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In this paper, we address the problem of designing incentive mechanisms by avirtual service provider (VSP) to hire sensing IoT devices to sell theirsensing data to help creating and rendering the digital copy of the physicalworld in the Metaverse. Due to the limited bandwidth, we propose to usesemantic extraction algorithms to reduce the delivered data by the sensing IoTdevices. Nevertheless, mechanisms to hire sensing IoT devices to share theirdata with the VSP and then deliver the constructed digital twin to theMetaverse users are vulnerable to adverse selection problem. The adverseselection problem, which is caused by information asymmetry between the systementities, becomes harder to solve when the private information of the differententities are multi-dimensional. We propose a novel iterative contract designand use a new variant of multi-agent reinforcement learning (MARL) to solve themodelled multi-dimensional contract problem. To demonstrate the effectivenessof our algorithm, we conduct extensive simulations and measure several keyperformance metrics of the contract for the Metaverse. Our results show thatour designed iterative contract is able to incentivize the participants tointeract truthfully, which maximizes the profit of the VSP with minimalindividual rationality (IR) and incentive compatibility (IC) violation rates.Furthermore, the proposed learning-based iterative contract framework haslimited access to the private information of the participants, which is to thebest of our knowledge, the first of its kind in addressing the problem ofadverse selection in incentive mechanisms.

Semantic Information Marketing in The Metaverse: A Learning-Based Contract Theory Framework