Semantic Information Marketing in The Metaverse: A Learning-Based
Contract Theory Framework
- URL: http://arxiv.org/abs/2302.11457v1
- Date: Wed, 22 Feb 2023 15:52:37 GMT
- Title: Semantic Information Marketing in The Metaverse: A Learning-Based
Contract Theory Framework
- Authors: Ismail Lotfi, Dusit Niyato, Sumei Sun, Dong In Kim, Xuemin (Sherman)
Shen
- Abstract summary: We address the problem of designing incentive mechanisms by a virtual service provider (VSP) to hire sensing IoT devices to sell their sensing data.
Due to the limited bandwidth, we propose to use semantic extraction algorithms to reduce the delivered data by the sensing IoT devices.
We propose a novel iterative contract design and use a new variant of multi-agent reinforcement learning (MARL) to solve the modelled multi-dimensional contract problem.
- Score: 68.8725783112254
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In this paper, we address the problem of designing incentive mechanisms by a
virtual service provider (VSP) to hire sensing IoT devices to sell their
sensing data to help creating and rendering the digital copy of the physical
world in the Metaverse. Due to the limited bandwidth, we propose to use
semantic extraction algorithms to reduce the delivered data by the sensing IoT
devices. Nevertheless, mechanisms to hire sensing IoT devices to share their
data with the VSP and then deliver the constructed digital twin to the
Metaverse users are vulnerable to adverse selection problem. The adverse
selection problem, which is caused by information asymmetry between the system
entities, becomes harder to solve when the private information of the different
entities are multi-dimensional. We propose a novel iterative contract design
and use a new variant of multi-agent reinforcement learning (MARL) to solve the
modelled multi-dimensional contract problem. To demonstrate the effectiveness
of our algorithm, we conduct extensive simulations and measure several key
performance metrics of the contract for the Metaverse. Our results show that
our designed iterative contract is able to incentivize the participants to
interact truthfully, which maximizes the profit of the VSP with minimal
individual rationality (IR) and incentive compatibility (IC) violation rates.
Furthermore, the proposed learning-based iterative contract framework has
limited access to the private information of the participants, which is to the
best of our knowledge, the first of its kind in addressing the problem of
adverse selection in incentive mechanisms.
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