Abstract
The claim that a person can use different strategies or processes to solve the same task is pervasive in decision making, categorization, estimation, reasoning, and other research fields. Yet such multi-strategy approaches differ widely in how they envision that the different strategies are coordinated and therefore do not represent one unitary approach. Toolbox models, for example, assume that people shift from one strategy to another as they adapt to specific task environments based on past experience. Unlike such multi-strategy selection approaches, multi-strategy blending approaches assume that the outputs of different strategies are blended into a joint, hybrid response (i.e., “wisdom of strategies” in one mind). The goal of this article is twofold. First, we discuss strategy blending as a conceptual alternative to strategy selection for modeling human judgment. Second, we investigate the predictive performance of the different approaches in synthetic and real-world environments. Taking a normative perspective, we study the coordination of rule-based and exemplar-based processes in estimation tasks. Our simulations using synthetic and real-world environments indicate that, for medium-sized samples, multi-strategy blending approaches lead to more accurate estimates than relying on a single strategy or selecting a strategy based on past experience—possibly because neither rule- nor exemplar-based processes in isolation are sufficient to capture statistical regularities that enable accurate estimates. This suggests that multi-strategy blending approaches can be advantageous to the degree that they rely on qualitatively different strategies.
Herzog, Stefan M