  This paper is intended to explain, in simple terms, some of the mechanisms
and agents common to multiagent financial market simulations. We first discuss
the necessity to include an exogenous price time series ("the fundamental
value") for each asset and three methods for generating that series. We then
illustrate one process by which a Bayesian agent may receive limited
observations of the fundamental series and estimate its current and future
values. Finally, we present two such agents widely examined in the literature,
the Zero Intelligence agent and the Heuristic Belief Learning agent, which
implement different approaches to order placement.
