Evidence map›Paper›PMID 35576210›Full record

ArticlePloS one2022

Institutional dynamics and learning networks.

Philip Poon, Jessica C Flack, David C Krakauer

Abstract read
In one paragraph

Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Philip PoonSanta Fe Institute, Santa Fe, NM, United States of America.
Jessica C FlackSanta Fe Institute, Santa Fe, NM, United States of America.
David C KrakauerSanta Fe Institute, Santa Fe, NM, United States of America.ORCID 0000-0002-4860-2700

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Institutions have been described as 'the humanly devised constraints that structure political, economic, and social interactions.' This broad definition of institutions spans social norms, laws, companies, and even scientific theories. We describe a non-equilibrium, multi-scale learning framework supporting institutional quasi-stationarity, periodicity, and switching. Individuals collectively construct ledgers constituting institutions. Agents read only a part of the ledger-positive and negative opinions of an institution-its "public position" whose value biases one agent's preferences over those of rivals. These positions encode collective perception and action relating to laws, the power of parties in political office, and advocacy for scientific theories. We consider a diversity of complex temporal phenomena in the history of social and research culture (e.g. scientific revolutions) and provide a new explanation for ubiquitous cultural resistance to change and novelty-a systemic endowment effect through hysteresis.

Indexed as

LearningSocial InteractionBiasHumans

Identifiers

PMID35576210
PMCPMC9109929

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.