Ranking Experiments under Sequential Sampling

[2608.19897] Ranking Experiments under Sequential Sampling Skip to main content Search Submit Donate Log in Economics > Theoretical Economics arXiv:2608.19897 (econ) [Submitted on 20 Aug 2026 ( v1 ), last revised 3 Nov 2026 (this version, v3)] Title: Ranking Experiments under Sequential Sampling Authors: Zihao Li , Tianhao Liu View PDF Abstract: We rank experiments when a decision maker samples sequentially and chooses when to stop. We define two orders: large-budget dominance compares values in every decision problem once the expected-sample budget exceeds a common threshold; small-cost dominance compares values net of sampling costs when the cost per observation is sufficiently small in each problem. Generically, the orders coincide and are characterized by directed Kullback--Leibler (KL) dominance: larger KL divergence for every ordered pair of states. We also characterize the cost per copy of producing a specified experiment at scale by sampling a fixed source experiment. With a full-support prior, these cost functions are precisely sums of statewise maxima of positively weighted directed KL divergences. These costs are additive with two states but exhibit economies of scope with three or more states. Both results rest on an exact reproduction theorem: any experiment generated by sequentially sampling one source can be reproduced exactly by sampling another, with sharp first-order KL conversion rates and a square-root remainder whose coefficient depends only on the two sources. Subjects: Theoretical Economics (econ.TH) Cite as: arXiv:2608.19897 [econ.TH] (or arXiv:2608.19897v3 [econ.TH] for this version) https://doi.org/10.48550/arXiv.2608.19897 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Zihao Li [ view email ] [v1] Thu, 20 Aug 2026 11:05:43 UTC (30 KB) [v2] Fri, 28 Aug 2026 11:49:12 UTC (45 KB) [v3] Tue, 3 Nov 2026 05:45:37 UTC (211 KB) Full-text links: Access Paper: View PDF TeX Source view license Current browse context: econ.TH < prev | next > new | recent | 2026-08 Change to browse by: econ References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

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