"The Inseparable Experiment"
# The Inseparable Experiment
In a flotation cell, crushed ore is mixed with water and reagents. Air bubbles carry valuable minerals to the surface while waste sinks. The operator adjusts reagent dosages, air flow rates, and residence times to maximize recovery. The problem is that the operator doesn't know exactly what's in the ore arriving at the cell. The mineralogy varies — sometimes silently, sometimes dramatically — and the optimal settings depend on a composition that is never directly observed.
The traditional approach separates this into two phases. First, characterize the ore — measure what you can, estimate what you can't. Then, given your best estimate, optimize the process settings. Characterization, then action. Analysis, then execution.
The researchers formulated the same problem as a Partially Observable Markov Decision Process. In this framework, every processing action simultaneously produces output and generates information about the ore. Changing the air flow rate doesn't just affect recovery — it changes the froth characteristics in ways that reveal something about the mineral composition. Every action is an experiment. Every experiment is production. The system learns to choose actions that are jointly optimal for both learning and earning, because the two cannot be separated.
The insight is not that information has value — that's well known. The insight is that in any system with hidden state, the distinction between learning and doing is an artifact of how the problem is formulated, not a feature of the problem itself. The flotation cell doesn't know whether it's being characterized or optimized. The ore doesn't care. The division between analysis and action is a convenience for the analyst, not a structure of the world.
