paper

ITS 832

CHAPTER 6

Features andAdded Value of Simulation Models UsingDifferent Modelling

Approaches Supporting Policy-Making Information Technology in a Global Economy

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 “playout” a scenario

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

STEPS IN DEVELOPING SIMULATION

MODELS

SIMULATION MODELSEXAMINED

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 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

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

• 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

OCOPOMO’S KOSICECASE

• 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

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. sell more 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’s Kosice Case -ABM

• SKIN – ABM

• Each approach has advantages and limitations