  Real world markets display power-law features in variables such as price
fluctuations in stocks. To further understand market behavior, we have
conducted a series of market experiments on our web-based prediction market
platform which allows us to reconstruct transaction networks among traders.
  From these networks, we are able to record the degree of a trader, the size
of a community of traders, the transaction time interval among traders and
other variables that are of interest. The distributions of all these variables
show power-law behavior. On the other hand, agent-based models have been
proposed to study the properties of real financial markets. We here study the
statistical properties of these agent-based models and compare them with the
results from our web-based market experiments. In this work, three agent-based
models are studied, namely, zero-intelligence (ZI), zero-intelligence-plus
(ZIP) and Gjerstad-Dickhaut (GD). Computer simulations of variables based on
these three agent-based models were carried out. We found that although being
the most naive agent-based model, ZI indeed best describes the properties
observed in real markets. Our study suggests that the basic ingredient to
produce the observed properties from real world markets could in fact be the
result of a continuously evolving dynamical system with basic features similar
to the ZI model.
