The Power and Limits of Distributed Representations in Strategic Decision Making

This paper develops a formal theory of distributed representations—collective cognitive models that emerge when organizations aggregate the simplified mental models of multiple members—and examines when they enhance or hinder strategic decision making. We extend Brunswik’s lens model to multiple decision makers and introduce “decision boundaries” from machine learning to explain how aggregation structures interact with individual internal representations across varying task environments. Using a mathematical model of project screening, we compare two prototypical aggregation rules (averaging and unanimity) against individual specialists (single-cue experts) and generalists (multicue learners) across various environments and levels of experience. Our analysis reveals that effectiveness depends critically on the three-way interaction between internal representations, aggregation structure, and environmental conditions: Specialists excel when one cue dominates; unanimity guards against errors when good projects are rare and decision makers lack experience; averaging delivers robust performance across most settings; and only highly experienced generalists outperform distributed representations, although such individuals are scarce in practice. These findings advance microfoundations by linking individual cognition and organizational aggregation, enrich the attention-based view by showing how cognitive processing and aggregation matter beyond attention allocation, and offer actionable guidance for designing decision processes under strategic uncertainty.