Policy-Making: Comparing Model-Based and Intuitive Policy Design

This report examines policy-making, focusing on the application of system simulations and the comparison between model-based and intuitive approaches. It investigates the differences between model-based policy design, which leverages data science and machine learning, and intuitive policy-making, highlighting the iterative nature of the latter. The report outlines current techniques for building models, including data analysis, sampling, and the development of smart decision support systems. It emphasizes how system models assist decision-making by providing visualizations and clarifying complex contexts, reducing uncertainty for policymakers. The report concludes by referencing key literature on the subject, providing a comprehensive overview of the role of models in modern policy design and implementation.

Running head: POLICY-MAKING
POLICY-MAKING
Name of Student
Name of University
Author’s Note

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Applicability of system simulations in policy-making
How is the model-based policy design different from intuitive policy making?
There had been numerous risks that had been identified that are associated with the
development of model as well as its implementation and hence consider the extent up to
which scientists could be held accountable for the purpose of communicating as well as
translating the model results to the policy makers such that weaknesses and the strengths of
the scientific evidence base along with the socioeconomic as well as the ethical impacts of
the biased predictions are understood properly (Zuiderwijk & Janssen, 2013). There are
numerous differences between model based policy design and intuitive policy making. Data
science as well as machine learning processes are presently developed into numerous
techniques that could provide inputs that are useful for the purpose of simulation models
along with building models. Methods as well as tools are developed for the purpose of
turning the intuitive policy making to model-based policy design. The model-based policy
design allows the stakeholders in collaborating between themselves with the help of
exchanging concerns as well as views regarding policy problem along with their solutions.
This process had helped in transforming the policy models for the purpose of simulation and
then transform the model based scenario to narrative scenarios for enabling simulation’s
understanding (Zuiderwijk, Helbig & Gil-García, 2014). The process of intuitive policy
making had been iterative because new scenario that had emerged from the numerous
discussions of the results. These results could be again used for the purpose of evaluation and
simulation.
What are the techniques currently used to build models?
Techniques that are used for the purpose of building models include the ones that
belong to the related disciplines for the purpose of analysing the data sets that are artificial,

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these data sets help in generating important insights of policy. One more technique that is
used include techniques of sampling as well as scenario discovery for the purpose of reducing
the obtained data sets in proportions that are manageable (Desouza, 2014). The next strategy
that is used include the development of smart decision support systems that are model based,
these are used for the purpose of dealing with numerous uncertain threats. Interfaces along
with the analytical capabilities that are advanced in nature besides easy as well as better
integration with the current ICT systems are necessary.
How does system models assist with decision making?
System models make use of visualizations of numerous trajectories, besides these the
critical indicators clarify numerous complex context of decisions for the policymakers. This
further helps in reducing the overall burden of uncertainty and the right to decision making is
provided to the policymakers instead of the researchers (Janssen, Wimmer & Deljoo 2015).
The system models help in executing the process in an effective manner besides intelligently
informed regarding the complexity of what is presently taking place and allow them to drive
the process of decision making even better. The internal as well as external evolutions break
through various silos of open up opportunities for carrying out social simulation along with
model based decision making.

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References
Desouza KC, (2014) Realizing the promise of big data: implementing big data projects. IBM
Center for the Business of Government, Washington, DC
Janssen M, Wimmer A. M & Deljoo A, (2015) Policy Practice and Digital Science.
Integrating Complex Systems, Social Simulation and Public Administration in Policy
Research
Zuiderwijk A, & Janssen M, (2013) A coordination theory perspective to improve the use of
open data in policy-making. Paper presented at the 12th conference on Electronic
Government (EGOV), Koblenz
Zuiderwijk A, Helbig N, Gil-García JR & Janssen M, (2014) Innovation through open data—
a review of the state-of-the-art and an emerging research agenda. J Theor Appl
Electron Commer Res 9(2):I–XIII.