Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
86 tokens/sec
Gemini 2.5 Pro Premium
43 tokens/sec
GPT-5 Medium
19 tokens/sec
GPT-5 High Premium
30 tokens/sec
GPT-4o
93 tokens/sec
DeepSeek R1 via Azure Premium
88 tokens/sec
GPT OSS 120B via Groq Premium
441 tokens/sec
Kimi K2 via Groq Premium
234 tokens/sec
2000 character limit reached

The Cultural Transmission of Tacit Knowledge (2201.03582v1)

Published 10 Jan 2022 in physics.soc-ph, q-bio.NC, and q-bio.PE

Abstract: A wide variety of cultural practices take the form of "tacit" knowledge, where the rules and principles are neither obvious to an observer nor known explicitly by the practitioners. This poses a problem for cultural evolution: if beginners cannot simply imitate experts, and experts cannot simply say or demonstrate what they are doing, how can tacit knowledge pass from generation to generation? We present a domain-general model of "tacit teaching", that shows how high-fidelity transmission of tacit knowledge is possible. It applies in cases where the underlying features of the practice are subject to interacting and competing constraints, as is expected both in embodied and in social practices. Our model makes predictions for key features of the teaching process. It predicts a tell-tale distribution of teaching outcomes: some students will be nearly perfect performers while others receiving the same instruction will be disastrously bad. This differs from most mainstream cultural evolution models centered on high-fidelity transmission with minimal copying errors, which lead to a much narrower distribution where students are mostly equally mediocre. The model also predicts generic features of the cultural evolution of tacit knowledge. The evolution of tacit knowledge is expected to be bursty, with long periods of stability interspersed with brief periods of dramatic change, and where tacit knowledge, once lost, becomes essentially impossible to recover.

Summary

We haven't generated a summary for this paper yet.

Dice Question Streamline Icon: https://streamlinehq.com

Follow-up Questions

We haven't generated follow-up questions for this paper yet.