Five simulation models and the main characteristics of modelling paradigms are discussed in Chapter 6dentify and discuss a theory based modelling approach for developing polcies in information technology.

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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

influenzaspread

• 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