feat: adding clarity and rewording

paper/src/mirrors/genpop/03-methodology.tex

@@ -130,9 +130,9 @@ To speak to realism, user interviews reported that the platform architecture mir
 
 The dynamic pricing mechanism elicited immediate behavioral adjustments. Participants were sensitive to price volatility: sudden boosts triggered urgency and faster booking attempts, while large listing-to-final discrepancies triggered deeper comparison behavior. This is comforting because the controlled setup still produces commercially relevant interaction data.
 
-\subsubsection{Design of Training Factorial Study}
+\subsubsection{Design of Training Sweeps}
 
-The simulator has multiple configurable factors. We design a multi-factor study across five axes derived from the sweep configurations: (1) RL algorithm (PPO, A2C, DQN, Q-table; 4 levels), (2) contamination ratio sampled at four representative levels between 0.1 and 0.6, (3) robustness radius (3 levels), (4) COI penalty weight at two reference levels, and (5) pricing action granularity (two discretization settings for action levels); giving a grid of 192 configurations. Behavioral distinguishability is assessed with a two-sample Mann--Whitney test on per-session divergence gap scores at cohort sizes $n_H=13$ and $n_A=16$.
+The simulator has multiple configurable factors. Training runs are driven by Weights \& Biases sweep definitions versioned with the codebase, mixing random and grid schedules rather than a single full factorial. For the contamination ratio $\alpha$, exploratory sweeps draw $\alpha$ uniformly on $[0.1,0.6]$; some sweeps use the narrower interval $[0.1,0.5]$. Grid sweeps fix explicit level sets, for example $\alpha\in\{0.1,0.2,0.3,0.4,0.6,0.8\}$ (six levels, including $0.8$ beyond the typical exploratory upper endpoint) or five levels $\{0.1,0.2,0.3,0.4,0.6\}$. Auxiliary schedules also include $\alpha=0$ alongside positive values. Robustness radius $\epsilon_\alpha$, COI penalty $\lambda_\text{coi}$, RL algorithm (\texttt{ppo}, \texttt{a2c}, \texttt{dqn}, \texttt{qtable}), and the discretization of the price action grid vary by sweep. Broad random search may use uniform $\epsilon_\alpha\in[0,0.3]$ and $\lambda_\text{coi}\in[0.05,0.6]$; tighter grids may fix $\epsilon_\alpha=0.2$ and restrict $\lambda_\text{coi}$ to $\{0.15,0.30\}$. Behavioral distinguishability is assessed with a two-sample Mann--Whitney test on per-session divergence gap scores at cohort sizes $n_H=13$ and $n_A=16$.
 
 While this scale is generally expensive for reinforcement learning, we execute it on a large TPU cluster to make the sweep tractable.