Making Uncertainty Explicit
We explore how temporal models can distinguish plausible futures and explain what remains unknown about a changing system.
A future has more than one path
An observation rarely determines a single future. Different hidden conditions, measurement limits and possible actions can produce a range of outcomes.
We explore how temporal models can preserve that range instead of reducing every question to one confident estimate.
Uncertainty with a reason
Useful uncertainty should help explain what is unknown: incomplete observations, an unfamiliar regime or disagreement between plausible explanations of the system.
Testing uncertainty against outcomes
We would examine whether stated uncertainty matches observed outcomes, particularly under unfamiliar conditions. A useful result should distinguish a narrow estimate supported by evidence from confidence that a model cannot justify.