树状数据交易中的利润再分配机制

中文摘要 AI 辅助

数据市场有望释放其经济价值，然而在实践中，其活跃程度远低于预期——原因之一是数据供应方并未因其在下游创造的价值而获得回报。数据的一个决定性特征是它的可复制性：买方可以将购买的数据精炼成新产品，并将其转售给许多下游买方，因此，单一来源会催生出一连串转售的级联，自然形成一棵树。由于上游卖方无法获取任何下游价值，其交易动机被削弱。现有研究提出利润再分配——将部分下游收入返还给上游贡献者——作为一种自然的补救措施。但是，利润再分配在可复制数据实际引发的树状结构市场中是否有效，这一问题仍未得到解答。为弥合这一差距，我们为树状结构数据市场中的利润再分配建立了一个原则性框架。我们引入了一个树上的序贯交易博弈，以及一类在其上的一般预算可行利润再分配机制（PRM）。我们推导了计算诱导均衡的高效算法——对于离散估值，采用多项式时间精确算法；对于连续估值，采用完全多项式时间近似方案（FPTAS）——通过一种子树分解技术，该技术控制了卖方子女之间潜在的耦合。随后，我们证明，在温和假设下，任何预算可行的PRM都会弱化地扩大均衡中发生的交易，而任何预算平衡的PRM还会相对于不进行再分配的基线，弱化地改善社会福利。在合成市场上的实验证实，这些收益是显著的，即使在假设不成立时也依然存在，并且随着树的深度和分支的增加而增长。

英文摘要

Markets for data promise to unlock its economic value, yet in practice they remain far less active than expected---one reason is that those who supply data are not rewarded for the value it creates downstream. A defining feature of data is its \emph{replicability}: a buyer can refine purchased data into a new product and resell it to \emph{many} downstream buyers, so a single source seeds a branching cascade of resales that naturally forms a \emph{tree}. Because an upstream seller captures none of this downstream value, its incentive to trade is weakened. Existing works propose \emph{profit reallocation}---returning part of downstream revenue to upstream contributors---as a natural remedy. But whether profit reallocation works on the tree-structured markets that replicable data actually induces has remained open. To bridge this gap, we develop a principled framework for profit reallocation on tree-structured data markets. We introduce a sequential trading game on a tree and a general class of budget-feasible profit reallocation mechanisms (PRMs) over it. We derive efficient algorithms to compute the induced equilibria---a polynomial-time exact algorithm for discrete valuations and a fully polynomial-time approximation scheme (FPTAS) for continuous ones---via a subtree decomposition technique that tames the potential coupling across a seller's children. We then prove that, under mild assumptions, \emph{any} budget-feasible PRM weakly expands the trades that occur in equilibrium, and any budget-balanced PRM additionally weakly improves social welfare, relative to the baseline that reallocates nothing. Experiments on synthetic markets confirm that these benefits are substantial, persist even when the assumptions fail, and grow with the depth and branching of the tree.

发表机构

• CFCS, School of Computer Science, Peking University（北京大学计算机科学与技术学院前沿计算研究中心）
• School of Economics, Peking University（北京大学经济学院）
• Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）

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