arXiv subjects

Jiarong Jin

Publications and source records attributed to Jiarong Jin.

3 recordsLinked to original sources

arXiv2026

Limitations of Best-of-Both-Worlds Solutions in Approval-Based Multiwinner Elections

We study the best-of-both-worlds fairness in approval-based multi-winner elections, asking whether ex-ante guarantees for a fractional outcome can be implemented while every realized committee satisfies an ex-post representation axiom. Recent work has shown that several ex-ante proportionality guarantees can be achieved together with strong ex-post representation axioms. We first prove that ex-ante weak Pareto optimality (weak PO), which requires that no other fractional outcome makes every voter strictly better off, is incompatible with ex-post justified representation (JR). Since fractional core stability implies weak PO, this also rules out the possibility of combining ex-ante fractional core, a central fairness notion for fractional committees, with ex-post JR. We further show that ex-ante AJR is incompatible with ex-post JR, even though AJR is a much stronger average-representation analogue of JR. On the positive side, we show that the fractional ex-ante side itself remains highly compatible: several natural ex-ante representation guarantees can be satisfied simultaneously, including fractional core, group-resource proportionality, AJR (as well as its strict strengthening AJR+), and Pareto-optimality. Hence, highly fair fractional committees may exist even when they cannot be implemented by randomization over JR committees. Our results separate fractional representation from best-of-both-worlds implementability and identify fundamental limitations of fair randomized committee selection.

cs.GT↗

arXiv2026

Auctions with Contract Design

We consider a new auction model where the bidders' utilities and the auctioneer's revenue depend on a quality factor of the transaction determined by costly and strategic investments of the bidders. Applications of our model include ad auctions, government concessions and crowdsourcing contests. Crucially, these quality-enhancing efforts made by the bidders are often sunk costs incurred prior to the allocation, creating a fundamental moral hazard problem where the risk of losing the auction discourages investments. In this paper, we study the design of revenue-maximizing contracts integrated into auctions: the auctioneer commits to a transfer rule that rewards the winner for the ex-post realized quality of the transaction to incentivize higher effort. Our new framework is a natural generalization of both the auction theory and the principal-agent model. We consider both the second-price and the first-price auctions. We show that natural symmetric Bayes Nash equilibria exist in both auctions. Assuming these natural equilibria are played by the bidders and the number of bidders is large, we study linear contracts and derive the optimal reward factor of the transfer rule that maximizes the auctioneer's revenue. As the main result, we show that the optimal reward factor converges to the auctioneer's marginal benefit from the quality, as the number of bidders grows. That is, it is optimal for the auctioneer to fully pass through the quality value to the winner. This observation is largely independent of the auction rule used: we derive a revenue equivalence theorem showing that the revenue remains the same as long as symmetric Bayes Nash equilibria exist. Lastly, by quantitatively comparing with the standard auctions where no quality reward is used, we show that the use of contracts effectively improves the revenue by incentivizing high investments from the bidders.

cs.GT↗

arXiv2025

On Pareto-Optimal and Fair Allocations with Personalized Bi-Valued Utilities

We study the fair division problem of allocating $m$ indivisible goods to $n$ agents with additive personalized bi-valued utilities. Specifically, each agent $i$ assigns one of two positive values $a_i > b_i > 0$ to each good, indicating that agent $i$'s valuation of any good is either $a_i$ or $b_i$. For convenience, we denote the value ratio of agent $i$ as $r_i = a_i / b_i$. We give a characterization to all the Pareto-optimal allocations. Our characterization implies a polynomial-time algorithm to decide if a given allocation is Pareto-optimal in the case each $r_i$ is an integer. For the general case (where $r_i$ may be fractional), we show that this decision problem is coNP-complete. Our result complements the existing results: this decision problem is coNP-complete for tri-valued utilities (where each agent's value for each good belongs to $\{a,b,c\}$ for some prescribed $a>b>c\geq0$), and this decision problem belongs to P for bi-valued utilities (where $r_i$ in our model is the same for each agent). We further show that an EFX allocation always exists and can be computed in polynomial time under the personalized bi-valued utilities setting, which extends the previous result on bi-valued utilities. We propose the open problem of whether an EFX and Pareto-optimal allocation always exists (and can be computed in polynomial time).

cs.GT↗