一个动态同分彩信息市场设计的统一框架
A Unified Framework for Dynamic Pari-Mutuel Information Market Design
摘要：
最近，一些新的同注分彩机制已经被引入，以组织或有债权市场。汉森介绍了一个做市商从对数评分规则，后来陈和彭诺克开发了一个成本函数公式的做市商。另一方面，Peters等人的SCPM模型基于使用凸优化模型的呼叫拍卖设置的想法。在这项工作中，我们开发了一个统一的框架，桥梁这些看似无关的模型集中组织或有债权市场。该框架，作为SCPM的推广，将支持许多理想的属性，如适当的评分，真实的出价（在近视的意义上），有效的计算，并保证最坏情况下的损失。事实上，我们的统一框架将使我们能够表达各种适当的评分规则，现有的或新的，从经典的效用函数在凸优化问题代表市场组织者。此外，我们利用对偶的概念表明，市场模型是等价的风险最小化问题的凸风险度量。这将使我们更清楚地了解各种机制所采取的风险态度的差异，特别是加深我们对汉森做市商等流行机制的直觉。总的来说，我们相信这项工作推进了我们的目标，市场组织者正在优化流行的同注分彩机制，将它们重新塑造成一个统一的框架。
英文摘要：
Recently, several new pari-mutuel mechanisms have been introduced to organize markets for contingent claims. Hanson introduced a market maker derived from the logarithmic scoring rule, and later Chen and Pennock developed a cost function formulation for the market maker. On the other hand, the SCPM model of Peters et al. is based on ideas from a call auction setting using a convex optimization model. In this work, we develop a unified framework that bridges these seemingly unrelated models for centrally organizing contingent claim markets. The framework, developed as a generalization of the SCPM, will support many desirable properties such as proper scoring, truthful bidding (in a myopic sense), efficient computation, and guarantees on worst case loss. In fact, our unified framework will allow us to express various proper scoring rules, existing or new, from classical utility functions in a convex optimization problem representing the market organizer. Additionally, we utilize concepts from duality to show that the market model is equivalent to a risk minimization problem where a convex risk measure is employed. This will allow us to more clearly understand the differences in the risk attitudes adopted by various mechanisms, and particularly deepen our intuition about popular mechanisms like Hanson's market-maker. In aggregate, we believe this work advances our understanding of the objectives that the market organizer is optimizing in popular pari-mutuel mechanisms by recasting them into one unified framework.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
