Simulation and forecasting of digital pricing models for an e-procurement platform using an agent-based simulation model

Aneesh Zutshi, Antonio Grilo, Tahereh Nodehi, Ahmad Mehrbod and Ricardo Jardim-Goncalves

Journal of Simulation, 2018, vol. 12, issue 3, 211-224

Abstract: Online businesses can be represented as a complex interaction of interconnected online users responding to the value proposition of an online company. We propose a Dynamic Agent-Based Modeling framework (DYNAMOD) that aims to explain these complex dynamics. This framework aids in the creation of simulation models that mimic the actual market behavior and perform business forecasting and decision support functions. Through a case study of the largest e-procurement provider in Portugal – Vortal.biz, we simulate their pricing model and analyze revenue impact by optimizing pricing using genetic algorithms. The objective of this research is to propose agent-based model as an effective method to forecast the impact of pricing decisions.

Date: 2018
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DOI: 10.1057/s41273-016-0045-6

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