The fusion task is to merge document lists retrieved from a corpus for a query. We use the Condorcet voting rule to theoretically and empirically analyze fusion methods. We also demonstrate the merits of a novel fusion method based on a different voting rule: Copeland.

People have limited computational resources, yet they make complex strategic decisions over enormous spaces of possibilities. How do people efficiently search spaces with combinatorially branching paths? Here, we study players' search strategies for a winning move in a "k-in-a-row" game. We find that players use scoring strategies to prune the search space and augment this pruning by a "shutter" heuristic that focuses the search on the paths emanating from their previous move. This strong pruning has its costs-both computational simulations and behavioral data indicate that the shutter size is correlated with players' blindness to their opponent's winning moves. However, simulations of the search while varying the shutter size, complexity levels, noise levels, branching factor, and computational limitations indicate that despite its costs, a narrow shutter strategy is the dominant strategy for most of the parameter space. Finally, we show that in the presence of computational limitations, the shutter heuristic enhances the performance of deep learning networks in these end-game scenarios. Together, our findings suggest a novel adaptive heuristic that benefits search in a vast space of possibilities of a strategic game.