The day in conversation for 2026-10-18

The discussion: Ruth: So, if we look at Delphos, the core idea seems to be that instead of us guessing which constants or transformations to add—like adding an intercept versus a quadratic term—we train an agent to decide that sequence based on maximizing some utility metric. Warren: That’s interesting because it moves away from purely heuristic searching. I was thinking last week about how we used gradient descent approximations for utility estimation; this approach seems to treat model specification as a sequential decision-making problem rather than a static optimization task. What constraints were you imposing on the action space for that Q-network? Iris: The constraint is how the agent navigates the modeling space itself. If it suggests adding a constant, does it mean we are comparing models with fixed vs. free intercepts, or is it suggesting parameters within a pre-defined structure? That’s crucial because if the action space isn't properly discretized, the learned Q-values might just be optimizing noise in an unrepresentative region of the utility landscape. Ruth: Exactly. And that leads me to think about those reward functions mentioned elsewhere. If Delphos is learning its own path, how does that compare statistically to a human researcher defining a reward function based on prior econometric intuition regarding behavioral plausibility? Are the agents' learned