Spotlight Talks

Speaker: Chido Onyeze (Cornell University)

Title: Equilibria in Dynamic Allocation of Shareable Goods

Abstract: We study the problem of repeatedly allocating a shareable good among multiple agents. In each round, a principal decides whether to allocate the good and, if so, which agents receive access. Each agent has a private valuation for access in each round, drawn independently over time from a joint distribution that may be arbitrarily correlated across agents. The principal’s goal is to ensure that each agent derives high utility from the allocated rounds, subject to a packing constraint on the allocation. At the same time, agents are strategic and act to maximize their own utility, potentially at the expense of others.

We ask whether, in the presence of such selfish behavior, there exist mechanisms that achieve outcomes that are both efficient and fair. To address this, we introduce the notion of the core as a benchmark for efficiency and fairness in this setting. We then show that a simple artificial-money mechanism approximately implements the core at equilibrium. Our approach utilizes a monetary mechanism as a black box, revealing a surprising connection between classical notions of efficiency in monetary mechanisms and the equilibrium properties of the resulting artificial-money mechanism.

Speaker: Kumar Kshitij Patel (Yale University)

Title: When do Score-based Data Valuation Methods Work and Why?

Abstract: Score-based valuation methods, such as Shapley-style scores and leave-one-out (LOO), are widely used for credit assignment in data markets, yet theory offers limited guidance on when and why they succeed. In this talk, we discuss recent work that studies these methods through the lens of best data subset selection for learning tasks. We show that even for monotone submodular valuation functions, LOO and Shapley-style scores cannot achieve a constant-factor approximation due to duplicate archetypes and collapsed pointwise credit. More broadly, boundary effects in canonical learning problems can induce supermodular spikes, ruling out constant-factor guarantees for any valuation method, including adaptive methods such as greedy selection. We identify two conditions that avert these failures: bounded curvature, which controls redundancy and restores guarantees for score-based methods, and coverage, which yields approximate submodularity over a sufficiently rich core for uniformly stable learning algorithms. We also examine the role of monotonicity and show separations between adaptive and non-adaptive methods for non-monotone valuations. Our results justify common practices such as deduplication while highlighting the importance of ensuring coverage before applying score-based selection.