The essay discusses the challenges of traditional prediction markets, focusing on the issues of fairness and accuracy in event outcomes. It highlights the shortcomings of human judgment in settling contracts and proposes utilizing blockchain and large language models (LLMs) as objective arbitrators to enhance transparency and fairness. The suggested approach aims to eliminate human subjectivity, thereby restoring liquidity and trust in prediction markets through a more consistent and transparent process. Experts recommend gradual implementation of these technologies for effective judgment systems.
Furthermore, the paper explores how blockchain technology can provide an immutable record of all transactions, reducing manipulation and increasing accountability. It also examines the potential of large language models to interpret complex data and provide unbiased assessments, which could significantly improve the reliability of market outcomes. The integration of these tools is expected to address current limitations, such as delayed settlements and disputes caused by human error or bias.
The authors emphasize that careful, phased deployment is essential to ensure system stability and public acceptance. They suggest pilot programs and continuous monitoring to refine the technology and build confidence among users. Ultimately, the goal is to create prediction markets that are more fair, transparent, and resistant to manipulation, leveraging cutting-edge blockchain and AI advancements.