Understanding and Anticipating the Emergent Outcomes of Algorithmic Matching on Digital Platforms

Abstract

Algorithmic matching facilitates value creation on digital platforms by connecting users to content, services, and to one another. Prior research on outcomes of algorithmic matching across different contexts, however, typically conceptualizes matching outcomes as individual- or population-level effects observed at a static point in time, assuming that these outcomes are non-emergent. We challenge this assumption and argue that the core characteristics of algorithmic matching make emergent, system-level outcomes not only possible but likely. We identify three foundational characteristics, data fidelity, reductionist interaction rules, and user intention heterogeneity, that jointly increase the likelihood of mismatches, triggering user adaptation. Drawing on complex adaptive systems as a meta-theoretical lens, we develop a multi-level theory that explains how three interacting feedback mechanisms generate dynamic, path-dependent emergent outcomes: ecosystem-level growth and diversity feedback, platform-level design and governance feedback, and user-level behavioral adaptation and prediction feedback. We demonstrate how these three feedback mechanisms can lead to emergent outcomes via an agent-based simulation of a dating platform. Our theorizing advances research on matching and complexity in digital platforms and offers a foundation for evaluating the long-run implications of algorithmic design and governance choices.