Representation Homogeneity and Systemic Instability in AI-Dominated Financial Markets: A Structural Approach

Paper: arXiv 2604.22818

Authors: Yimeng Qiu, Qiwei Han

Abstract

This paper investigates how similarity in the informational representation of market states among Artificial Intelligence (AI) trading agents can generate systemic instability in financial markets. We construct a structural multi-agent market model calibrated using high-frequency microstructural moments. AI agents are modeled through a two-layer decision architecture consisting of a nonlinear representation layer and an adaptive linear readout layer. The representation layer maps raw market states into high-dimensional feature vectors, while the readout layer generates return forecasts that feed into a risk-controlled trading rule. This representation-based microfoundation separates two objects that are often conflated in the literature: representation homogeneity (the degree to which agents encode market states into similar feature spaces) and forecast overlap (the degree to which agents produce similar return predictions). We show theoretically that these two concepts are related but not equivalent, and that representation homogeneity can compress the effective space of forecast disagreement under stress even when predictions appear diverse in normal times. Through controlled factorial experiments that vary representation homogeneity while conditioning on alternative risk-aversion and learning-rate distributions, we hypothesize that increasing representation similarity amplifies synchronization in beliefs and positions, leading to volatility clustering, liquidity stress, and elevated tail risk. Our structural mechanisms suggest that low perceived volatility regimes can endogenously accumulate hidden leverage through position stickiness, which subsequently collapses when shocks trigger synchronized deleveraging. The results provide a structural foundation for macroprudential policies aimed at monitoring and preserving diversity in how AI systems represent and process market information.

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 5.0/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper introduces a novel structural framework distinguishing representation homogeneity from forecast overlap in AI trading agents, backed by a formal two-layer decision architecture. While mathematically sophisticated and highly novel in its macroprudential focus, empirical rigor is limited to calibrated multi-agent simulations rather than extensive real-world backtesting on empirical datasets.

Research Flowchart

  flowchart TD
    A["Research Goal: Investigate how AI Representation Homogeneity drives Systemic Risk"] --> B["Data & Calibration: High-Frequency Microstructural Moments"]
    B --> C["Model Architecture: Multi-Agent Model with Two-Layer AI Agents"]
    C --> D["Computational Process: Factorial Experiments Varying Representation Similarity"]
    D --> E["Core Mechanism: Separation of Representation Homogeneity vs. Forecast Overlap"]
    E --> F["Key Outcomes: Belief Synchronization, Liquidity Stress, & Tail Risk"]
    F --> G["Policy Impact: Macroprudential Policies Preserving AI Representation Diversity"]

Cited References (13)

1. Heterogeneous beliefs and routes to chaos in a simple asset pricing model (1998)
2. Heterogeneous agent models in economics and finance (2006)
3. Agent-based computational finance (2006)
4. Agent-based computational economics: Growing economies from the bottom up (2002)
5. Inductive reasoning and bounded rationality (1994)
6. Growing Artificial Societies: Social Science from the Bottom Up (1996)
7. Continuous auctions and insider trading (1985)
8. Optimal dealer pricing under transactions and return uncertainty (1981)

0 of 13 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.