Searcharxiv⌕ Search

arXiv · 2610.33707

A Factor-6 Truthful-in-Expectation Mechanism for Cascade Position Auctions

Abstract

Position auctions require assigning advertisers to positions and determining their display order. Under cascade behavior, earlier clicks prevent users from reaching later placements, coupling the click probabilities of selected advertisements. Existing methods achieve constant-factor welfare approximation and monotone logarithmic-factor approximation separately, leaving open whether both guarantees can be attained simultaneously. The difficulty is that welfare-preserving rounding need not maintain the monotonicity of individual click-through rates required for truthful bidding. We resolve this problem by developing a polynomial-time randomized mechanism that achieves a factor-6 welfare approximation and truthfulness in expectation for arbitrary public advertiser-position click probabilities. The mechanism optimizes a reach-weighted linear relaxation and converts its solution into feasible ordered allocations through matching decomposition, probability-bounded grouping, and calibrated attenuation. This construction compensates for cascade dependence and ensures that each advertiser's expected click-through rate equals exactly one sixth of its fractional click coordinate. The resulting identity transfers the monotonicity of the fractional allocation to the implemented mechanism, enabling exact incentive-compatible payments while preserving the welfare guarantee. The allocation lottery and payments are computable in polynomial bit time, with exact sampling in expected polynomial time.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Qiuyu Chen, Baao Xie. 2026-10-24. A Factor-6 Truthful-in-Expectation Mechanism for Cascade Position Auctions. https://arxiv.org/abs/2610.33707

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Prophet Inequalities with Delayed and Uncertain Acceptance

We introduce the *prophet inequality with delayed and uncertain acceptance* model, a variant of the classical prophet inequality in which a decision-maker sequentially evaluates options whose acceptance is uncertain and revealed only after a random delay. At each time step, the decision-maker observes the realized value of the arriving option and must irrevocably decide whether to select it or continue searching. Once an option is selected, the process is suspended for a delay drawn from a known distribution with expectation $d$, during which no other option can be considered. When the delay expires, the selection succeeds with a known probability. If it succeeds, the decision-maker receives the realized value and the process terminates; otherwise, the search resumes. In addition to the online decision-maker, we consider two stronger benchmarks: the value-aware decision-maker, who knows all value realizations in advance but not the acceptance outcomes, and the prophet, who knows both the values and the acceptance outcomes. We show that, in the worst case, both decision-makers achieve a $1/(d + 2)$ fraction of the prophet's value, while the decision-maker achieves half of the value-aware decision-maker's value. We then turn to the no-delay case, where we establish an equivalence between our model and the classical prophet inequality, and show that the value-aware decision-maker beats the $1/2$ barrier against the prophet whenever all acceptance probabilities are bounded away from $0$.

cs.GT↗

Position Auctions with a Capacity Constraint

Sponsored search auctions are commonly modeled as an assignment of a fixed set of positions to a set of advertisers, with welfare maximization reducible to a standard matching problem. Motivated by modern advertising environments, we study a richer variant of the classical position auctions model, in which ads may have heterogeneous sizes and the platform must jointly select a subset of ads and assign them to positions subject to a global capacity constraint. The size of an ad can capture the physical space it occupies on a page or its consumption of a scarce platform resource, such as communication bandwidth or computational capacity. Thus, size measures the resource cost imposed by an ad on the platform, which need not be related to the value generated by displaying it. We begin by studying a general formulation of the matching with a capacity constraint problem and propose an algorithmic technique that goes beyond known greedy methods while achieving constant-factor approximation guarantees for social welfare. Our allocation rule augments density-based ordering with capacity-aware local improvements, which allow for re-allocations that improve welfare, while respecting the capacity constraint. Applied in the context of position auctions, we analyze this algorithm under the standard assumptions of single-parameter agents, publicly known ad sizes, and position-dependent click-through rates (CTRs). In this setting, the size of an ad determines the amount of capacity it consumes, while the CTR depends only on the position to which it is assigned. We show that a minor modification to our algorithmic approach yields a universally truthful mechanism that achieves a 4-approximation of the optimal social welfare, and we show that this guarantee is tight. To the best of our knowledge, this is the first such mechanism for this variant of the capacity-constrained matching.

cs.GT↗

Otter: A Provably MEV-Resilient Automated Market Maker via Surplus Redistribution

Miner extractable value (MEV) in automated market makers allows block builders to profit from transaction ordering and injected trades, imposing costs on users and contributing to builder centralization. We introduce Otter (Optimal Truthful Trading with Excess Redistribution), a two-asset batch AMM that achieves provable MEV resilience when the consensus layer provides censorship resilience and block space is uncongested. Otter makes truthful behavior a dominant strategy for both users and builders. Consequently, a builder cannot profit from strategic deviations, including reordering bids or injecting sybil bids, even when the builder is itself a trader with intrinsic value. Moreover, we show that Otter maximizes social welfare, in a strong sense, within a natural class of mechanisms satisfying the desired game-theoretic properties. To achieve these guarantees, we introduce a new paradigm called surplus redistribution, which provably prevents residual surplus from being captured as MEV by redirecting it to the broader community. Specifically, the pool's output tokens need not be distributed entirely among users in the current batch. Instead, residual surplus may be transferred to a smart contract governed by the decentralized community. The accumulated surplus can be used to benefit community members --- for example, by subsidizing transaction fees, rewarding liquidity providers, or returning assets to the pool. Our approach relies on the underlying consensus layer to provide censorship resilience. We motivate its necessity through an impossibility result showing that the desired game-theoretic guarantees become unattainable when the builder is additionally allowed to censor transactions. Thus, our results also provide a mathematically formal demonstration of how consensus-level security guarantees can fundamentally expand what is achievable at the application layer.

cs.GT↗