Identify and discuss a theory based modelling approach for developing polcies in information technology. 
 As indicated above, select one example and describe how data visualization techniques can be used by a practitioner or analyst in developing a policy to solve a specific problem. Provide a thoughtful research-based analysis and include at least two references to support your thoughts.
 

ITS 832

CHAPTER6

Features andAddedValue of Simulation
Models UsingDifferent Modelling

Approaches SupportingPolicy-Making
InformationTechnology in aGlobal Economy

INTRODUCTION

• Simulation Models in policy-making – foundations

• eGovPoliNet
• Internationalmultidisciplinarypolicycommunity in ICT

• Selected Modelingapproaches
• VirSim – Pandemic policy

• microSim – Swedish population

• MEL-C – Early Life-course

• Ocopomo’sKosiceCase – Energy policy

• SKIN – Dynamic systems component interaction

FOUNDATIONS OFSIMULATION

MODELING
• Simulation model

• Smaller, less detailed, less complex (or all)
• Computer software

• Approximates real-worldbehavior
• 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

STEPS INDEVELOPINGSIMULATION

MODELS

SIMULATION MODELSEXAMINED

VIRSIM

• A Model to Support PandemicPolicy-Making
• Simulates thespread of pandemic influenza

• Goal

• Determine theoptimal time andduration of school closings to affect

influenza

spread

• System dynamicsmodel
• Separates population into3segments

• Younger than20 yearsold
• 20 – 59 years old
• 60 years old and older

• No environmental features considered
• Only inputdata forSweden

MICROSIM

• Micro-simulation Model
• Modeling theSwedish Population

• Goal
• Determine how multiplebehavior features affect influenza

spread

• Micro-simulation model

• More granular thanVirSim

• Focused only on Sweden

• Robustfor intended population

MEL-C
• Modeling the EarlyLife-Course

• Knowledge-based inquiry toolWith Intervention
modeling (KIWI)

• Goal

• Identify social development milestones in early life
that most affect lateroutcomes

• Health,nutrition, education, living conditions, etc.

• Micro-simulationmodel

• Genericapplicability

• Limitedby rangeofoptions

• Evidence-based

• Not very flexible when considering untested
approaches

OCOPOMO’S KOSICECASE

• Kosice self-governing region energy policy simulation
• Goal

• Develop better energypolicy
• And measurepolicy effectiveness

• House insulation and renewable energy sources

• ABM model
• Modelis geographically anchored

• Difficult to apply to other regions
• Many geographicfeatures

• Stakeholder engagement iskey

SKIN

• 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.sellmore or buy better

SUMMARY

• 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’sKosiceCase -ABM

• SKIN – ABM

• Each approach has advantages and limitations