A full-factorial experiment in the Public Goods Game shows that information regime and incentive strength explain 85.8% of cooperation-rate variance; algorithm choice accounts for just 3.8%. Agents with the least information cooperate most (83% vs 42% under full observation), attributable to state-space compression. TreeSHAP and Shapley-variance decomposition confirm information structure, not algorithm selection is the primary design lever for cooperation.

The execution of large portfolio transactions requires balancing market impact and adverse price drift. The Almgren-Chriss (2001) framework provides a meanvariance trade-off for martingale price processes, but practitioners often utilize short-term alpha signals. This paper re-evaluates the optimal liquidation problem using Stochastic Optimal Control. By incorporating a mean-reverting alpha signal into the price dynamics, we derive a closed-form solution using the Hamilton-Jacobi-Bellman (HJB) equation. The resulting optimal trading rate is an affine function of the current inventory and the predictive signal. This results in a trajectory that adjusts execution speed to capture transient alpha. This work provides a transparent and additive framework for institutional execution desks.