王会举, 黄玮煊, 岳晓. 基于劳资博弈模型的实用查询定价新算法[J]. 计算机研究与发展.
 引用本文: 王会举, 黄玮煊, 岳晓. 基于劳资博弈模型的实用查询定价新算法[J]. 计算机研究与发展.
Wang Huiju, Huang Weixuan, Yue Xiao. Novel Practical Query Pricing Algorithm Based on Labor Game Model[J]. Journal of Computer Research and Development.
 Citation: Wang Huiju, Huang Weixuan, Yue Xiao. Novel Practical Query Pricing Algorithm Based on Labor Game Model[J]. Journal of Computer Research and Development.

## Novel Practical Query Pricing Algorithm Based on Labor Game Model

• 摘要: 在数据要素化的推动下，传统查询定价方法因其前提假设要求过高、灵活动态性支持有限、关键因素考虑不足等问题，面临落地难的巨大挑战. 为解决以上问题，创新设计了基于劳资博弈模型的查询定价算法，该算法利用劳资博弈模型对数据交易中参与方进行建模，将数据交易平台和数据买方分别视作工会和用人单位；数据交易平台（工会）负责各交易数据集价值（劳动者工资）公平透明计算，以尽可能促成交易为目标；数据买方根据各数据集估量价值、自身需求和自身预算，决定各数据集购买数量，藉此实现兼顾三方利益的交易数据集定价. 实验表明，该算法相比于流行的斯塔克伯格博弈模型，更能兼顾各方利益，更加公平；相比于传统的基于查询的数据定价方法，该定价算法更易落地应用、更具动态灵活性，可以跟随查询结果的变化实现价格的动态调整. 该定价算法时间复杂度为O(N)（N为查询相关数据集个数），且具有无套利性.

Abstract: With the promotion of data as a production factor, traditional query pricing methods face tremendous challenges in practical applications due to their overly strict premise assumptions, limited support for flexibility and dynamics, and inadequate consideration of key factors. To address these issues, we innovatively design a query pricing algorithm based on the labor game model. This algorithm models the participants in data transactions as labor unions and employers, treating the data trading platform and data buyers as the labor union and employers, respectively. The data trading platform (labor union) is responsible for the fair and transparent calculation of the value of each traded dataset (wages), aiming to facilitate transactions as much as possible. Data buyers determine the purchase quantities of datasets based on their estimated value, personal needs, and budgets, thereby achieving a pricing strategy that balances the interests of all three parties. Experimental results demonstrate that compared to the popular Stackelberg game model, our algorithm better accommodates the interests of all parties and ensures greater fairness. Compared to traditional query-based data pricing methods, our pricing algorithm is more practical, offers greater flexibility and dynamics, and can dynamically adjust prices in response to changes in query results. The time complexity of our pricing algorithm is O(N), where N is the number of datasets related to the query, and it also guarantees no arbitrage.

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