| dc.description.abstract | Climate change is one of the most important global issues the world is facing today. Many governments,
including the Dutch government, have agreed to take measures in order to reduce carbon
emission and reverse climate change. One of the measures taken by the Dutch government aims
to decrease the amount of fossil fuel transportation by encouraging the purchase of battery-only
electric vehicles (BEV). By implementing policies, which target different segments of the population,
policy-makers try to encourage the population to make the switch from conventional internal
combustion engine vehicles (ICEVs) to battery-only electric vehicles (BEVs). Before policies can
be introduced, policy-makers want to know whether their targeted policies can be implemented
in a cost efficient way. With our research we introduce an Agent-Based Model (ABM) in which
the dynamic effects of policies can be observed. The model is created using real world data. The
agents in our model behave according to the expectations of policy-makers and other theoretical
assumptions. The agent population is subdivided into segments. The effect of the target policies
on the different segments is analysed.
Our model allows the users to influence the behaviour of agents by the introduction of target
policies, enabling users to observe the effect of target policies on different population segments.
Afterwards, we try to assess whether our model can aid policy-makers in determining how costeffective
their policies are. We evaluate the usefulness of our model by letting policy makers validate
multiple simulated scenarios. Resulting from our evaluation we are able to show that our model
is an easily accessible tool for policy makers, allowing them to test their assumptions, and observe
the effects of their policies on the dynamics of the simulation. The behaviour of the simulation can
be easily explained and adjusted, allowing policy-makers to observe different hypothetical future
scenarios.
From the construction and validation of the Agent-Based Segmentation Model, we are able to
generalise our findings and answer our main research goal: how can population segmentation be
applied in a useful way within Agent-Based Modelling (ABM). We devise a guideline for the integration
of segmentations withing Agent-Based Models as well as how to evaluate the usefulness of
such models. | |
