Architecture Log: Systemic Constraints in Agent-Based Modelling Category: Financial Engineering / Systems Architecture

The following script represents the transition of a financial simulation from a theoretical prototype to a production-grade engine. The core logic was generated by AI, with architectural constraints applied to address specific failures common in standard economic modelling.

The objective was to move beyond "Ensemble Averaging" and introduce non-linear constraints that reflect the realities of risk, intangible value, and market trust.

colab.research.google.com

Google Colab

1. Addressing Non-Ergodicity

Standard simulations often rely on ensemble averages, where the success of a few outliers masks the ruin of the majority. This assumes the system is ergodic—that the group average applies to the individual over time. In reality, human performance is non-ergodic; ruin is an absorbing state.

Technical Implementation: The engine was updated to utilise Time Averaging rather than Ensemble Averaging. We introduced an "Absorbing Barrier" mechanism. If an agent’s performance metrics fall below a calculated RUIN_THRESHOLD, the agent does not revert to the mean; they exit the active system. This aligns the model with the mathematical reality that negative divergence can be terminal.

2. Latent Variables & The McNamara Fallacy

A common error in quantitative modelling (the McNamara Fallacy) is presuming that unmeasurable factors, such as cultural capital, have a value of zero. Conversely, assigning them a static "index" value is equally flawed, as these assets do not behave like income.

Technical Implementation: We restructured "Intangibles" as a latent variable representing Optionality rather than cash flow. In the script, cultural capital functions as a stochastic resilience buffer—a probability function that triggers only when an agent encounters the absorbing barrier. This models systemic resilience without artificially inflating productivity metrics.

3. Endogenous Friction (Trust Decay)

In standard models, transaction costs are typically fixed constants. However, in low-trust environments, these costs rise dynamically. The initial script failed to account for the economic impact of divergence between "Perceived Value" (Signal) and "Realised Value" (Truth).

Technical Implementation: We introduced a recursive feedback loop for Epistemic Trust. The system now calculates the "Trust Gap" (the delta between signal and truth) at each time step.

• If the gap widens, a TRUST_DECAY coefficient is applied.

• This coefficient correlates inversely with SYSTEM_FRICTION (transaction costs).

  
  

This ensures the model accurately penalises high-variance systems where hype outpaces reality, preventing the simulation from optimizing for false signals.

Summary

The resulting engine is "production-ready" not due to code complexity, but due to the integration of these boundary conditions. By explicitly modelling ruin, latent optionality, and dynamic friction, the script provides a robust environment for stress-testing economic assumptions.