Options
2022
Book Article
Title
Tokenomics: Decentralized Incentivization in the Context of Data Spaces
Abstract
A significant challenge in bootstrapping a jointly used infrastructure such as Data Spaces is to incentivize the participants to invest in setting up the infrastructure. In this chapter, we investigate this challenge and possible solutions, focusing on an approach called “Tokenomics.”
The incentivization scheme should be utilized by governance frameworks, in which the participants of Data Spaces remain capable of action and independent through automated, effective, and fair decision-making processes. Also, potential participants should be motivated to participate in the establishment and further development of the system, while on the other hand, undesirable behavior should be penalized. In combination with distributed ledger technology (DLT) and machine-readable, legally compliant smart contracts, participant behavior can be affected in such a way that both data quality and quantity are improved for the whole Data Space.
To derive possible design options for Tokenomics approaches, we examine different token frameworks and their impact on participants. The investigation of the frameworks is carried out taking into account five significant domains: technical, behavior, inherent value, coordination, and pseudo-archetypes. Furthermore, we investigate which token designs provide smaller or larger incentives in order to join or maintain a DLT-based ecosystem.
The incentivization scheme should be utilized by governance frameworks, in which the participants of Data Spaces remain capable of action and independent through automated, effective, and fair decision-making processes. Also, potential participants should be motivated to participate in the establishment and further development of the system, while on the other hand, undesirable behavior should be penalized. In combination with distributed ledger technology (DLT) and machine-readable, legally compliant smart contracts, participant behavior can be affected in such a way that both data quality and quantity are improved for the whole Data Space.
To derive possible design options for Tokenomics approaches, we examine different token frameworks and their impact on participants. The investigation of the frameworks is carried out taking into account five significant domains: technical, behavior, inherent value, coordination, and pseudo-archetypes. Furthermore, we investigate which token designs provide smaller or larger incentives in order to join or maintain a DLT-based ecosystem.