Fashion and art cycles

Peter Klimek, Robert Kreuzbauer, Stefan Thurner, Fashion and art cycles are driven by counter-dominance signals of elite competition: quantitative evidence from music styles, 10 Jan 2019, arXiv:1901.03114v1 [physics.soc-ph]

Abstract: Human symbol systems such as art and fashion styles emerge from complex social processes that govern the continuous re-organization of modern societies. They provide a signaling scheme that allows members of an elite to distinguish themselves from the rest of society. Efforts to understand the dynamics of art and fashion cycles have been based on 'bottom-up' and 'top down' theories. According to 'top down' theories, elite members signal their superior status by introducing new symbols (e.g., fashion styles), which are adopted by low-status groups. In response to this adoption, elite members would need to introduce new symbols to signal their status. According to many 'bottom-up' theories, style cycles evolve from lower classes and follow an essentially random pattern. We propose an alternative explanation based on counter-dominance signaling. There, elite members want others to imitate their symbols; changes only occur when outsider groups successfully challenge the elite by introducing signals that contrast those endorsed by the elite. We investigate these mechanisms using a dynamic network approach on data containing almost 8 million musical albums released between 1956 and 2015. The network systematically quantifies artistic similarities of competing musical styles and their changes over time. We formulate empirical tests for whether new symbols are introduced by current elite members (top-down), randomness (bottom-up) or by peripheral groups through counter-dominance signals. We find clear evidence that counter-dominance-signaling drives changes in musical styles. This provides a quantitative, completely data-driven answer to a century-old debate about the nature of the underlying social dynamics of fashion cycles.

A note on their method:

Empirical tests are then needed to determine which model mechanism best describe s the actual evolution of musical styles. To this end we developed a method to quantify musical styles by determining each style’s typical instrumentation. From a dataset containing almost eight million albums that have been released since 1950, we extracted information about a user - created taxonomy of fifteen musical genres, 422 musical styles, and 570 different instruments. The instruments that are typically associated with a given genre (or style) were shown to be a suitable approximation to formally describe the characteristics of a style [ 29 ]. Therefore, the similarity between styles can be quantified through the similarity of their instrumentation. For instance, in Figure 1A we show an example of four different musical styles (blue circles) that are linked to five instruments (green squares). Here a link indicates that the instrument is (typically) featured in a release belonging to that style. The higher the overlap in instruments between two styles, the higher is their similarity and the thicker is the line that connects the styles in Figure 1A.