Cargando…
Institutional dynamics and learning networks
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 su...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9109929/ https://www.ncbi.nlm.nih.gov/pubmed/35576210 http://dx.doi.org/10.1371/journal.pone.0267688 |
_version_ | 1784708987397079040 |
---|---|
author | Poon, Philip Flack, Jessica C. Krakauer, David C. |
author_facet | Poon, Philip Flack, Jessica C. Krakauer, David C. |
author_sort | Poon, Philip |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9109929 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-91099292022-05-17 Institutional dynamics and learning networks Poon, Philip Flack, Jessica C. Krakauer, David C. PLoS One Research Article 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. Public Library of Science 2022-05-16 /pmc/articles/PMC9109929/ /pubmed/35576210 http://dx.doi.org/10.1371/journal.pone.0267688 Text en © 2022 Poon et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Poon, Philip Flack, Jessica C. Krakauer, David C. Institutional dynamics and learning networks |
title | Institutional dynamics and learning networks |
title_full | Institutional dynamics and learning networks |
title_fullStr | Institutional dynamics and learning networks |
title_full_unstemmed | Institutional dynamics and learning networks |
title_short | Institutional dynamics and learning networks |
title_sort | institutional dynamics and learning networks |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9109929/ https://www.ncbi.nlm.nih.gov/pubmed/35576210 http://dx.doi.org/10.1371/journal.pone.0267688 |
work_keys_str_mv | AT poonphilip institutionaldynamicsandlearningnetworks AT flackjessicac institutionaldynamicsandlearningnetworks AT krakauerdavidc institutionaldynamicsandlearningnetworks |