| Title |
Gap distributions for analysing buyer behaviour in agent-based simulation / |
| Authors |
Ahrens, Andreas ; Purvinis, Ojaras ; Zaščerinska, Jelena |
| DOI |
10.5220/0007308900230029 |
| ISBN |
9789897583551 |
| Full Text |
|
| Is Part of |
Proceedings of the 8th international conference on sensor networks. Vol. 1: Sensornets 2019, February 26-27, 2019, Prague, Czech Republic / editors: C. Benavente-Peces, A. Ahrens, O. Camp.. Setúbal : SciTePress, 2019. p. 23-29.. ISBN 9789897583551 |
| Keywords [eng] |
agent-based simulation ; binary customer behaviour ; Buyers’ burstiness ; exponential distribution ; Gap processes ; Weibull distribution ; Wilhelm distribution |
| Abstract [eng] |
Simulation models allow predicting the development of real situations in various technical, business and social systems. However, many real situations in business environment are of bursty nature. Buyers often appear concentrated or, in other words, bursty. Different approaches for analysing buyers’ behaviour have been developed. One of these approaches focuses on analysis of gaps between buyers, and the buyers’ scenario is completely described by the sequence of gaps. The present research is interdisciplinary, namely telecommunications and business management. The methodology of the present contribution is built on adaptation of gap distribution functions from data transmission theory in telecommunications to bursty business process in business management. The aim of the paper is to demonstrate inter-connections between different gap distribution functions such as Weibull, Exponential and Wilhelm as well as to compare different gap distribution functions for their suitability when analysing bursty processes. Furthermore, this contribution provides the mathematical description of gap processes. The comparison results of different gap distribution functions are presented. The theoretical results are confirmed by practical implementation in agent-based simulation environment. |
| Published |
Setúbal : SciTePress, 2019 |
| Type |
Conference paper |
| Language |
English |
| Publication date |
2019 |
| CC license |
|
