  We describe an agent-based simulation of a fictional (but feasible)
information trading business. The Gas Price Information Trader (GPIT) buys
information about real-time gas prices in a metropolitan area from drivers and
resells the information to drivers who need to refuel their vehicles.
  Our simulation uses real world geographic data, lifestyle-dependent driving
patterns and vehicle models to create an agent-based model of the drivers. We
use real world statistics of gas price fluctuation to create scenarios of
temporal and spatial distribution of gas prices. The price of the information
is determined on a case-by-case basis through a simple negotiation model. The
trader and the customers are adapting their negotiation strategies based on
their historical profits.
  We are interested in the general properties of the emerging information
market: the amount of realizable profit and its distribution between the trader
and customers, the business strategies necessary to keep the market operational
(such as promotional deals), the price elasticity of demand and the impact of
pricing strategies on the profit.
