The Ethics of LLM Sandbox and Persona Dynamics

Battle Count: 50

The paper is primarily about AI ethics and LLM design, not quantitative trading. However, it uses extensive financial regulation analogies (Basel I/II/III, VaR model manipulation, London Whale, Societe Generale rogue trader) to illustrate how formal safety systems become gameable and performative. The discussion of reality laundering in trading contexts (leverage, liquidity, crowded positions, pump-and-dump dynamics, asymmetric information) is directly relevant to how LLM-based trading advisors might misrepresent market realities. The paper's critique of VaR model manipulation and risk measurement reflexivity has implications for AI-assisted risk management. The proposed top-down causal requirements framework could inform how trading AI systems should be specified to preserve market microstructure realities while blocking harmful actions.

As a conceptual paper, there is no implementation to assess. The proposed framework (top-down causal requirements specification) is described at a high level of abstraction without concrete algorithms, architectures, or code. Implementing the ideas would require significant interdisciplinary work spanning AI safety engineering, requirements engineering, causal modeling, and domain-specific expertise. The paper provides philosophical direction rather than technical specifications.

This is a purely conceptual/theoretical paper with no experiments, datasets, code, or quantitative results. It presents a philosophical argument supported by analogies from financial regulation and compliance literature. There is nothing to computationally reproduce. The arguments are normative and structural rather than empirical.