papers/earle-genetic-marketing/sections/07-discussion.tex

\section{Discussion}


\subsection{Limitations}

\textbf{Cold start.} New campaigns lack historical data for surrogate modeling. We address this through transfer learning from similar campaigns.

\textbf{Platform constraints.} Advertising APIs impose rate limits and approval delays. Batch deployments and pre-approval queues mitigate these.

\textbf{Attribution complexity.} Multi-touch attribution complicates fitness measurement. We use last-click attribution with view-through windows.

\subsection{Ethical Considerations}

Automated marketing optimization raises ethical concerns:

\begin{itemize}
\item \textbf{Manipulation}: Optimized persuasion techniques may exploit cognitive biases.
\item \textbf{Privacy}: Targeting refinement may identify vulnerable populations.
\item \textbf{Authenticity}: Generated content may lack human authenticity.
\end{itemize}

We address these through mandatory human review, prohibited targeting categories, and transparency requirements.