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ITS 832 CHAPTER 6 FEATURES AND ADDED VALUE OF SIMULATION MODELS USING DIFFERENT MODELLING APPROACHES SUPPORTING POLICY-MAKING

INFORMATION TECHNOLOGY IN A GLOBAL ECONOMY

DR. JORDON SHAW

INTRODUCTION

• 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

FOUNDATIONS OF SIMULATION 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 “play out” a scenario

• Approaches discussed • System dynamics • Agent-based modeling (ABM) • Micro-simulation

STEPS IN DEVELOPING SIMULATION MODELS

SIMULATION MODELS EXAMINED

VIRSIM

• 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 dynamics model • Separates population into 3 segments

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

• No environmental features considered • Only input data for Sweden

MICROSIM

• Micro-simulation Model • Modeling the Swedish Population

• Goal • Determine how multiple behavior features affect influenza spread

• Micro-simulation model • More granular than VirSim • Focused only on Sweden • Robust for intended population

MEL-C

• Modeling the Early Life-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

OCOPOMO’S KOSICE CASE

• Kosice self-governing region energy policy simulation • Goal

• Develop better energy policy • And measure policy effectiveness

• House insulation and renewable energy sources • ABM model • Model is geographically anchored

• Difficult to apply to other regions • Many geographic features

• Stakeholder engagement is key

SKIN

• Simulating Knowledge Dynamics in Innovation Networks • Goal

• Improve innovation through interactions • ABM model • Based on general market model • Agents are both

• Sellers (providers) • Buyers (consumers)

• Agents consider dynamic interaction • Modify behavior to improve innovation • i.e. sell more or buy better

SUMMARY

• Examined five models built on three approaches • VirSim – System dynamics • MicroSim - Microsimulation • MEL-C - Microsimulation • Ocopomo’s Kosice Case - ABM • SKIN–ABM

• Each approach has advantages and limitations • Simulations allow multiple models to be investigated

• Without real-world consequences