This document discusses using information visualization and visual analytics to help evaluate model output from policy analysis. It presents an approach that characterizes different stakeholders and bridges knowledge gaps between them using visualization. Several case studies are described that use visualization for policy optimization, social simulation, and urban planning. The conclusion states that visualization can make complex model output more accessible and help define collaborations and identify hurdles in applying visualization to policy analysis.

1. ITS 832
Chapter 15
Visual Decision Support for Policy Making:Advancing
Policy Analysis withVisualization
Information Technology in a GlobalEconomy
Introduction

2. · 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

3. 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

4. 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

5. CHAPTER 13
Management of Complex Systems: Toward Agent-Based
Gaming for Policy
Information Technology in a GlobalEconomy
INTRODUCTION· Simulating/Managing Social Complex
Phenomena
· Leadership and Management in Complex Systems
· Serious Gaming
· Agent-Based Games for Testing Leadership and Management

6. · Single and Multiplayer Settings
· Summary and conclusions
SIMULATING AND MANAGING SOCIAL COMPLEX
PHENOMENA· Study of how people interact
· Scale prohibits experimentation with real populations
· Agent-Base modeling (ABM)
· Networked agents
· Each agent is an individual· Interaction may modify agent
behavior
· Managing complex phenomena introduces complexity
· Techniques to manage turbulent situations vary
· Technique success depends on responding to agent behavior
· Which may change based on interactions
LEADERSHIP AND MANAGEMENT IN COMPLEX SYSTEMS
· Traditional leadership research
· Generally focuses on single period in time
· Doesn’t address dynamic relationships
· Timing of leadership principle application matters
· Primary leadership functions
· Instructional and regulatory
· Developmental
· Simulations offer promise to help model leadership in
complex systems

7. SERIOUS GAMING· Applying gaming techniques to real life
situations
· Flight simulators· Effective for evaluating complex
environments
· Player must interact with multiple actors and situations·
Currently used for side range of training applications
· Leadership use
· Deterministic – limited scope
· ABMs in serious gaming can help understand more complex
interactions
AGENT-BASED GAMES FORTESTING LEADERSHIP AND
MANAGEMENT· ABM games with autonomous AI population
· Test leadership style effectiveness
· Explore which styles work best in different situations
· Determine the best choice for a given scenario· Current state
of the art is more conceptual
· Advances needed in interfaces
· Need to allow users to interact with simulation
· Keep players engaged
BEHAVIOR IMPACTED BY MULTIPLE FACTORS
SINGLE AND MULTIPLAYER GAMES· AI may react poorly
to management input
· Simulating unexpected consequences of decisions
· Overactive AI may degrade realism· Players can dynamically
see how decisions affect others
· Early simulations allow for only single players

8. · Multiple real players adds more realistic interaction
· Players replace someAI
· Players interact with each other andAI
SUMMARY AND CONCLUSIONS
· ABM-based gaming can measure behaviors of players
· Supports experimentation in controlled environment
· Study leaderships and management in complex systems
· Focus
· Interaction with leadership
· Interaction with players as a result of leadership action