When Data Pricing Meets Non-Cooperative Game Theory

Driven by the growing field of data intelligence, data market emerges as a promising paradigm for data exchange, enabling the full utilization of data. Data pricing is an essential function in data market that reflects the values or cost of data and is dependent on interactions among multiple market participants including data buyers, data sellers, and data brokers. Game theory presents a promising approach to model the multi-participant interplay in data pricing, yet challenged by the specific nature of data. In this paper, we present a blueprint for applying game theory to data pricing. From a game-theoretic perspective, we highlight the unique characteristics of data (compared to traditional goods) and suggest important desiderata for effective data pricing. We identify four key dimensions (Participant, Object, Action, and Information) to understand the landscape of game theory based data pricing. Within each dimension, data-specific challenges and research gaps are identified. Our work establishes a foundational understanding of data pricing through the lens of game theory and opens up promising research directions in this developing field.