A Generative Model of Conspicuous Consumption and Status Signaling

Generative AI & LLMs
Published: arXiv: 2603.13220v1
Authors

Logan Cross Jordi Grau-Moya William A. Cunningham Alexander Sasha Vezhnevets Joel Z. Leibo

Abstract

Status signaling drives human behavior and the allocation of scarce resources such as mating opportunities, yet the generative mechanisms governing how specific goods, signals, or behaviors acquire prestige remain a puzzle. Classical frameworks, such as Costly Signaling Theory, treat preferences as fixed and struggle to explain how semiotic meaning changes based on context or drifts dynamically over time, occasionally reaching tipping points. In this work, we propose a computational theory of status grounded in the theory of appropriateness, positing that status symbols emerge endogenously through a feedback loop of social observation and predictive pattern completion. We validate this theory using simulations of groups of Large Language Model (LLM)-based agents in the Concordia framework. By experimentally manipulating social visibility within naturalistic agent daily routines, we demonstrate that social interactions transform functional demand into status-seeking behavior. We observe the emergence of price run-ups and positive price elasticity (Veblen effects) for both real-world luxury items and procedurally generated synthetic goods, ruling out pretraining bias as the sole driver. Furthermore, we demonstrate that "influencer" agents can drive the endogenous formation of distinct subcultures through targeted sanctioning, and find that similar social influence effects generalize to non-monetary signaling behaviors. This work provides a generative bridge between micro-level cognition and macro-level economic and sociological phenomena, offering a new methodology for forecasting how cultural conventions emerge from interaction.

Paper Summary

Problem
The main problem addressed in this research paper is understanding why certain goods, behaviors, or ideas become potent status symbols while others fade, and why their meanings shift so dynamically across cultures and time. Researchers have struggled to empirically isolate the mechanisms governing these displays, as they emerge organically and are socially constructed. Current theoretical frameworks offer only a patchwork of independent explanations for these phenomena.
Key Innovation
The researchers propose a new computational theory of status grounded in the theory of appropriateness. This theory posits that status symbols emerge endogenously through a feedback loop of social observation and predictive pattern completion. They validate this theory using simulations of groups of Large Language Model (LLM)-based agents in the Concordia framework. This approach allows them to model the complexity of real-world semiotics, from fashion choices to subcultural signaling.
Practical Impact
This research has the potential to improve our understanding of human behavior, particularly in the context of status signaling and conspicuous consumption. By developing a generative model of status, the researchers can provide insights into how status symbols emerge, shift, and propagate. This knowledge can be applied in various fields, such as marketing, sociology, and psychology, to better understand consumer behavior and social dynamics. Additionally, this research can inform the development of more effective interventions to address issues related to social comparison and consumerism.
Analogy / Intuitive Explanation
Imagine a game of "dress-up" where players observe and imitate each other's fashion choices. In this game, certain items, like designer clothing or accessories, become status symbols because they are associated with prestige and social approval. The researchers propose that this process of social observation and imitation is a key driver of status signaling in real life. By modeling this process using LLM agents, they can simulate the emergence of status symbols and understand how they spread through social networks.
Paper Information
Categories:
cs.MA
Published Date:

arXiv ID:

2603.13220v1

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