Features and Added Value of Simulation Models Using Different Modelling Approaches Supporting Policy-Making here

ITS 832 Chapter 6

Features and Added Value of Simulation Models Using Different Modelling

Approaches Supporting Policy-Making Information Technology in a Global Economy

Professor Miguel Buleje

Introduction • Simulation Models in policy-making – foundations

• eGovPoliNet • GLOBAL multidisciplinary policy modeling community

in Information and Communication Technology (ICT).

• Brings researcher together from various disciplines to share ideas, discuss knowledge assets, and developing joint research findings.

• 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

• Definition: smaller, less detailed, less complex (or all) • Can be use to better understand real life processes and

relationships.

• Computer software • Approximates real-world behavior

• Benefits • Easier, simpler than monitoring reality • Possibly the only feasible way to “play out” a scenario

• Approaches discussed /paradigms • 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 – 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 thanVirSim

• 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

• Agent Based Modeling (ABM) model • Model is geographically focused

• Difficult to apply to other regions • Many geographic features

• Stakeholder engagement is key

SKIN

• Simulating Knowledge Dynamics in Innovation Networks (SKIN) • Goal

• Improve innovation through interactions

• Agent Based Modeling (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 - Agent Based Modeling (ABM) SKIN – Agent Based Modeling (ABM)

• Each approach has advantages and limitations • Opportunity for future research as in this area, to unify the

different modeling theories presented into ONE modeling platform

• Simulations allow multiple models to be investigated • Without real-world consequences