  Figgie is a card game that approximates open-outcry commodities trading. We
design strategies for Figgie and study their performance and the resulting
market behavior. To do this, we develop a flexible agent-based discrete-event
market simulation in which agents operating under our strategies can play
Figgie. Our simulation builds upon previous work by simulating latencies
between agents and the market in a novel and efficient way. The fundamentalist
strategy we develop takes advantage of Figgie's unique notion of asset value,
and is, on average, the profit-maximizing strategy in all combinations of agent
strategies tested. We develop a strategy, the "bottom-feeder", which estimates
value by observing orders sent by other agents, and find that it limits the
success of fundamentalists. We also find that chartist strategies implemented,
including one from the literature, fail by going into feedback loops in the
small Figgie market. We further develop a bootstrap method for statistically
comparing strategies in a zero-sum game. Our results demonstrate the
wide-ranging applicability of agent-based discrete-event simulations in
studying markets.
