Please read Chapter 6 in your textbook titled: Policy practice and digital science: Integrating complex systems, social simulation and public administration in policy research (Vol. 10) by Janssen, Wi

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 Michael Solomon 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 – 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 • 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