Name
Abstract | ||
|---|---|---|
Agent-based modeling has become a key technique for modeling and simulating dynamic, complicated behaviors in the social and political sciences. Although many robust toolkits for developing and running these simulations exist, systems that support analysis of their results are few and tend to be overly general. So, social scientists have had difficulty interpreting the results of their increasingly complex simulations. To help bridge this gap between data generation and interpretation, researchers collaborated with political science analysts to design two tools for interactive data exploration and domain-specific data analysis. Testing by the analysts validated that these tools provided an efficient framework to explore individual trajectories and the relationships between variables. The tools also supported hypothesis generation by enabling analysts to group simulations according to multidimensional similarity and drill down to investigate further. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/MCG.2011.90 | IEEE Computer Graphics and Applications |
Keywords | Field | DocType |
software agents,data models,computer graphic,modeling and simulation,data analysis,computer model,data visualization,visual analytics,data visualisation,political science,computational modeling,computer graphics,politics,data model | Data science,Computer vision,Data modeling,Data visualization,Data exploration,Visualization,Computer science,Software agent,Drill down,Artificial intelligence,Computer graphics,Test data generation | Journal |
Volume | Issue | ISSN |
32 | 1 | 0272-1716 |
Citations | PageRank | References |
4 | 0.56 | 4 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| R. Jordan Crouser | 1 | 189 | 15.89 |
| Daniel Kee | 2 | 4 | 0.56 |
| Dong Hyun Jeong | 3 | 269 | 20.38 |
| Remco Chang | 4 | 983 | 64.96 |