Comparison of the Behavior of Human and Robot Traders in a Computerized Double Auction Market

本项目进行了一系列的人力双向拍卖市场 实验，以生成匹配的数据集的比赛， “机器人交易员”在人工的双重拍卖市场中竞争。 双重拍卖市场是简化版的AURORA 电子双向拍卖市场最近开发的芝加哥 贸易委员会。 将对行为进行精确的比较 人类和机器人交易员的区别 现有的人体实验数据 双向拍卖市场不能直接与 计算机化的市场，因为后者将时间分为离散的 买卖步骤，并采用了新的制度 设计，芝加哥规则，只允许持有最高 待执行的最低出价或最低出价 交易 该项目利用实验来研究 在芝加哥规则中， 市场与离散的交易区间，以及后果 对可以发送和接收的消息的限制 交易员。 本研究的结果将改进实验方法， 我们对广泛使用的市场制度的理解。 通过匹配 人类实验数据与比赛的结果， 计算机化的机器人交易员，该项目将能够提供 对“人工智能”代理人能力的强有力考验 来模仿人类交易者在双向拍卖市场中的行为。 如果计算机模拟可以补充人类实验， 将大大增加实验的数量和类型， 研究人员可以用来研究机构的影响， 变化 实验的结果也应该提供新的 对双重拍卖的性能的见解。 这是 这很重要，因为双向拍卖是一种交易方式， 芝加哥交易所使用的机构，纽约股票交易所 以及许多其他证券和商品交易所 在全世界都有。

This project conducts a series of human double auction market experiments to generate a matching data set for a tournament of "robot traders" who compete in an artificial double auction market. The double auction market is a simplified version of the AURORA electronic double auction market recently developed by the Chicago Board of Trade. Precise comparisons will be made of the behavior of human and robot traders. Existing experimental data on human double auction markets are not directly comparable to the computerized market because the latter divides time into discrete bid-offer and buy-sell steps, and adopts a new institutional design, Chicago Rules, that allows only holders of the highest outstanding bid or lowest outstanding offer to execute transactions. The project uses the experiments to study the impact of Chicago rules, the effects of variations in period length in a market with discrete trading intervals, and the consequences of restrictions on the messages that can be transmitted and received by traders. The results of this research will improve experimental methods and our understanding of widely used market institutions. By matching human experimental data with the results from the tournament of computerized robot traders, the project will be able to provide strong tests of the ability of "artificially intelligent" agents to mimic the behavior of human traders in Double-Auction markets. If computer simulations could complement human experiments, this would dramatically increase the number and type of experiments that investigators could use to study the effects of institutional changes. The results of the experiments should also provide new insights into the performance of double auctions. This is important because the double auction is the type of trading institution used by the Chicago Board of Trade, the New York Stock Exchange, and many other securities and commodities exchanges throughout the world.