Thinking about geospatial use, there are already plenty of tools that model complex systems and allow realtime or near-realtime exploration of cause/effect - for example a tool called ArcFM that I have worked with allows modelling of utility networks and as a very simple example you could disconnect part of the system and see how the rest of the system is impacted. This type of system implies a significant up front and ongoing cost, and these days is usually based on one of the established COTS solutions. I think Improbable would find this a difficult niche to sell into.
I can see that a utility network could be just one part of a larger system i.e. a rich city model with all key systems modelled, such that you could simulate a tsunami and see what power networks go down, how this affects rail links, where that strands people in the city, what evacuation they may need, etc. etc. The trouble with this is that the investment in creating such a model may vastly outweigh cheaper simulation that provides a good enough result, and that you may end up with such complex interactions that you can no longer get results in a timely manner.
SDSS's find application with NP-complete problems, and work around this NP-completeness in various ways to find solutions that are good enough. Instead of modelling all possible outcomes, single parameters are varied to view their impact, and in this way the nature of the problem can be ascertained. I can see that this methodology could work within SpatialOS, with the system providing a container for vastly scaled up models.
Further edit: I wonder what coordinate systems are available. If they are serious about geospatial they may have some work to do.
I can see that a utility network could be just one part of a larger system i.e. a rich city model with all key systems modelled, such that you could simulate a tsunami and see what power networks go down, how this affects rail links, where that strands people in the city, what evacuation they may need, etc. etc. The trouble with this is that the investment in creating such a model may vastly outweigh cheaper simulation that provides a good enough result, and that you may end up with such complex interactions that you can no longer get results in a timely manner.
Thinking about global scale, I was reminded of Buckminster Fuller's World Game: https://en.wikipedia.org/wiki/World_Game
I'm open-minded yet sceptical. But what do I know, I haven't just raised $500m!
Edit: it's just occurred to me that what SpatialOS are talking about sounds something like a Spatial Decision Support System: https://en.wikipedia.org/wiki/Spatial_decision_support_syste...
SDSS's find application with NP-complete problems, and work around this NP-completeness in various ways to find solutions that are good enough. Instead of modelling all possible outcomes, single parameters are varied to view their impact, and in this way the nature of the problem can be ascertained. I can see that this methodology could work within SpatialOS, with the system providing a container for vastly scaled up models.
Further edit: I wonder what coordinate systems are available. If they are serious about geospatial they may have some work to do.