Endogenous Punishment-Driven Distributed Computing Environment for Data Factor Property Right Protection
DOI:
https://doi.org/10.66834/1zj2hv33Keywords:
Data Factor Property Rights Protection, Common Pool Resources Governance, Evolutionary Game, Endogenous Punishment, Network Address Translation(NAT)Abstract
The non-excludability and non-rivalry characteristics of data pose challenges to Coase’s property rights theory. Based on Ostrom’s Common Pool Resources (CPR) Governance Theory, this study proposes defining the data factor as a six-tuple and designing a computational environment that supports the protection of data factor property rights. This definition integrates the original dataset, transaction entities, commitments, supervision, and punishment into a unified whole, specifically referred to as data factor. Similar to the intellectual property protection system, the six-tuple first endows data factors with excludability in a contractual sense. Secondly, to effectively support such exclusivity, this study proposes a distributed property rights protection computing environment with the capabilities of “supervision-punishment-commitment”, adopting the “terminal-pipe-cloud” architectural template. This computing environment better matches the self-organizing nature of data trading activities. We use evolutionary game theory to verify how the new data factor definition promotes the development of trading markets. To provide practical reference, the simulation focuses on two key aspects: market stability under the coexistence of multiple strategies, and the impact of the initial conditions of the institutional environment on participants’ strategy choices.
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