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

which should consists of below 4 modules. Chapter is attached.

• CHAPTER SUMMARY: Summarize chapter presented during the week. Identify the main point (as in “What’s your point?”), thesis, or conclusion of the key ideas presented in the chapter.
• SUPPORT: Do research outside of the book and demonstrate that you have in a very obvious way. This refers to research beyond the material presented in the textbook. Show something you have discovered from your own research. Be sure this is obvious and adds value beyond what is contained in the chapter itself. 
• EVALUATION: Apply the concepts from the appropriate chapter. Hint: Be sure to use specific terms and models directly from the textbook in analyzing the material presented and include the page in the citation.
• SOURCES: Include citations with your sources. Use APA style citations and references.

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