Based be designed in Vensim. Inputs of this

Based on
the literature review one could notice that there is not a research that
explores the policy space of the EU under the possible scenarios of Brexit.
Thus, this leads us to formulate the following research question based on the
problem statement:

“What financial policies could the EU adopt
during and after the Brexit to protect the Union’s economy?”

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structure the answer to this research question, sub-questions have to be
devised. Our objective is to identify the most suitable financial policies for
the EU in order to protect its economy. To reach the answer to this question
the following 6 sub-questions have to be answered first:

•    What are the implications of Brexit to the
EU and the EU under the 3 given scenarios?

•    What are the possible financial policies of
the EU and the UK to deal with the implication of the imminent Brexit?

•    What are the implications of each policy
implemented by one actor to the other one?

•    What can external instruments affect the
premises of the Brexit and how?

•    How are the implications of Brexit and the
Union’s real economy correlate?

•    How do the possible financial policies of
the EU and the UK affect their real economy?





Research Approach

combination of the above-mentioned questions is going to help our research to
answer the principal question.

In order to
answer the first 4 questions, a multi-actor analysis is going to take place.
This way we will be able to map objectives, means and criteria that our main
agents (the EU and the UK) have. Dependent on the literature and to already
existing research, an actor analysis, a means-end diagram and an objectives
tree are going to be developed for each actor of the system. This work will
help us develop a multi-actor system’s diagram, where also external actors’
actions are going to affect the criteria and the actors of our system.

To answer
the fifth question an SD (system dynamics) model is going to be designed in
Vensim. Inputs of this model would be published data and information (market
status, trading indices, GDPs etc.). This model will provide us with insight
regarding the impact of the financial sector and real economy by utilizing
already existing research such as agent correlations (Samitas, Polyzos, & Siriopoulos, 2017), short-term correlations
(Baker, Carreras,
Kirby, Meaning, & Pigott, 2016) and long-term
correlations (Ebell, Hurst,
& Warren, 2016) 
to describe the relations between the financial sector and the real
economy and also set up the premises to explore the impact of possible policies.

The last
sub-question refers to the impact of the possible policies to the real economy.
This last part will combine all the findings from previous steps of the
project. The means from the multi-actor analysis will act as the financial
policies, the objectives as Key Performance Indicators (KPIs) for our system
and the model as the basis of research for an exploratory modelling project. By
utilizing EMA Workbench, which is a Python library designed by researchers in
TU Delft for exploratory modelling, deep uncertainty is going to be introduced
in our research and by using the above-mentioned KPIs as optimizers we will
attempt to attain an answer to the principal question posed in this research