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Incentive and Playful Strategy for a Participative Model of Learning and Experimenting Blockchain | IEEE Conference Publication | IEEE Xplore

Incentive and Playful Strategy for a Participative Model of Learning and Experimenting Blockchain


Abstract:

In this paper, we are interested in the scalability of simulator models of new technologies, such as the blockchain, which present many technical and consensual constrain...Show More

Abstract:

In this paper, we are interested in the scalability of simulator models of new technologies, such as the blockchain, which present many technical and consensual constraints and challenges. Thus our proposal focuses on an important feature of meta-models of such simulators, namely the management of the incentive to participate in the proposal to make them evolve continuously. This is done through the election of a king based on his significant contributions to the evolution of the model. The introduction of an evolution can be an optimization of the model's performance or the integration of a recent advance in technology. To better illustrate our proposal, we do an implementation of a blockchain simulator with a perspective of optimizing its upgradability and trying to move towards a single simulation platform based on our model that aggregates the learning and experiment of the blockchain while managing the low and high level details.
Date of Conference: 07-10 February 2021
Date Added to IEEE Xplore: 10 March 2021
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Conference Location: PyeongChang, Korea (South)

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