Pith Number

pith:YFBJFYUA

pith:2026:YFBJFYUASCDOOTFFLB7WQIDM42

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Designing Persuasive Experiments

Abhi Vemulapati, Karun Adusumilli

Regulators can align experiment incentives by setting a minimum expected social-welfare threshold that experimenters optimize subject to.

arxiv:2605.16703 v1 · 2026-05-15 · econ.EM

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4 Citations open

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Claims

C1strongest claim

Under normal priors, sampling according to the Neyman-allocation is always optimal, independent of the specific objectives. Furthermore, we characterize the optimal stopping-rule. In a numerical study calibrated to historical clinical-trial data, our framework reduces expected sample-sizes by over 48% relative to classical designs that attain the same social-welfare.

C2weakest assumption

The framework requires no knowledge of experimenters' private preferences or costs and mitigates strategic Bayesian persuasion, with the regulator only setting a minimum expected welfare threshold that experimenters optimize subject to.

C3one line summary

Regulators set welfare thresholds constraining experimenters' designs, making Neyman allocation optimal under normal priors and reducing sample sizes over 48% in calibrated simulations compared to classical approaches.

References

16 extracted · 16 resolved · 1 Pith anchors

[1] Open to Bayesian Statistics 2026

[2] Diffusion Approximations for Thompson Sampling in the Small Gap Regime 2021 · arXiv:2105.09232

[3] Kamenica, E. and M. Gentzkow (2011). Bayesian Persuasion.American Economic Re- view 101(6), 2590–2615. Kolotilin, A., R. Corrao, and A. Wolitzky (2025). Persuasion and Matching: Optimal Productive Tra 2011

[4] Liang, A., X. Mu, and V. Syrgkanis (2022, January). Dynamically Aggregating Diverse Information.Econometrica 90(1), 47–80. Liptser, R. S. and A. N. Shiryaev (2011).Statistics of Random Processes II: A 2022

[5] Reprint of the 2nd ed. Makary, M. (2026). FDA is Now Open to Bayesian Statistical Approaches. A Leap Forward! Post on X. Moore, T., H. Zhang, G. Anderson, and G. Alexander (2018). Estimated Costs of P 2026 · doi:10.2139/ssrn.2991567

Receipt and verification

First computed	2026-05-20T00:02:37.339950Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

c14292e2809086e74ca5587f68206ce693d2e8df4a456d9cd1ee854583fff8c5

Aliases

arxiv: 2605.16703 · arxiv_version: 2605.16703v1 · doi: 10.48550/arxiv.2605.16703 · pith_short_12: YFBJFYUASCDO · pith_short_16: YFBJFYUASCDOOTFF · pith_short_8: YFBJFYUA

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/YFBJFYUASCDOOTFFLB7WQIDM42 \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: c14292e2809086e74ca5587f68206ce693d2e8df4a456d9cd1ee854583fff8c5

Canonical record JSON

{
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    "abstract_canon_sha256": "7cd83abc38c7289cd6b5344b2a457bec5e55d808ce6a066ccbee5e458fdb2925",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
    "primary_cat": "econ.EM",
    "submitted_at": "2026-05-15T23:33:14Z",
    "title_canon_sha256": "419c55eb4a3c1b0e2f370bdfb05d5b33a5132aeed93ebd740cdaea45b635d07c"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.16703",
    "kind": "arxiv",
    "version": 1
  }
}