We study two well-known electronic markets: an over-the-counter (OTC) market, in which each trader looks for the best counterpart through bilateral negotiations, and a double auction (DA) market, in which traders post their quotes publicly. We focus on the DA–OTC efficiency gap and show how it varies with different market sizes (10, 20, 40, and 80 traders). We compare experimental results from a sample of 6400 undergraduate students in Economics and Management with zero-intelligence agent-based simulations. Simulation results show that the traded quantity increases with market size under both DA and OTC. Experimental results confirm the same tendency under DA, while the share of periods in which the traded quantity is lower than the efficient one increases with market size under OTC, ultimately leading to a DA–OTC efficiency gap increasing with the market size. We rationalize these results by putting forward a simple model of OTC market as a repeated bargaining procedure under incomplete information on buyers' valuations and sellers' costs. We show that efficiency decreases slightly with size due to two counteracting effects: acceptance rates in earlier interactions decrease with size, and earlier offers increase, but not always enough to compensate for the decrease in acceptance rates.

中文翻译：

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零智能与人工代理：对不同规模的双重拍卖和场外交易市场效率的实验分析

我们研究了两个著名的电子市场：场外交易 (OTC) 市场，每个交易者通过双边谈判寻找最佳对手；以及双重拍卖 (DA) 市场，交易者公开发布报价。我们关注 DA-OTC 效率差距，并展示它如何随着不同市场规模（10、20、40 和 80 位交易员）而变化。我们将 6400 名经济学和管理学本科生样本的实验结果与基于零智能代理的模拟进行了比较。模拟结果表明，在DA和OTC下，交易量随着市场规模的增加而增加。实验结果证实了 DA 下的相同趋势，而交易量低于有效时段的份额随着 OTC 市场规模的增加而增加，最终导致 DA-OTC 效率差距随着市场规模的扩大而增加。我们通过提出一个简单的场外交易市场模型来合理化这些结果，作为在买方估值和卖方成本信息不完整的情况下的重复谈判程序。我们表明，由于两个抵消效应，效率随规模略有下降：早期交互中的接受率随规模下降，早期报价增加，但并不总是足以弥补接受率的下降。