{"title":"从思想到物质:考古记录中的创新模式与社会学习生态学","authors":"Kathryn Demps, Nicole M. Herzog, Matt Clark","doi":"10.1017/aaq.2023.71","DOIUrl":null,"url":null,"abstract":"<p>Archaeology and cultural evolution theory both predict that environmental variation and population size drive the likelihood of inventions (via individual learning) and their conversion to population-wide innovations (via social uptake). We use the case study of the adoption of the bow and arrow in the Great Basin to infer how patterns of cultural variation, invention, and innovation affect investment in new technologies over time and the conditions under which we could predict cultural innovation to occur. Using an agent-based simulation to investigate the conditions that manifest in the innovation of technology, we find the following: (1) increasing ecological variation results in a greater reliance on individual learning, even when this decreases average fitness due to the costs of learning; (2) decreasing population size increases variability in the types of learning strategies that individuals use; among smaller populations drift-like processes may contribute to randomization in interpopulation cultural diffusion; (3) increasing the mutation rate affects the variability in learning patterns at different rates of environmental variation; and (4) increasing selection pressure increases the reliance on social learning. We provide an open-source R script for the model and encourage others to use it to test additional hypotheses.</p>","PeriodicalId":7424,"journal":{"name":"American Antiquity","volume":"49 1","pages":""},"PeriodicalIF":2.7000,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"From Mind to Matter: Patterns of Innovation in the Archaeological Record and the Ecology of Social Learning\",\"authors\":\"Kathryn Demps, Nicole M. Herzog, Matt Clark\",\"doi\":\"10.1017/aaq.2023.71\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Archaeology and cultural evolution theory both predict that environmental variation and population size drive the likelihood of inventions (via individual learning) and their conversion to population-wide innovations (via social uptake). We use the case study of the adoption of the bow and arrow in the Great Basin to infer how patterns of cultural variation, invention, and innovation affect investment in new technologies over time and the conditions under which we could predict cultural innovation to occur. Using an agent-based simulation to investigate the conditions that manifest in the innovation of technology, we find the following: (1) increasing ecological variation results in a greater reliance on individual learning, even when this decreases average fitness due to the costs of learning; (2) decreasing population size increases variability in the types of learning strategies that individuals use; among smaller populations drift-like processes may contribute to randomization in interpopulation cultural diffusion; (3) increasing the mutation rate affects the variability in learning patterns at different rates of environmental variation; and (4) increasing selection pressure increases the reliance on social learning. We provide an open-source R script for the model and encourage others to use it to test additional hypotheses.</p>\",\"PeriodicalId\":7424,\"journal\":{\"name\":\"American Antiquity\",\"volume\":\"49 1\",\"pages\":\"\"},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2023-12-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"American Antiquity\",\"FirstCategoryId\":\"98\",\"ListUrlMain\":\"https://doi.org/10.1017/aaq.2023.71\",\"RegionNum\":1,\"RegionCategory\":\"历史学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ANTHROPOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"American Antiquity","FirstCategoryId":"98","ListUrlMain":"https://doi.org/10.1017/aaq.2023.71","RegionNum":1,"RegionCategory":"历史学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ANTHROPOLOGY","Score":null,"Total":0}
引用次数: 0
摘要
考古学和文化进化理论都预测,环境变异和人口规模会(通过个人学习)影响发明的可能性,并(通过社会吸收)将其转化为全人口的创新。我们利用大盆地采用弓箭的案例研究来推断文化变异、发明和创新的模式如何随着时间的推移影响对新技术的投资,以及我们可以预测文化创新发生的条件。利用基于代理的模拟来研究技术创新的表现条件,我们发现了以下几点:(1)生态变异的增加会导致对个体学习的更大依赖,即使由于学习成本而降低了平均适合度;(2)种群数量的减少会增加个体所使用的学习策略类型的变异性;在较小的种群中,类似漂移的过程可能会导致种群间文化传播的随机化;(3)突变率的增加会影响不同环境变异率下学习模式的变异性;以及(4)选择压力的增加会增加对社会学习的依赖。我们为该模型提供了一个开源的 R 脚本,并鼓励其他人使用它来检验其他假设。
From Mind to Matter: Patterns of Innovation in the Archaeological Record and the Ecology of Social Learning
Archaeology and cultural evolution theory both predict that environmental variation and population size drive the likelihood of inventions (via individual learning) and their conversion to population-wide innovations (via social uptake). We use the case study of the adoption of the bow and arrow in the Great Basin to infer how patterns of cultural variation, invention, and innovation affect investment in new technologies over time and the conditions under which we could predict cultural innovation to occur. Using an agent-based simulation to investigate the conditions that manifest in the innovation of technology, we find the following: (1) increasing ecological variation results in a greater reliance on individual learning, even when this decreases average fitness due to the costs of learning; (2) decreasing population size increases variability in the types of learning strategies that individuals use; among smaller populations drift-like processes may contribute to randomization in interpopulation cultural diffusion; (3) increasing the mutation rate affects the variability in learning patterns at different rates of environmental variation; and (4) increasing selection pressure increases the reliance on social learning. We provide an open-source R script for the model and encourage others to use it to test additional hypotheses.