Understanding Business Network Dynamics Using Agent-based Modelling and Simulation
About this project
In progress 2016 - 2021
This project aims at gaining a better understanding of business network dynamics. More specifically, it is about the structural changes that emerge in business networks as results of endogenous and exogenous triggers. It focuses on the emergent change processes in business network, some of their influencing factors and their implications for the structure of the business networks. This research emphasizes the dynamics coming from multiple business relationships being terminated and new ones being established and addresses the structural changes that this generate. Industrial Networks Approach (INA/IMP) and Complexity Perspective are the theoretical points of departure in this thesis.
In this research business networks are viewed as complex adaptive systems in which heterogeneous business actors are interacting with each other. They are responding and adapting to the environment, and their non-linear local interactions give rise to emergent macro-level outcomes and regularities, which in turn may exert a top-down influence on the actors’ behaviour. To investigate the dynamics that this complexity creates a simulation and case methodology is used: Agent-based Modelling and Simulation (ABM).
To address the aim of the thesis two agent-based models of supply networks embedded in the empirical context of sustainability are designed and implemented. Both Agent-based Models of the thesis are representing business networks as real empirical phenomena. The models involve some levels of abstraction and are aimed at investigating some of the theoretical properties of the two supply networks.
This thesis project illustrates the potential of Agent-based Modeling and simulation in addressing business network dynamics specifically under more complex and highly interactive contexts with a large number of actors (beyond what is common within INA/IMP with the dominant case study methodology). It shows how ABM and simulations can contribute to a better understanding of business network dynamics.