Abstract
To move toward environmental sustainability, we propose that a computational approach may be needed due to the complexity of resource production and consumption. While digital sensors and predictive simulation has the potential to help us to minimize resource consumption, the indirect relation between cause and effect in complex systems complicates decision making. To address this issue, we examine the central role that data-driven decision making could play in critical domains such as sustainability or medical treatment.
We developed systems for exploratory data analysis and data visualization to support hypothesis generation, hypothesis testing, and decision making. In addition to sensors in buildings, infrastructure, or the environment, we also propose the instrumentation of user interfaces to help measure performance in decision making applications. We show the benefits of applying principles of data analysis and instructional interface design, to both simulation systems and decision support interfaces. We hope that projects such as these will help people to understand the link between their choices and the consequences of their decisions.
We developed systems for exploratory data analysis and data visualization to support hypothesis generation, hypothesis testing, and decision making. In addition to sensors in buildings, infrastructure, or the environment, we also propose the instrumentation of user interfaces to help measure performance in decision making applications. We show the benefits of applying principles of data analysis and instructional interface design, to both simulation systems and decision support interfaces. We hope that projects such as these will help people to understand the link between their choices and the consequences of their decisions.
| Originalsprog | Engelsk |
|---|
| Forlag | Department of Computer Science, Faculty of Science, University of Copenhagen |
|---|---|
| Antal sider | 144 |
| Status | Udgivet - 2015 |