Saad Khan's Publications

Sorted by DateClassified by Publication TypeClassified by Research Category

Agent-based modeling of a price information trading business

S.A. Khan and L. Bölöni. Agent-based modeling of a price information trading business. In Computer and Information Sciences II, pp. 361–367, Springer London, 2012.

Download

[PDF] 

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-2012-ISCIS,
  title={Agent-based modeling of a price information trading business},
  author={S.A. Khan and L. B{\"o}l{\"o}ni},
  booktitle={Computer and Information Sciences II},
  pages={361--367},
  year={2012},
  publisher={Springer London}
  bib2html_dl_pdf = {
  http://arxiv.org/pdf/1303.7445v1.pdf},
  bib2html_pubtype = {Refereed Conference},
  bib2html_rescat = {Autonomous Agents},
  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.
   }    
}

Generated by bib2html.pl (written by Patrick Riley, Saad Khan ) on Thu Jul 10, 2014 15:43:58