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Posted by Mark Liberman

https://languagelog.ldc.upenn.edu/nll/?p=73134&utm_source=rss&utm_medium=rss&utm_campaign=skirt-length-oscillations

https://languagelog.ldc.upenn.edu/nll/?p=73134

…and other applications of non-linear dynamics. A press release from Northwestern University — "Bell-bottoms today, miniskirts tomorrow: Math reveals fashion's 20-year cycle":

Fashion insiders and beauty magazines have long cited the "20-year-rule"—the idea that clothing trends often resurface every two decades. According to Northwestern University scientists, that observation isn't just anecdotal. It's a mathematical reality.

In a new study, the Northwestern team developed a new mathematical model showing that fashion trends tend to cycle roughly every 20 years. By analyzing roughly 37,000 images of women's clothing spanning from 1869 to today, the team found that styles rise in popularity, fall out of favor and then eventually experience renewal. Along with supporting common perceptions about the life cycles of fads, the researchers say these results could help explain how new ideas spread in society.

The study's lead author, Emma Zajdela, will present these findings on Tuesday, March 17, at the American Physical Society (APS) Global Physics Summit in Denver. Her talk, "Back in Fashion: Modeling the Cyclical Dynamics of Trends," is part of the session "Statistical Physics of Networks and Complex Society Systems."

Emma Zajdela's abstract:

Many people have experienced firsthand the idea that "fashion comes back," from bell-bottom jeans to mini-skirts. Historically, a lack of quantitative data posed a barrier to explicit mathematical study of this system; however, newly digitized historical records now make such work possible. We constructed a new database quantifying tens of thousands of women's dresses from 1869 to present day. Our analysis indicates that fashion is cyclical and, remarkably, in line with common knowledge in the fashion industry, this cycle is approximately 20 years long. We developed a mathematical model to understand and predict the evolution of these trends inspired by a continuous-time version of bounded confidence interval models for opinion dynamics. This model includes the idea of "optimal distinctiveness," which has been shown to be present in other dynamics of human innovation and time-delay dynamics. This conceptually simple mechanistic model performs well at replicating the dynamics of the trends observed. Large-scale social phenomena such as fashion trends are of intrinsic interest themselves, but a better understanding of this fashion system will contribute to elucidating the interplay of creativity, differentiation, conformity, and diffusion of ideas in broader human systems.

In fashion (as in music, birdsong, and language), maximal appeal comes from the introduction of  modest innovation into familiar patterns.

Zajdela's 2023 PhD dissertation — "Mathematical modeling of complex systems with applications to scientific collaboration at conferences and chimera states for coupled oscillators":

Complex systems exhibit the remarkable property that the behavior of the collective is greater than the sum of its parts. Mathematical modeling validated with data provides understanding of the underlying mechanisms that drive these emergent behaviors. Here, we present models of two types of complex systems using an applied dynamical systems framework: a social system and a system of coupled oscillators. The first model predicts how scientific collaborations form at in-person and virtual conferences. The second analyzes coupled oscillators with amplitude and proves the existence and stability of a new class of chimera states, the “phase chimera.”

And an application to conference dynamics — Zajdela, Emma R., Kimberly Huynh, Andrew L. Feig, Richard J. Wiener, and Daniel M. Abrams. "Face-to-face or face-to-screen: A quantitative comparison of conferences modalities." PNAS nexus 4, no. 1 (2025):

The COVID-19 pandemic forced a societal shift from in-person to virtual activities, including scientific conferences. As society navigates a “new normal,” the question arises as to the advantages and disadvantages of these alternative modalities. We introduce two new comprehensive datasets enabling direct comparison between virtual and in-person conferences: the first, from a series of nine small conferences, encompasses over 12,000 pairs of potential scientific collaborators across five virtual and four in-person meetings on a range of scientific topics; the expressed goal of these conferences is to create novel collaborations. The second dataset, from a series of three large physics conferences, encompasses >250,000 possible pairs of scientific collaborators. Our study provides quantitative insight into benefits and drawbacks of virtual and in-person conferences for team formation, community building, and engagement. We demonstrate the causal role of formal interaction on team formation across both modalities. Our findings show that formal interaction impacted team formation significantly more in virtual settings, while informal interaction played a larger role at in-person conferences as compared with virtual. We show that a nonlinear memory model for predicting team formation based on interaction outperforms seven alternative models. The model suggests that prior knowledge and interaction time contribute to catalyzing collaborations in both settings. Our results underscore the critical responsibility of organizers for optimizing professional interactions, whether virtual or in-person.

https://languagelog.ldc.upenn.edu/nll/?p=73134&utm_source=rss&utm_medium=rss&utm_campaign=skirt-length-oscillations

https://languagelog.ldc.upenn.edu/nll/?p=73134