In a Grove, Again: Optimizing over Feasible Event Histories for Incentive-Driven Testimony

We formulate narrative agency in testimony as a computational problem for Creative AI. Rather than generating different texts, agents select feasible histories from shared evidence. A deterministic simulator constructs $\mathrm{Feasible}(F)$, the set of histories consistent with public facts $F$. Each witness selects the feasible history that maximizes a transparent self-interest utility $U_i$. We study the framework's computational feasibility across three exactly enumerated domains: an agent-operations incident, an adaptation of Akutagawa's \emph{In a Grove}, and an adaptation of Browning's \emph{The Ring and the Book}. Enumeration provides ground truth. Q-learning recovers every reference optimum, while genetic search recovers all but the hardest Ring witness in $6/8$ seeds. This work provides a formal problem definition and feasibility framework for goal-driven history selection, rather than a new text-generation architecture.