Pith Number

pith:YQYDIXUO

pith:2026:YQYDIXUOORWLU46TPJCAQTULAV

not attested not anchored not stored refs resolved

Large Learning Through Imitation: An Experiment

Gabriel Lopez-Moctezuma, Marina Agranov, Omer Tamuz, Philipp Strack

Observing and imitating others' actions leads agents to choose the optimal action more often even though actions add no new payoff information.

arxiv:2605.17662 v1 · 2026-05-17 · econ.TH

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

Despite the fact that actions contain no additional payoff-relevant information, and despite potential herd behavior, free riding and information overload issues, observing and imitating the actions of others leads agents to take the optimal action more often in the second setting.

C2weakest assumption

That the experimental implementation successfully isolates the effect of observing actions without introducing confounds from subject understanding, interface design, or payoff structure that would not exist in the theoretical environments described.

C3one line summary

An experiment finds that imitation of others' actions improves information aggregation and optimal choice rates compared to public data alone in repeated social learning settings.

References

44 extracted · 44 resolved · 0 Pith anchors

[1] M. Agranov, B. Gillen, and D. Persitz. Behavioral and structural barriers to information aggregation in networks. working paper, 2025 2025

[2] L. R. Anderson and C. A. Holt. Information cascades in the laboratory. The American economic review, pages 847--862, 1997 1997

[3] M. Angrisani, A. Guarino, P. Jehiel, and T. Kitagawa. Herd behavior in a laboratory financial market. American Economic Review, 95: 0 1427--1443, 2005 2005

[4] M. Angrisani, A. Guarino, P. Jehiel, and T. Kitagawa. Information redundancy neglect versus overconfidence: a social learning experiment. American Economic Journal: Microeconomics, 13 0 (3): 0 163--97 2021

[5] Y. Azrieli, C. Chambers, and P. Healy. Incentives in experiments: A theoretical analysis. Journal of Political Economy, 126: 0 1472--1503, 2018 2018

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:04:51.525148Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

c430345e8e746cba73d37a44084e8b0554550fbae37e1a2b4d96df5898a6d8a1

Aliases

arxiv: 2605.17662 · arxiv_version: 2605.17662v1 · doi: 10.48550/arxiv.2605.17662 · pith_short_12: YQYDIXUOORWL · pith_short_16: YQYDIXUOORWLU46T · pith_short_8: YQYDIXUO

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/YQYDIXUOORWLU46TPJCAQTULAV \
  | 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: c430345e8e746cba73d37a44084e8b0554550fbae37e1a2b4d96df5898a6d8a1

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "4e33460c6bff6740cd268993e38055d0bf62b8e9121e646b9df14dc1be21f6d4",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "econ.TH",
    "submitted_at": "2026-05-17T21:45:07Z",
    "title_canon_sha256": "6bbc9e9aaf16b010bbd619969d2cd4c925181e4d2c723ef72a18a9f39e9fba04"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.17662",
    "kind": "arxiv",
    "version": 1
  }
}