The Role of Transparency in Reducing Information Asymmetry

Abstract

This paper presents a novel, cross-disciplinary investigation into the role of
transparency as a dynamic, multi-layered mechanism for reducing information asymmetry, moving beyond its traditional treatment in economics and information systems as a static, binary variable. We argue that conventional models fail to capture the complex, iterative, and context-dependent nature of how transparency
operates in socio-technical systems. To address this gap, we introduce the Transparency Feedback Loop (TFL) framework, a computational model that conceptualizes transparency not as an endpoint but as a continuous process of signal generation, interpretation, and trust calibration between information holders and seekers.
The framework integrates concepts from complex systems theory, behavioral economics, and human-computer interaction. We implement the TFL framework in an
agent-based simulation environment to model information exchange in two distinct
domains: (1) a simulated financial marketplace with algorithmic traders and human
investors, and (2) a longitudinal health data-sharing scenario inspired by continuous learning systems for developmental monitoring. Our results demonstrate that
dynamic, granular transparency—characterized by the explicability of data provenance, algorithmic intent, and uncertainty—significantly outperforms static, bulk
disclosure in reducing perceived and actual information asymmetry. Crucially, we
find a non-linear relationship: increasing transparency yields diminishing returns
in asymmetry reduction beyond a context-specific threshold, and can even increase
perceived asymmetry if it overwhelms cognitive capacity or reveals contradictory
information. The simulation reveals that the most effective asymmetry reduction
occurs when transparency mechanisms are adaptive, responding to the seeker’s
evolving needs and the holder’s changing constraints. This research contributes a
new theoretical lens and a computational methodology for designing transparency
interventions in complex information environments, with implications for algorithmic governance, platform regulation, and collaborative data ecosystems.