A Generative Model of Conspicuous Consumption and Status Signaling

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.

Figures (13)

• Figure 1: The figure illustrates how status preferences shift dynamically through agent-to-agent interaction rather than hard-coded utility functions. (Left) The Initial Preference Distribution shows an agent with a current snapshot of its preferences. (Middle) During the Social Interaction & Signal Transmission phase, the focal agent (left) interacts with a partner displaying a distinct status signal (an Armani blazer). The dialogue, which includes both casual conversation and specific acknowledgment of the item, is encoded into the agent's memory stream (depicted in the architecture schematic below). (Right) The Updated Preference Distribution shifts toward the observed status symbol (Armani Suit).
• Figure 2: Simulation Timeline and Daily Phases. The experiment runs for 5 days. The inset details the daily workflow: agents first compete for resources in 5 rounds of marketplace activity , followed by private daily life events. Finally, agents enter the social phase, where they engage in "first date" interactions consisting of visual observations of status goods and 80 turns of conversation.
• Figure 3: A comparison of consumer patterns on status goods across experimental conditions. Error bars represent SEM for 10 seeds. ${*} p < 0.05$, ${**} p < 0.01$, ${***} p < 0.001$.
• Figure 4: Price run-ups in single representative episode for two popular status goods. Dashed lines indicate breaks in between days where in Social condition the agents go on dates and have open dialogue.
• Figure 5: Heatmaps of prices across time for each item for an example run in the Social Life condition.

Theorems & Definitions (1)

• Conjecture 1: Social Exposure Increases Signaling through the Weight of Precedent