Papers for

online marketplace operators

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Regulators reduce high prices in AI pricing agent markets

Mitigating Emergent Collusion in LLM Pricing Agents

Abstract: Recent work shows that LLM-based pricing agents can produce supracompetitive outcomes in repeated oligopoly environments without being explicitly instructed to collude. We reproduce the qualitative prompt-sensitivity effect of Fish et al. using DeepSeek-V3.1: the P1 prompt produces significantly higher prices and profits than P2, although our outcomes are less monopoly-like than the original GPT-4 results. We then evaluate three regulatory interventions: a prompt-only warning, a Harrington-inspired expected-damages payoff regulator, and an active random entrant. The prompt-only regulator reduces but does not eliminate above-Nash pricing. The Harrington regulator brings P1 outcomes close to the duopoly Nash benchmark and removes the statistically significant P1--P2 gap. The active entrant produces the strongest effect, pushing both prompts below the appropriate random-entrant Nash benchmark. Overall, our experiments provide preliminary evidence that interventions that alter incentives or market participation can reduce supracompetitive pricing more effectively than prompt warnings alone.

Fri 11 SeptComputer Science and Game Theory
The gist
Sometimes AI agents that set prices in repeated market situations end up charging higher prices by working together without being told to do so. The authors repeated prior findings showing that small changes in how the AI agents are prompted can lead to big differences in their pricing behavior. They tested three ways to reduce these high prices: just a warning message in the prompt, a system that changes the agents' rewards based on penalties, and adding a random new competitor. The last two approaches worked better than just warnings, helping prices move closer to normal competitive levels.
Open → 2609.13037v1

Conditional transaction mechanisms optimize evaluation order for offline users

Opening the Strategic Pandora Box: Conditional Transaction Mechanisms

Abstract: Conditional transaction engines (CTEs) execute conditional instructions for offline users. This paper formalizes the mechanism-design problem within each engine invocation. A conditional transaction mechanism (CTM) decides which pending conditions to evaluate first because each evaluation delays the eventual write. We model this problem as Strategic Pandora, a discounted variant of the Pandora's box model with independent Bernoulli boxes. Agents report privately assessed success probabilities and, in the full model, values for the write action. We propose the reported-Weitzman mechanism (RW) and the reported-values second-price mechanism (RWSP). To compare revenue without a common prior, we introduce dynamic No-Betting Revenue. Under the stated competition and equilibrium conditions, every qualifying pure equilibrium of RW or RWSP earns a constant fraction of its corresponding dynamic NBR benchmark.

Thu 10 SeptComputer Science and Game Theory
The gist
Sometimes computer systems need to decide which tasks to handle first when not all users are online, and each decision affects how long final updates take. This paper studies how to design rules that choose the order to check conditions using each user's own estimate of success chances and value. The authors introduce new methods that aim to get payments or rewards close to a theoretical best, even without shared knowledge among users. Their work helps understand how to efficiently manage such conditional tasks when information is private and timing matters.
Open → 2609.12197v1