Tuesday, 16:15 - 16:40 h, Room: MA 376


Gwen Spencer
Fragmenting and vaccinating graphs over time and subject to uncertainty: Developing techniques for wildfire and invasive species containment

Coauthor: David Shmoys


Decisions about the containment of harmful processes that spread across landscapes (for example, wildfire and invasive species) often must be made under uncertainty and as the system evolves in time. Not all resources are available immediately and containment efforts may fail to prevent spread. The valuable probabilistic predictions produced by ecologists and foresters have been under-utilized because of the difficulty of optimizing when stochastic features and spatial connectedness (or, in this case, disconnectedness) interact.

I will introduce several simple models in graphs that generalize existing work in the CS theory literature and explain provably-good algorithmic results for several settings. These models capture qualitative tradeoffs with important implications for sustainable management. How should resources for wildfire containment be divided across preventive fuel removals and real-time fire suppression efforts, and how can these deployments be coordinated to maximum advantage? If attempts to block invasive species spread are not perfectly reliable, but redundancy is costly, where should managers concentrate their resources?


Talk 3 of the invited session Tue.3.MA 376
"Approximation algorithms for stochastic combinatorial optimization" [...]
Cluster 22
"Stochastic optimization" [...]


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