ABM-Based Gaming simulation for policy making
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ITS 832 CHAPTER 15 VISUAL DECISION SUPPORT FOR POLICY MAKING: ADVANCING POLICY ANALYSIS
WITH VISUALIZATION
INFORMATION TECHNOLOGY IN A GLOBAL ECONOMY
DR. JORDON SHAW
INTRODUCTION
• Background
• Approach
• Case Studies • Optimization
• Social Simulation
• Urban Planning
• Conclusion
BACKGROUND
• Assessing policy options for societal problems is difficult
• Decision making methods • Data driven
• Model driven
• Visual decision supports helps in evaluating model output
• Information visualization and visual analytics • Makes complex results accessible to many
• Policy analysis • Part of process aimed at solving societal problems
DATA VISUALIZATION
POLICY CYCLE
APPROACH
• Characterization of stakeholders • Policy makers • Policy analysts • Modeling experts • Domain experts
• Public stakeholders
• Bridging knowledge gaps • With information visualization (IV) • Cohesive view of model representation
VISUAL SUPPORT FOR POLICY ANALYSIS
APPROACH, CONT’D.
• Synergy effects of applying IV to policy analysis
• Communication - facilitated
• Complexity - reduced
• Subjectivity - reduced
• Validation - improved
• Transparency and reproducibility of results - increased
CASE STUDIES
• Optimization • Optimization of regional energy plans considering impacts
• Environmental
• Economical
• Social
• Social Simulation • Simulation of the impact of different policy instruments on the adoption of photovoltaic (PV) panels by
homeowners
• Urban planning • Integration of heterogenous data sources in planning activities
SUMMARY OF CASE STUDIES
CONCLUSION
• Current model output is often difficult to understand • Not accessible for non-specialists
• Information visualization (IV) • Makes model output more accessible
• This paper applies IV to policy analysis
• Contributions • Defined collaborations
• Identified hurdles
• Defined interface methodology