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- W2747342983 abstract "When a prediction market has to consider a kind of privacy protection, it is necessary to choose appropriate design parameters to treat the trade-offs to apply a transaction fee to avoid obtaining revenues only from local swings of the price and its side-effects about suppressing liquidity of the market to ensure the performance of prediction. In this paper, we propose an idea and its prototype framework for the parameter tuning mechanism to make tuning faster by applying a BLMAB algorithm to make tuning faster by applying a on the design of a prediction market which considers privacy protections." @default.
- W2747342983 created "2017-08-31" @default.
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- W2747342983 date "2017-07-01" @default.
- W2747342983 modified "2023-09-23" @default.
- W2747342983 title "A BLMAB-based parameter tuning approach for privacy-preserving prediction markets" @default.
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- W2747342983 doi "https://doi.org/10.1109/agents.2017.8015325" @default.
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