  We propose the concept of Speculative Execution for Visual Analytics and
discuss its effectiveness for model exploration and optimization. Speculative
Execution enables the automatic generation of alternative, competing model
configurations that do not alter the current model state unless explicitly
confirmed by the user. These alternatives are computed based on either user
interactions or model quality measures and can be explored using
delta-visualizations. By automatically proposing modeling alternatives, systems
employing Speculative Execution can shorten the gap between users and models,
reduce the confirmation bias and speed up optimization processes. In this
paper, we have assembled five application scenarios showcasing the potential of
Speculative Execution, as well as a potential for further research.
