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We present an agent-based stock market simulation in which traders utilise a hybrid mixture of common information criteria based inference procedures, including minimum message length (MML) inference. Traders in our model compete with each other using a range of different inference techniques to infer the parameters and appropriate order of simple autoregressive (AR) models of stock price evolution. We show that such traders are initially profitable while a significant population of randomdoi:10.1109/ichis.2005.99 dblp:conf/his/CollieDF05 fatcat:qh2e7suv7fbftd6tnrrno6vnba