Keywords
Decentralized Pricing;
Brain-in-a-vat (BIV);
Platform Vibrancy;
Government Governance;
Event Study
How to Cite
Abstract
As data emerges as a novel factor of production, the issues of data asset ownership, valuation, and circulation have increasingly become focal points in financial research and policy-making. This paper employs the theoretical metaphor of a “brain without a vat” to map distributed cognition, information flows, and decentralized governance onto the data asset market, thereby constructing an analytical framework for decentralized data asset pricing.Drawing on on-chain transaction data from decentralized data trading platforms (e.g., Ocean Protocol, Streamr, Numerai) spanning 2019–2024, and extending the analysis with panel data from 600 firms, we test the core hypotheses through multivariate regression, event studies, and difference-in-differences/event-time designs (DID/event-time).The principal findings include: platform activity exerts a significant positive effect on the pricing efficiency of data assets; corporate application intensity likewise significantly enhances pricing efficiency; and both liquidity and governance participation positively contribute to price discovery. Event study results indicate that critical events (such as the launch of new datasets or major DAO votes) generate significant positive abnormal returns on event dates, with the conclusions remaining robust under multiple validation checks.
References
Üslü, S. (2019). Pricing and liquidity in decentralized asset markets. Econometrica, 87(6), 2079–2140. https://ideas.repec.org/a/wly/emetrp/v87y2019i6p2079-2140.html
Nguyen, M. H. (2025). The determinants of funding liquidity risk in decentralized finance. Journal of Financial Intermediation. https://www.sciencedirect.com/science/article/pii/S1044028324001273
Kumar, R. (2025). Decentralized finance evolution: A comprehensive bibliometric analysis. Journal of Financial Markets. https://www.sciencedirect.com/science/article/pii/S2666188825007713
Carapella, F., et al. (2024). The financial stability implications of digital assets. Economic Policy Review, November. https://www.newyorkfed.org/medialibrary/media/research/epr/2024/EPR_2024_digital-assets_azar.pdf
Abrar, K., & Hossein, G. (2025). Smart contracts and decentralized finance: Risks and governance. NYU Stern. https://www.stern.nyu.edu/sites/default/files/2025-05/Glucksman_Hossein_Smart%20Contracts%20and%20Decentralized%20Finance.pdf
Fan, Z., Marmolejo-Cossío, F., Moroz, D. J., Neuder, M., Rao, R., & Parkes, D. C. (2021). Strategic liquidity provision in Uniswap v3. arXiv. https://arxiv.org/abs/2106.12033
He, X. D., Yang, C., & Zhou, Y. (2024). Optimal design of automated market makers on decentralized exchanges. arXiv. https://arxiv.org/abs/2404.13291
Capponi, A., & Jia, R. (2021). The adoption of blockchain-based decentralized exchanges. arXiv. https://arxiv.org/abs/2103.08842
Lim, T. (2022). Predictive crypto-asset automated market making architecture for DeFi using deep reinforcement learning. arXiv. https://arxiv.org/abs/2211.01346
Hutchins, E. (1995). Distributed cognition. Cognitive Science Journal. https://en.wikipedia.org/wiki/Distributed_cognition
Hutto, D. D., & Myin, E. (2013). Evolving enactivism: Basic minds meet content. MIT Press. https://en.wikipedia.org/wiki/Enactivism
Bauer-Hänsel, I. (2024). Designing a blockchain-based data market and pricing model. Journal of Database Management, 35(3), 24–38. https://doi.org/10.1080/10864415.2023.2295068
Chadda, S., Chen, H.-Y., Mahadevia, R., Zhang, Q., & Alshareef, S. (2023). Exploring the performance and potential of synthetic assets in investment portfolios: A comparative analysis of three use cases. SSRN. https://ssrn.com/abstract=4507094
Grinschgl, S., & Neubauer, A. C. (2022). Supporting cognition with modern technology: Distributed cognition today and in an AI-enhanced future. Frontiers in Artificial Intelligence. https://doi.org/10.3389/frai.2022.908261
Hao, J. (2023). The evolution of data pricing: From economics to … PMC. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10560029/
Kumar, R. (2025). Decentralized finance evolution: A comprehensive bibliometric analysis. Journal of Financial Markets. https://www.sciencedirect.com/science/article/pii/S2666188825007713
Liu, Z., Nersessian, N. J., & Stasko, J. T. (2007). Distributed cognition as a theoretical framework for information visualization. IEEE InfoVis. https://faculty.cc.gatech.edu/~stasko/papers/infovis08-dcog.pdf
Nguyen, M. H. (2025). The determinants of funding liquidity risk in decentralized finance. Journal of Financial Intermediation. https://www.sciencedirect.com/science/article/pii/S1044028324001273
Pei, J. (2020). A survey on data pricing: From economics to data science. arXiv preprint. https://arxiv.org/abs/2009.04462
Üslü, S. (2019). Pricing and liquidity in decentralized asset markets. Econometrica, 87(6), 2079–2140. https://ideas.repec.org/a/wly/emetrp/v87y2019i6p2079-2140.html
Zhang, M., Beltran, F., & Liu, J. (2023). A survey of data pricing for data marketplaces. arXiv preprint. https://arxiv.org/abs/2303.04810
Liu, Ziyang, et al. "A hybrid YOLO-UNet3D framework for automated protein particle annotation in Cryo-ET images." Scientific Reports 15.1 (2025): 25033.
Xu, Libo, Chunhong Yuan, and Zuowen Jiang. "Multi-Strategy Enhanced Secret Bird Optimization Algorithm for Solving Obstacle Avoidance Path Planning for Mobile Robots." Mathematics 13.5 (2025): 717.
Wang, Yichen, et al. "An Efficient Scheduling Method in Supply Chain Logistics Based on Network Flow." Processes 13.4 (2025): 969.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright (c) 2025 Yinuo He, KaiFei Li, Jingyu Qi, Man Yang, Chunhua Feng (Author); Jiayi Chen (Co-Authors)