ITS 832 Chapter 6 Features and Added Valueof Simulation Models Using Different Modelling Approaches Supporting Policy-Making Information Technology in a GlobalEconomy 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 •.

1. ITS 832
Chapter 6
Features and Added Valueof Simulation
Models Using Different Modelling
Approaches Supporting Policy-Making
Information Technology in a GlobalEconomy
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

2. • 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

3. • 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

4. • 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

5. • 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

6. • 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