3. Data & Information Processing: The CI should facilitate efficient data and information fusion and analysis. The Internet has enabled the sharing of data in a simple and cost effective way, from the producers' side. Consumers of the data must still locate the appropriate data and deal with multiple incompatible data formats. The heterogeneity, volume and geographic distribution of data implies that social scientists, without the proper tools and use of database techniques, will be left to write custom programs that will tend to be less efficient than well crafted database and middleware methods. Unlike simulations of physical systems, models of socio-technical systems are usually data-intensive. Moreover, the data sets are being continually collected, refined, integrated and aligned to support ongoing analysis. Analogous to physical simulations, the output data is large and processing it is a computational challenge. More importantly, a POMDP model of socio-technical systems implies that a lot of data mining and analysis has to be done in concert with the simulation. This implies stringent computational requirements.
4. User Support: Development of appropriate analysis frameworks for users are needed, including user interfaces, high level formalisms to set up experiments, and visual and data analytics, which include methods for integrating heterogeneous databases to support multi-view visualization (e.g., disease spread in a geographic region and epidemic curves); methods for visualizing and analyzing large co-evolving coupled networks; and data mining and knowledge discovery tools to support analytical processes.
In addition, we need to develop environments and tools for simulation assisted decision support and consequence analysis. This includes methods for presenting results of analysis and simulations so as to avoid confirmation bias and framing effects, simulation based micro-economic analysis of decisions, and methods in risk analysis for ranking assets and understanding the inherent uncertainties in modeling such systems.
Figure 2 shows a conceptual architecture of the overall system that we are developing. Simfrastructure assumes the role of coordination between all the constituent components. This includes high resolution models for simulating large socio-technical systems, SimDM: a distributed data management environment, the underlying data and compute grids that provide low level data and compute services. Simfrastructure uses (tuple/java)-spaces to achieve the desired coordination goals. Currently, we have operating models for public health, commodity markets, transportation, integrated telecommunication networks, urban populations and built infrastructure. These models can all run on high performance computing platforms. We are currently extending them to work on grid-like architectures developed as a part of the NSF funded Teragrid initiative.
