(untitled) (gjstudies, 2025)
This paper presents a novel, cross-disciplinary investigation into the role oftransparency 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 transparencyoperates 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 anagent-based simulation environment to model information exchange in two distinctdomains: (1) a simulated financial marketplace with algorithmic traders and humaninvestors, and (2) a longitudinal health data-sharing scenario inspired by continuous learning systems for developmental monitoring. Our results demonstrate thatdynamic, granular transparency—characterized by the explicability of data provenance, algorithmic intent, and uncertainty—significantly outperforms static, bulkdisclosure in reducing perceived and actual information asymmetry. Crucially, wefind a non-linear relationship: increasing transparency yields diminishing returnsin asymmetry reduction beyond a context-specific threshold, and can even increaseperceived asymmetry if it overwhelms cognitive capacity or reveals contradictoryinformation. The simulation reveals that the most effective asymmetry reductionoccurs when transparency mechanisms are adaptive, responding to the seeker’sevolving needs and the holder’s changing constraints. This research contributes anew theoretical lens and a computational methodology for designing transparencyinterventions in complex information environments, with implications for algorithmic governance, platform regulation, and collaborative data ecosystems.