Cultural evolution in Vietnam's early 20th century: A Bayesian networks analysis of Hanoi Franco-Chinese house designs

• Published in: Social sciences & humanities open 2019, Vol.1 (1), p.100001, Article 100001
• Main Authors: Vuong, Quan-Hoang, Bui, Quang-Khiem, La, Viet-Phuong, Vuong, Thu-Trang, Ho, Manh-Toan, Nguyen, Hong-Kong T., Nguyen, Hong-Ngoc, Nghiem, Kien-Cuong P., Ho, Manh-Tung
• Format: Article
• Language: English
• Subjects: Bayesian network; Buddhism; Cultural evolution; Franco-Chinese style; French colonialism; Hamiltonian Markov chain Monte Carlo; Hanoi architecture; House façade; Old quarter
• ISSN: 2590-2911; 2590-2911
• DOI: 10.1016/j.ssaho.2019.100001

The study of cultural evolution has taken on an increasingly interdisciplinary and diverse approach in explicating phenomena of cultural transmission and adoptions. Inspired by this computational movement, this study uses Bayesian networks analysis, combining both the frequentist and the Hamiltonian Markov chain Monte Carlo (MCMC) approach, to investigate the highly representative elements in the cultural evolution of a Vietnamese city's architecture in the early 20th century. With a focus on the façade design of 68 old houses in Hanoi's Old Quarter (based on 248 photos and 78 data lines), the study argues that it is plausible to look at the aesthetics, architecture, and designs of the house façade to find traces of cultural evolution in Vietnam, which went through more than six decades of French colonization and centuries of sociocultural influence from China. The in-depth technical analysis, though refuting the presumed model on the probabilistic dependency among the variables, yields several results, the most notable of which is the strong influence of Buddhism over the decorations of the house façade. Particularly, in the top 5 networks with the best Bayesian Information Criterion (BIC) scores and small p-values, the variable for decorations (DC) always has a direct probabilistic dependency on the variable B for Buddhism. The paper then discusses these findings and suggests integrating Bayesian statistics in social sciences in general and for studies of cultural evolution and architectural transformation in particular.