Cultural diffusion dynamics depend on behavioural production rules
Culture is an outcome of both the acquisition of knowledge about behaviour through social transmission, and its subsequent production by individuals. Acquisition and production are often discussed or modelled interchangeably, yet to date no study has explored the consequences of their interaction for cultural diffusions. We present a generative model that integrates the two, and ask how variation in production rules might influence diffusion dynamics. Agents make behavioural choices that change as they learn from their productions. Their repertoires may also change, and the acquisition of behaviour is conditioned on its frequency. We analyse the diffusion of a novel behaviour through social networks, yielding generalizable predictions of how individual-level behavioural production rules influence population-level diffusion dynamics. We then investigate how linking acquisition and production might affect the performance of two commonly used inferential models for social learning; network-based diffusion analysis, and experience-weighted attraction models. We find that the influence that production rules have on diffusion dynamics has consequences for how inferential methods are applied to empirical data. Our model illuminates the differences between social learning and social influence, demonstrates the overlooked role of reinforcement learning in cultural diffusions, and allows for clearer discussions about social learning strategies.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2022 |
|---|---|
| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:289 |
|---|---|
| Enthalten in: |
Proceedings. Biological sciences - 289(2022), 1980 vom: 10. Aug., Seite 20221001 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Chimento, Michael [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Agent-based model |
|---|
| Anmerkungen: |
Date Completed 11.08.2022 Date Revised 10.09.2022 published: Print-Electronic Dryad: 10.5061/dryad.vx0k6djvk Citation Status MEDLINE |
|---|
| doi: |
10.1098/rspb.2022.1001 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM344655334 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM344655334 | ||
| 003 | DE-627 | ||
| 005 | 20231226023259.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2022 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1098/rspb.2022.1001 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1148.xml |
| 035 | |a (DE-627)NLM344655334 | ||
| 035 | |a (NLM)35946158 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Chimento, Michael |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Cultural diffusion dynamics depend on behavioural production rules |
| 264 | 1 | |c 2022 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 11.08.2022 | ||
| 500 | |a Date Revised 10.09.2022 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Dryad: 10.5061/dryad.vx0k6djvk | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Culture is an outcome of both the acquisition of knowledge about behaviour through social transmission, and its subsequent production by individuals. Acquisition and production are often discussed or modelled interchangeably, yet to date no study has explored the consequences of their interaction for cultural diffusions. We present a generative model that integrates the two, and ask how variation in production rules might influence diffusion dynamics. Agents make behavioural choices that change as they learn from their productions. Their repertoires may also change, and the acquisition of behaviour is conditioned on its frequency. We analyse the diffusion of a novel behaviour through social networks, yielding generalizable predictions of how individual-level behavioural production rules influence population-level diffusion dynamics. We then investigate how linking acquisition and production might affect the performance of two commonly used inferential models for social learning; network-based diffusion analysis, and experience-weighted attraction models. We find that the influence that production rules have on diffusion dynamics has consequences for how inferential methods are applied to empirical data. Our model illuminates the differences between social learning and social influence, demonstrates the overlooked role of reinforcement learning in cultural diffusions, and allows for clearer discussions about social learning strategies | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a agent-based model | |
| 650 | 4 | |a cultural evolution | |
| 650 | 4 | |a experience weighted attraction models | |
| 650 | 4 | |a network-based diffusion analysis | |
| 650 | 4 | |a reinforcement learning | |
| 650 | 4 | |a social learning | |
| 700 | 1 | |a Barrett, Brendan J |e verfasserin |4 aut | |
| 700 | 1 | |a Kandler, Anne |e verfasserin |4 aut | |
| 700 | 1 | |a Aplin, Lucy M |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Proceedings. Biological sciences |d 1990 |g 289(2022), 1980 vom: 10. Aug., Seite 20221001 |w (DE-627)NLM012597821 |x 1471-2954 |7 nnns |
| 773 | 1 | 8 | |g volume:289 |g year:2022 |g number:1980 |g day:10 |g month:08 |g pages:20221001 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1098/rspb.2022.1001 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 289 |j 2022 |e 1980 |b 10 |c 08 |h 20221001 | ||