ABM-Based Gaming simulation for policy making

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ITS832Chapter15.pdf

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