Agent-based modeling of a price information trading business

TL;DR

This paper presents an agent-based simulation of a fictional gas price information trading business, analyzing market dynamics, profit distribution, and strategic behaviors using real geographic and statistical data.

Contribution

It introduces a detailed agent-based model incorporating real-world data and negotiation strategies to study the properties of a gas price information market.

Findings

Quantifies potential profits and their distribution.

Analyzes the impact of pricing strategies on market sustainability.

Examines negotiation strategies and their effects on profits.

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.