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Dynamic Resource Allocation in Conservation Planning
1California Institute of Technology Center for the Mathematics of Information
Daniel Golovin Andreas Krause
Beth Gardner Sarah Converse Steve Morey
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Ecological Reserve Design
How should we select land for conservationto protect rare & endangered species?
Case Study: Planned Reserve in Washington State
Mazama pocket gopherstreaked horned lark Taylor’s checkerspot
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Land parcel details About 5,300 parcelssoil types, vegetation, slopeconservation cost
Problem Ingredients
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Land parcel details Geography: Roads, Rivers, etc
Problem Ingredients
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Land parcel details Geography: Roads, Rivers, etc Model of Species’ Population
DynamicsReproduction, Colonization, Predation,
Disease, Famine, Harsh Weather, …
Problem Ingredients
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Time t+1
Population Dynamics
EnvironmentalConditions (Markovian)
Our Choices
Protected Parcels
Time t
Modeled using a Dynamic Bayesian
Network
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Time t+1
Population Dynamics
EnvironmentalConditions (Markovian)
Our Choices
Protected Parcels
Time t
Modeled using a Dynamic Bayesian
Network
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Model Paramters From the ecology literature, or Elicited from panels of domain experts
An
nu
al
Patc
h S
urv
ival
Pro
bab
ilit
y
Patch Size (Acres)
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From Parcels to Patches
So we group parcels into larger patches.
Patch 1 Patch 2
Most parcels are too small to sustain a gopher family
We assume no colonization between patches,and model only colonization within patches.We optimize over (sets of) patches.
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The Objective Function
In practice, use sample average approximation
Selected patches R
Pr[alive after 50yrs]
0.8
0.7
0.5
f(R)= 2.0 (Expected # alive)
Choose R to maximize species persistence
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“Static” Conservation Planning
Select a reserve of maximum utility, subject to budget constraint
NP-hard
But f is submodular We can find a near-optimal solution
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Structure in Reserve Design
Diminishing returns: helps more in case A
than in case B
Utility function f is submodular:
A B
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Theorem [Sviridenko ‘04]: We can efficiently obtain reserve R such that
Solving the “Static” Conservation Planning Problem
More efficient algorithm with slightly weaker guarantees [Leskovec et al. ‘07]
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Selected patches are very diverse
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Results: “Static” Planning
• Can get large gain through optimization
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Time t+1
Build up reserve over time At each time step t, the budget Bt
and the set Vt of available parcels may change
Need to dynamically allocate budget tomaximize value of final reserve
Dynamic Conservation Planning
Time t
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Opportunistic Allocation forDynamic Conservation
In each time step: Available parcels and budget appear Opportunistically choose near-optimal allocation
Theorem: We get at least 38.7% of the value of the best clairvoyant algorithm*
* Even under adversarial selection of available parcels & budgets.
Time t=1Time t=2
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• Large gain from optimization & dynamic selection
Results: Dynamic Planning
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Dynamic Planning w/Failures Parcel selection may fail
Purchase recommendations unsuccessfulPatches may turn out to be uninhabitable
Can adaptively replan, based on observations
Opportunistic allocation still near-optimalProof uses adaptive submodularity
[Golovin & Krause ‘10]
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Dynamic Planning w/Failures
Failures increase the benefit of adaptivity
50% failure rate
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Related Work
Existing softwareMarxan [Ball, Possingham & Watts ‘09]Zonation [Moilanen and Kujala ‘08]General purpose softwareNo population dynamics modeling, no
guarantees
Sheldon et al. ‘10Models non-submodular population
dynamicsOnly considers static problemRelies on mixed integer programming
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Conclusions Reserve design: prototypical
optimization problem in CompSustAI
Large scale, partial observability, uncertainty,
long-term planning, …
Exploit structure near-optimal solutions
General competitiveness result about opportunistic allocation with submodularity