Persona-Trained Monte Carlo: Estimating Market-Outcome Distributions via Swarms of Persona-Conditioned Neural Policy Bots in a Limit Order Book
arXiv:2606.29556v1 Announce Type: new Abstract: We propose Persona-Trained Monte Carlo (PTMC), a method for estimating distributions of market-outcome statistics by repeatedly simulating limit-order-book interaction among swarms of persona-conditioned neural-policy trading bots. Each run instantiates many bots sharing one trained policy network but conditioned on heterogeneous, individually sampled persona parameters drawn from a learned trader-heterogeneity distribution; the bots interact in a continuous double auction, and the resulting price path is one Monte Carlo sample. Repeating this ov
The proliferation of advanced AI models and agentic systems makes it increasingly feasible to simulate complex economic interactions with high fidelity, moving beyond traditional econometric models.
This development allows for more accurate and dynamic forecasting of market behaviors under various conditions, offering a significant advantage in strategic market planning and risk assessment for institutional players.
The ability to simulate market-outcome distributions using persona-conditioned AI bots fundamentally changes how market impact is modeled and understood, moving from statistical correlation to simulated interaction.
- · Quantitative hedge funds
- · High-frequency trading firms
- · Market makers
- · Financial AI/ML developers
- · Traditional macroeconomic forecasters
- · Retail investors without advanced tools
- · Models reliant on historical data alone
More sophisticated and potentially unfair market strategies emerge among those with access to such simulation tools.
Increased market volatility as dominant firms exploit predictive advantages, potentially leading to new regulatory scrutiny.
The definition of market efficiency and arbitrage opportunities could be fundamentally redefined if market outcomes become highly predictable for a select few.
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Read at arXiv cs.LG