S. A. Khan and L. Bölöni

Agent-based modeling of a price information trading business


Cite as:

S. A. Khan and L. Bölöni. Agent-based modeling of a price information trading business. In Proc. of 26th International Symposium on Computer and Information Sciences (ISCIS-2011), pp. 257–262, October 2011.

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Abstract:

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.

BibTeX:

@inproceedings{SKhan-2011-ISCIS,
   title = "Agent-based modeling of a price information trading business",
   author = "S. A. Khan and L. B{\"o}l{\"o}ni",
   booktitle = "Proc. of 26th International Symposium on Computer and Information Sciences (ISCIS-2011)",
   year = "2011",
   month = "October",
   pages = "257-262",
   abstract = {
   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.
   }
}

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