Computation in a distributed information market
Computation in a distributed information market
复制标题
分布式信息市场中的计算
DOI：
10.1145/779928.779947
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发表时间：
2003
期刊：
影响因子：
--
通讯作者：
Rahul Sami
中科院分区：
文献类型：
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作者：
J. Feigenbaum;L. Fortnow;David M. Pennock;Rahul Sami
根据由经验和实验室证据支持的经济理论，金融证券的均衡价格反映了有关证券价值的所有信息。我们调查的计算过程中的路径走向均衡，在交易者之间分布的信息逐步揭示随着时间的推移，并纳入市场价格。我们开发了一个简化的信息市场模型，沿着交易策略，以正式的计算过程中的属性。我们表明，证券的收益不能表示为加权阈值函数的分布式输入位不能保证收敛到适当的均衡预测的经济理论。另一方面，证券的收益是阈值函数，保证收敛，所有的先验概率分布。此外，这些阈值证券收敛在最多$n$轮，其中$n$是分布式信息的位数。我们还证明了一个下界，显示了一种类型的阈值安全，需要至少$n/2$轮收敛在最坏的情况下。
According to economic theory supported by empirical and laboratory evidence, the equilibrium price of a financial security reflects all of the information regarding the security's value. We investigate the computational process on the path toward equilibrium, where information distributed among traders is revealed step-by-step over time and incorporated into the market price. We develop a simplified model of an information market, along with trading strategies, in order to formalize the computational properties of the process. We show that securities whose payoffs cannot be expressed as weighted threshold functions of distributed input bits are not guaranteed to converge to the proper equilibrium predicted by economic theory. On the other hand, securities whose payoffs are threshold functions are guaranteed to converge, for all prior probability distributions. Moreover, these threshold securities converge in at most $n$ rounds, where $n$ is the number of bits of distributed information. We also prove a lower bound, showing a type of threshold security that requires at least $n/2$ rounds to converge in the worst case.