## 3 The Bike-Share Case
### Evaluation--Integration and Policy Analysis  
In contrast to partial testing, which emphasises the rationality of one or more subsystems (i.e., procedural rationality), whole-model testing incorporates all problem-relevant \begin{table}
\begin{tabular}{l l l} \hline \hline
**Procedure** & **Description** \\ \hline Bias Correction Layer & \\ \hline
1. Initialise the Layer & Create a trainable bias term initialised to zero. \\ \hline
2. Forward Pass (Call Method) & For each prediction, adjust it by adding the bias term. \\ \hline
3. Configuration Retrieval & Return the layer configuration. \\ \hline Asymmetric Hybrid Loss Function & \\ \hline \multirow{6}{*}{1. Initialise the Loss Function} & Parameters: \\  & alpha: Weight for combining MSE and MAE. \\  & lambda\_bias: Penalty for bias correction. \\  & lambda\_over: Penalty for over-predictions. \\  & lambda\_under: Penalty for under-predictions. \\  & local\_corr: Whether to compute bias correction locally (per \\  & sample) or globally (batch-wise). \\ \hline \multirow{6}{*}{2. Compute the Loss} & Calculate prediction errors: \(Y_{pred}=Y_{true}\). \\  & Separate errors into over-predictions (positive) and \\  & under-predictions (negative). \\ \cline{1-1}  & Calculate MAE with different penalties for over- and \\ \cline{1-1}  & under-predictions. \\ \cline{1-1}  & Calculate MSE for all errors. \\ \cline{1-1}  & Combine MSE and MAE using the weighting parameter \(\alpha\). \\ \hline \multirow{6}{*}{3. Bias Correction} & Calculate the bias correction term, either locally \\  & (sample-wise) or globally (batch-wise). \\ \cline{1-1}  & Add a penalty for bias to the loss, scaled by \(\lambda\) bias. \\ \hline \multirow{2}{*}{4. Return Final Loss} & Final loss is the weighted sum of MSE, MAE and the bias \\ \cline{1-1}  & correction penalty. \\ \hline \multirow{2}{*}{5. Configuration Retrieval} & Return the loss function configuration, including \\ \cline{1-1}  & all hyperparameters. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of adjustments to the LSTM model for bias correction. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Model** & **MSE Rentals** & **MSE Returns** & **Bias Rentals** & **Bias Returns** \\ \hline STAT & 1.46 & 1.27 & 0 & 59 \\ \hline ML HI-B & 1.05 & 1.03 & 18 & \(-\)59 \\ \hline ML LO-B & 1.05 & 1.02 & 17 & 29 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Accuracy and bias metrics for the three scenarios in partial model testing. interactions between subsystems. These inter-subsystem interactions are crucial for assessing whether the simplifying assumptions driving the design of decision rules remain valid in light of the system's emergent dynamic complexity. A common artefact of dynamic complexity is policy resistance, which occurs when subsystem goals pull in different directions, leading to behaviours contradicting overarching system goals. Policy resistance is one example of an archetype in systems literature, defined as \"common patterns of problematic behaviour\" ([28], p. 111). While there are many archetypes, policy resistance is the most relevant for assessing the impact of ML interventions on whole-system behaviour, particularly when algorithms and human judgment interact. Since ML is less likely to be influenced by existing organisational beliefs [24], it is more open to exploration, potentially placing the organisation in an unexplored decision space. However, this novelty--seen as beneficial within the limited scope of the ML model--might induce resistance elsewhere in the system. Moreover, non-linearities and delays, typical of complex systems, mean that resistance may not manifest immediately, often producing a \"better before worse\" effect ([29], p. 59). Our approach in this final step of the bike-share case builds on this insight. Based on assumptions about the future state mental model, we vary control variables that inform the judgmental component to map the fitness or performance landscape.