The paper begins by observing the common phenomenon of trends becoming overcrowded and losing their initial creative energy. It establishes the central research question: how does the balance between invention and imitation shift as a trend gains popularity, particularly when the number of imitators surpasses the number of experts? This question connects to several fields, including cultural evolution, social economics, social-learning theory, complexity theory, and ecology. The authors acknowledge the varying terminology used across these fields to describe the invention-imitation dichotomy (e.g., innovation vs. adoption, producers vs. scroungers, exploration vs. exploitation) but emphasize that the core concept remains consistent. For the study, the terms "experts" (inventors) and "imitators" (adopters) are used. The paper posits that a critical ratio of experts to imitators exists; a high ratio fuels novelty, while a low ratio results in stagnation. Popular culture, being novelty-driven, is particularly susceptible to this dynamic. The authors hypothesize that trends exhibiting boom-and-bust dynamics will show a critical transition point where the expert-imitator ratio decreases sharply, resulting in a "dilution of expertise" rather than its complete disappearance. This transition is expected to be accompanied by decreased diversity and increased redundancy as imitation surpasses invention. Simple diffusion models are noted as inadequate because they assume a fixed expert-imitator ratio, whereas this study aims to model the dynamic shift in this ratio. The paper further explains that cultural products are complex systems combining existing and novel elements, and the study will use this multi-component perspective in analysis.

The paper draws on existing literature in several fields to build its theoretical framework. It cites Schumpeter's definition of innovation as the copying of unique inventions and Bass's model of new product growth. It also references work on social learning, using concepts like "producers" and "scroungers." The literature on complexity science and business is incorporated through the exploration-exploitation dichotomy. Existing theories on cultural evolution, social economics, social learning, complexity and ecology are reviewed, highlighting the common thread of the critical balance between invention and imitation. The authors review models of cultural transmission and diffusion, noting the limitations of existing models in capturing the dynamic shift between invention and imitation phases during the growth and decline of a cultural phenomenon. Finally, the role of novelty in popular culture is discussed, supported by examples from various domains like video games, cryptocurrencies, and internet memes. The authors highlight the necessity of a high ratio of experts to imitators to sustain novelty and the potential for this ratio to change over time, resulting in a “boom-and-bust” cycle.

The study uses three boom-and-bust case studies and three control datasets. The boom-and-bust cases include Atari 2600 video games (1980s), cryptocurrency documentation (2010s), and Reddit r/Punpatrol posts (2018). Controls include Commodore Vic-20 games, scientific publications in optics, and Reddit r/Politics posts. Data collection involved retrieving video game ROMs, cryptocurrency white papers, and Reddit posts. Data processing included reverse engineering source codes from Atari ROMs, extracting text from PDF files, and using natural language processing (NLTK) techniques for Reddit data. The hierarchical structure of cultural products (e.g., games, documents, posts) is represented as a three-layered multi-layered network: product, sentence, symbol. Interactions between elements are represented as a bipartite network. For natural-language texts, stop words are eliminated, and synonyms are replaced with their most frequent counterparts. The study uses a modified Polya urn model to simulate cultural production, representing invention and imitation as a sequential random sampling process. The model links invention and imitation rates to populations of experts and imitators, which dynamically change based on the success rate of producing novel artifacts (the probability of finding at least one novel component). Three metrics are used to characterize cultural product diversity: lexical diversity (Zipf's law exponent), information density (Lempel-Ziv-Welch compression), and structural complexity (block decomposition method – BDM). The Polya urn model is used to predict the evolution of these metrics under different imitation-invention ratios. Finally, the model is compared with actual data to test the research hypothesis regarding the dilution of expertise.

The study found that all three boom-and-bust datasets exhibited a rapid increase in cultural products followed by a collapse. The model accurately predicted the temporal dynamics of cultural productivity, showing that the initial growth is driven by experts. The interplay between cultural reinforcement and individual strategies then leads to a dilution of expertise, as imitation outweighs invention. The abundance of imitators is highest in the Atari case study. Even when the number of experts exceeds imitators in the declining phase, the diversity loss is irreversible. Comparisons between the boom-and-bust datasets and their controls reveal key differences in several metrics: •Lexical diversity: The Zipf's law exponent increases in the boom-and-bust cases as they enter the exponential growth phase, reflecting a higher ratio of imitators to experts. •Information density: Information density decreases in the boom-and-bust cases as they enter the exponential growth phase, indicating increased redundancy. •Structural complexity: Structural complexity decreases during the boom-and-bust periods in the Atari and cryptocurrency datasets, consistent with predictions for increasing imitation. The control datasets do not show this significant decline. In the Reddit case study, the target community shows a significant decrease in lexical diversity and information density compared to the control community, supporting the hypothesis.

The findings support the hypothesis that a dilution of expertise occurs during the boom-and-bust cycles of cultural production. The model successfully captures the interplay between invention and imitation, providing a mechanism for explaining the observed patterns of diversity and complexity. The study highlights the importance of considering the dynamic interaction between experts and imitators rather than assuming a fixed ratio. This challenges existing diffusion models that often rely on static ratios. The results suggest that the decline in cultural productivity is an intrinsic property of the system, resulting from the inability of the expert community to maintain its relative size during rapid growth. The limitations of simple diffusion models are underscored, emphasizing the necessity of incorporating dynamic expert-imitator ratios for a more accurate representation of cultural evolution. The paper’s framework demonstrates the importance of multi-scale analysis in cultural evolution studies and opens avenues for incorporating more complex factors like social network structure and memory effects in future research.

The study provides strong evidence for the "dilution of expertise" hypothesis. A novel model accurately predicts boom-and-bust dynamics and the associated changes in diversity and complexity. This framework offers a more nuanced understanding of cultural evolution by incorporating the dynamic interplay between invention and imitation and emphasizes the importance of maintaining a balance between the two. Future research could focus on refining the model by incorporating more sophisticated population structures, memory effects, and exploring mechanisms for mitigating the negative consequences of expertise dilution. The development of early warning signals for excessive imitation could be particularly valuable.

The model simplifies certain aspects of cultural production, such as assuming each agent creates one artifact per time unit. The analysis relies on specific metrics for diversity, complexity, and information density; different choices might yield varying results. The case studies are diverse, potentially limiting the generalizability of the findings across different cultural domains. While the study provides a compelling argument for the dilution of expertise, the exact thresholds for the transition point between invention and imitation dominance need further investigation.