Cultural evolution applies evolutionary concepts and tools to explain the change of culture over time. Despite advances in both theoretical and empirical methods, the connections between cultural evolutionary theory and evidence are often vague, limiting progress. Theoretical models influence empirical research but rarely guide data collection and analysis in logical and transparent ways. Theoretical models themselves are often too abstract to apply to specific empirical contexts and guide statistical inference. To help bridge this gap, we outline a quality-assurance computational workflow that starts from generative models of empirical phenomena and logically connects statistical estimates to both theory and real-world explanatory goals. We emphasize and demonstrate validation of the workflow using synthetic data. Using the interplay between conformity, migration, and cultural diversity as a case study, we present coded and repeatable examples of directed acyclic graphs, tailored agent-based simulations, a probabilistic transmission model for longitudinal data, and an approximate Bayesian computation model for cross-sectional data. We discuss the assumptions, opportunities, and pitfalls of different approaches to generative modeling and show how each can be used to improve data analysis depending on the structure of available data and the depth of theoretical understanding. Throughout, we highlight the significance of ethnography and of collecting basic cultural and demographic information about study populations and call for more emphasis on logical and theory-driven workflows as part of science reform.

中文翻译：

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桥接理论和数据：文化进化的计算工作流程


文化进化论应用进化论的概念和工具来解释文化随时间的变化。尽管理论和实证方法都取得了进步，但文化进化论和证据之间的联系往往是模糊的，这限制了进步。理论模型影响实证研究，但很少以合乎逻辑和透明的方式指导数据收集和分析。理论模型本身往往过于抽象，无法应用于特定的实证背景并指导统计推断。为了帮助弥合这一差距，我们概述了一个质量保证计算工作流程，该工作流程从经验现象的生成模型开始，并在逻辑上将统计估计与理论和现实世界的解释目标联系起来。我们强调并演示了使用合成数据对工作流程的验证。以从众、迁移和文化多样性之间的相互作用为案例研究，我们提出了有向无环图的编码和可重复示例、定制的基于代理的模拟、纵向数据的概率传输模型和横截面数据的近似贝叶斯计算模型。我们讨论了生成建模的不同方法的假设、机会和陷阱，并展示了如何根据可用数据的结构和理论理解的深度使用每种方法来改进数据分析。在整个过程中，我们强调了人种学和收集有关研究人群的基本文化和人口统计信息的重要性，并呼吁更加强调逻辑和理论驱动的工作流程，作为科学改革的一部分。