How is the model-based policy design different from intuitive policy making? What are the techniques currently used

 Your readings in this unit, along with the two sources that you located on systems modeling for decision making in IT, evaluate and analyze the applicability of system simulations in policy-making. 
Address the following:

How is the model-based policy design different from intuitive policy making?
What are the techniques currently used to build models?
How does system models assist with decision making?

Your paper should be approximately 500 words and demonstrate proper APA formatting and style. You do not need to include a cover page or abstract, but be sure to include your name, assignment title, and page number in the running header of each page. Your paper should include a minimum of four references from your unit readings and assigned research; the sources should be appropriately cited throughout your paper and in your reference list. Use meaningful section headings to clarify the organization and readability of your paper.

ITS 832
Features andAdded Value of Simulation Models UsingDifferent Modelling
Approaches Supporting Policy-Making Information Technology in a Global Economy

• Simulation Models in policy-making – foundations
• eGovPoliNet • International multidisciplinary policy community in ICT
• Selected Modeling approaches • VirSim – Pandemic policy
• microSim – Swedish population
• MEL-C – Early Life-course
• Ocopomo’s Kosice Case – Energy policy
• SKIN – Dynamic systems component interaction

MODELING • Simulation model
• Smaller, less detailed, less complex (or all) • Computer software
• Approximates real-world behavior • Benefits
• Easier, simpler than monitoring reality • Possibly the only feasible way to “playout” a scenario
• Approaches discussed • System dynamics • Agent-based modeling (ABM) • Micro-simulation



• A Model to Support Pandemic Policy-Making • Simulates the spread of pandemic influenza
• Goal • Determine the optimal time and duration of school closings to affect
influenza spread
• System dynamicsmodel • Separates population into 3 segments
• Younger than 20 years old • 20 – 59 years old • 60 years old and older
• No environmental features considered • Only input data for Sweden

• Micro-simulation Model • Modeling the Swedish Population
• Goal • Determine how multiple behavior features affect influenza
• Micro-simulation model
• More granular than VirSim
• Focused only on Sweden
• Robustfor intended population

MEL-C • Modeling the EarlyLife-Course
• Knowledge-based inquiry tool With Intervention modeling (KIWI)
• Goal
• Identify social development milestones in early life that most affect later outcomes
• Health, nutrition, education, living conditions, etc.
• Micro-simulation model
• Generic applicability
• Limited by range of options
• Evidence-based
• Not very flexible when considering untested approaches

• Kosice self-governing region energy policy simulation • Goal
• Develop better energypolicy • And measure policy effectiveness
• House insulation and renewable energy sources
• ABM model • Modelis geographically anchored
• Difficult to apply to other regions • Many geographicfeatures
• Stakeholder engagement iskey

• Simulating Knowledge Dynamics in Innovation Networks • Goal
• Improve innovation throughinteractions • ABM model • Based on general market model • Agents areboth
• Sellers (providers) • Buyers (consumers)
• Agentsconsider dynamic interaction • Modify behavior to improve innovation • i.e. sell more or buy better

• Simulations allow multiple models to be investigated • Without real-worldconsequences
• Examined five models built on three approaches • VirSim – System dynamics
• MicroSim -Microsimulation
• MEL-C – Microsimulation
• Ocopomo’s Kosice Case -ABM
• Each approach has advantages and limitations


ITS 832
Chapter 5 From Building a Model to Adaptive Robust Decision MakingUsing Systems Modeling
Information Technology in a Global Economy

• Systems modeling • Focus on decision makingabilities
• Legacy System Dynamics (SD)modeling
• Recent innovations
• What the futureholds
• Examples

Systems modeling
• Dynamic complexity • Behavior evolves overtime
• Modeling methods • System Dynamics (CD) • Discrete Event Simulation(DES) • Multi-actorSystems Modeling (MAS) • Agent-based Modeling (ABM) • ComplexAdaptive Systems Modeling (CAS)
• Enhanced computing supports model based decision making • Modeling and simulation has become interdisciplinary
• Operation research, policy analysis, data analytics, machine learning, computer science

Legacy System DynamicsModeling
• 1950s – Jay W.Forrester
• Primary characteristics • Feedback effects – dependent on their own past
• Accumulation effects – building up intangibles
• Behavior ofa system is explained • Casual theory – model generates dynamic behavior
• Works well when • Complex system responds to feedback and accumulation

Recent Innovations
• Detailed list of individual innovations
• Deep uncertainty • Analysts do not know or cannot agree on
• Model
• Probability distributions of key features
• Value of alternative outcomes
• Two primary evolutions • Smarter methods (DataScience)
• Usability/accessibility advances

What theFuture Holds
• Better models
• More data (“BigData”)
• Social media
• Advanced capabilities for • Hybrid modeling
• Simultaneous modeling

Assessing the Risk, and Monitoring, of New Infectious
 Diseases
Simple systems model with deep uncertainty
Integrated Risk-CapabilityAnalysis Under Deep
 Uncertainty
System-of-systems approach
Policing Under DeepUncertainty
Smart model-based decision support system

• Modeling has long been used with complex systems
• Recent evolutions have advancedmodeling • Increase computing power
• Social media and Big data
• Sophisticated analytics
• Multi-method and hybrid approaches are now feasible
• Continued move intointerdisciplinary study • Advanced modeling for complex systems

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