listing supply in search results. One possible solution is to promote all instant book listings in search results; however, booking rate will be damaged due to some improperly promoted low quality listings. In this paper, we present a novel smart instant book filter system that applies the instant book filter intelligently and shifts request-to-book to instant book without hurting booking rate. Compared with traditional two-sided markets research that targets finding strategies to maximize a single overall social welfare goal, our proposed system aims to maximize the instant book rate with the constraint that booking rate should not decrease, forming a dual-objective optimization problem. We show A/B testing results for our system exposed to global users, demonstrating that our model performs better than previous human-expert-tuned models in terms of both instant book rate and overall booking rate. We also achieved improved metrics such as the host instant book adoption rate. The proposed system has been deployed to production and is serving millions of users every day.

Abstract: In decentralized consumer-to-consumer (C2C) marketplaces, people list goods, negotiate with strangers, and rate one another, so trust rests on reputation. Large language model (LLM) agents now act for users, raising risks to their money, privacy, and reputation. We introduce BazaarBench, a simulated C2C marketplace and benchmark for evaluating the safety of these agents. It tracks ownership, item condition, and commitments across transactions, combining record checks with rubric-based LLM judgments to identify six failure types across five stages. We run three base markets for 30 simulated days, each with 100 agents using one model and inventories drawn from a public eBay sample. Across 45 continuations, we evaluate five models under ordinary instructions, deadline pressure, or adversarial instructions to exploit other traders. Each continuation runs for seven simulated days from a copy of a market's day-30 state. The tested model controls the same 20 selected agents, retaining their personas, inventories, and histories, while the other 80 keep the base model. All five models attempt to promise the same item to multiple buyers under ordinary instructions. Adding targets and deadlines increases these attempts for every model. Under adversarial instructions, the share of tested sellers' committed transactions completed despite unavailable items or overstated conditions rises from 15.4% to 33.4%, reaching 55.5% for GPT-5.4. Averaged across models and markets, simulated weekly earnings per tested agent rise from USD 20 under ordinary instructions to USD 33 under adversarial instructions. Most of the increase comes from items the sellers never held. We release the simulator, saved market states, evaluation code, and records covering 357,608 agent model calls for evaluating new models and developing safer marketplace agents.

Abstract: The $p$-mean welfare objective unifies several classical social welfare criteria for the allocation of indivisible goods. We study its maximization under additive nonnegative utilities when item and agent multiplicities are encoded in binary. For every fixed finite rational $p<1$, $p\neq0$, we show that the problem is $\mathsf{NP}$-hard with only two item types. Nash welfare ($p=0$) and egalitarian welfare ($p=-\infty$) are NP-hard with three item types. These results hold both for computing an optimal allocation and for rational-threshold decision, and the utilities and threshold can be required to be positive integers. We also show that maximizing $p$-mean social welfare is strongly NP-hard with one agent type and an unrestricted number of item types, for every fixed finite rational $p<1$ and for $p=-\infty$. A quantitative gap in this reduction rules out an FPTAS in the latter setting unless $\mathsf{P}=\mathsf{NP}$. On the positive side, for a fixed number of item types and an arbitrary number of agent types, we give an FPTAS for every fixed $p\in\mathbb Q\cup\{-\infty\}$. Its running time is polynomial in the compact input length and in $1/\varepsilon$, and it returns a compressed allocation. For a fixed number of agent types and an unrestricted number of item types, we give a PTAS for every fixed finite rational $p<1$, also in the fully compact model. We further give explicit compact-model proofs of the classical exact allocation algorithms for one item type, and for egalitarian welfare with two item types. These results essentially settle the complexity and approximability of $p$-mean welfare maximization with few item types and/or few agent types, leaving only the exact complexity of Nash welfare maximization with two item types unresolved in the small-item-type classification.

Abstract: We study the problem of fairly allocating $m$ indivisible goods among $n$ agents who arrive online, under the notion of maximin share (MMS) fairness. Fair allocation with online arrivals is notoriously challenging: prior work achieves constant-factor MMS guarantees only when agents' preferences belong to a set of valuation functions known in advance, while no guarantees were known without such prior information. We develop a new randomized online algorithm for additive and submodular valuations, that we call Single-or-Sample, and that achieves a constant-factor approximation to MMS simultaneously for all agents, with constant probability. The algorithm requires no prior knowledge about the agents' valuations, and works against adversarial (oblivious) inputs. We further establish a fundamental tradeoff between approximation and success probability. Specifically, for any $c \ge 1$, no online algorithm can guarantee a $1/c$-approximation to MMS with probability exceeding $1 - 1/c^2$, even for binary additive valuations. This rules out constant MMS with probability asymptotically closer to $1$ than a constant. For XOS, the lower bound is much stronger: no algorithm can achieve even $1/\log\log n$-MMS to all agents with a constant probability. We complement this lower bound with an algorithm for the regime $c\in Ω(\log n)$, namely $1/c$-MMS to all agents with probability $(1-O(1/c))$, and show that this tradeoff is tight for XOS. Our constant-factor algorithm introduces several new ideas, combining greedy submodular maximization with randomized allocation and single-item reduction. A key technical ingredient is a new approach for analyzing iterative sampling without replacement. We develop concentration bounds that apply to a broad class of adaptive processes with complex dependencies across elements and rounds, which may be of independent interest.

Abstract: We study a matching mechanism where agents and objects are described by features rather than complete rankings. A single spectral projection reduces the problem to a one-dimensional sort, computable in O(N log N) time. We prove that on descaled features and preferences, our algorithm obtains the exact Nash Social Welfare (NSW) optimum within the projected space, with an unconditional utilitarian-welfare guarantee and a conditional NSW guarantee. The proposed mechanism is stable against exogenous noise but not strategy-proof; we provide an explicit profitable misreport. On an agentic AI shopping application, the diagnostics correctly anticipate both a success and a failure case. A 100-instance robustness study confirms the findings.

Abstract: Digital platforms increasingly rely on automated budget-management systems to regulate participation across allocation opportunities. A common tool is multiplicative pacing, which scales buyers' bids so that campaign-level budgets are spent gradually. Although pacing is operationally simple, its aggregate behavior depends on the underlying mechanism. In first-price single-item auction markets, pacing enjoys strong structural and computational properties that often fail in second-price auctions. It is unclear whether these properties extend to richer platform mechanisms. We develop a unified theory of pacing equilibria across a hierarchy of mechanisms. We first show that, even in first-price position auctions, the market-equilibrium interpretation of the single-item benchmark can fail. We then identify bid-maximizing pay-your-bid mechanisms as a broad class in which pacing equilibria exist, are unique, and admit an Eisenberg-Gale-type convex-program characterization. This implies efficient computation, Pareto-efficiency, and liquid-welfare guarantees, enabling pacing integration into large-scale optimization-based allocation routines. Finally, for abstract mechanisms satisfying a payment monotonicity condition, we prove equilibrium existence by smoothing discontinuities. With additional conditions and appropriate tie-breaking, we obtain uniqueness, revenue maximality among budget-feasible pacing vectors, and shill-proof implementability, and provide convergent budget-adjustment dynamics for approximate equilibria. Overall, many favorable properties of first-price pacing extend beyond single-item auctions under appropriate mechanism-level conditions.