A comparison of adaptive management and
real options approaches for environmental
decision making
iadine Chadès, T. Tarnopolskaya, S. Dunstall, J. Rhodes, and A. Tulloch
http://iadine-chades.org/
@iadinec

There are many things we don’t
know: uncertainty.
Urgency and planning: decide
today but think about
tomorrow.
Don’t spend too much!
We want our decisions to
achieve something and do it
well.
Making decisions is difficult. Why?
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How do we know if we are making good decisions?
We need 1) an objective 2) to assess (monitor) whether our
decisions are getting us closer to our objective.
Good?
Bad?
Good?
Bad?

Adaptive management (AM) & real options (RO)
Both approaches are based on
stochastic optimal control and
Markov decision processes.
AM accounts for a small
number of hidden variables.
RO deals with high-dimensional
problems with multiple
stochastic risk factors.
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Good?
Bad?
Good?
Bad?

Adaptive Management is ‘learning by doing’
Adaptive management is an iterative process of reducing
uncertainty through time by learning by doing and monitoring
(Walters and Hilborn, 1978).
Principal tool for conserving endangered species under global
change.
CSIRO. POMDP the Swiss army knife of the adaptive ecologist
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Don’t knowt+1Don’t knowt

Adaptive management is “learning by doing”
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Decisions are selected to achieve a management objective while
simultaneously gaining information to improve future
management outcomes.
manage
monitor
learn
objective

Specifically tailored to
account for structural
uncertainty:
1) Parameter: survival,
growth, probability of
success
2) Model: competing
scenarios, density
dependence
Adaptive management is making decisions
when we don’t know the system dynamics
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R. Bellman
Stochastic dynamic
programming
manage
monitor
learn
objective
Bayes theorem
Active adaptive management calculates a plan that
provides the best actions to implement, given our
current knowledge … and what we will learn in the
future

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R. Bellman
Stochastic dynamic
programming
manage
monitor
learn
objective
Bayes theorem
Passive adaptive management calculates a plan that
provides the best actions to implement, given our
current knowledge … Learning occurs independently.

Two types of adaptive management
principle: active and passive
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Active:
Includes future learning opportunities when calculating best
decisions.
Solutions are optimal, but difficult to calculate!
Active AM = POMDP (Chades et al, AAAI 2012)
Passive:
Heuristics approaches developed because finding the optimal
solution might be impossible.
Assumes “certainty equivalence”. No formal guarantee of
performance.
Techniques: Stochastic Dynamic Programming (MDP)

Where and when should we invest in sea level rise mitigation to
protect migratory shorebirds under uncertainty?
2) Adaptive management
(Nicol et al, 2013, IJCAI) (Nicol
et al, Proc B 2015).
Learn as we manage:
birds saved +56.2%
1) Bottleneck index
(Iwamura et al, 2013, Proc. B)
birds saved +31%
Uncertain future
SLR scenarios and
consequences
Where and when
to protect?
East Asian
Australasian flyway

Real Options Concept
Flexibility to adapt to changing circumstances is crucial for
successful business operation under uncertainty
Uncertainty creates opportunity which can be harnessed,
using project’s flexibility, to improve project’s
performance
Concept came from financial risk area
Real option: right (without obligation) to undertake a certain
business activity in the future
11 |

Dual purpose of Real Options
Real Options Valuation / Analysis:
1. Dynamic project valuation under uncertainty.
2. Flexible management under uncertainty
(optimisation of sequential decisions under
uncertainty)
12 |

Real Options Value vs NPV
Value of flexibility
Net Present Value (NPV) – a static, passive project valuation
approach. It assumes that decisions are taken today and will not be
changed
Real Options Valuation is based on optimising both decisions
(options) and their timing in the future under uncertainty
Optimal flexible management in the face of uncertainty adds value
13 |
Value of flexibility
Project value NPV Option Value

Examples of Real Options
 to delay, temporarily stop or completely abandon the project in
the future
 to expand or contract the project in the future
 to accelerate/decelerate the project in the future
A decision to stop timber harvesting when a woodland caribou
population becomes threatened with extinction (Morgan et al.,
2007)
Current applications of real options typically consider a single real
option under a single stochastic risk factor. Do not require
advanced numerical techniques.
Future applications for adapting to climate change: broader range of
stochastic processes and new techniques (Mezey and Conrad,
2010)
14 |

Regression Monte Carlo Approach
Regression Monte Carlo: a combination of Monte Carlo simulations
with Bellman Optimality Principle (Stochastic dynamic programming)
Introduced in the finance industry by Longstaff & Schwarz (2001)
under the name Least Squares Monte Carlo (LSMC).
LSMC has drawbacks (stability problems, memory complexity) that
prevent its use for high-dimensional problems:
New advanced regression Monte Carlo methods are suitable for
complex high-dimensional real options problems:
New regression Monte Carlo Methods for high-dimensional real options
problems in minerals industry, by Langrene, Tarnopolskaya, Chen, Zhu and
Cooksey – MODSIM2015, Session E6 Real Options
15 |

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Adaptive Management Real Options
Attitude to
uncertainty
Reduce structural uncertainty to
maximise management outcomes.
Harness uncertainty to add value
though optimal management.

Presentation title | Presenter name | Page 17
Adaptive Management Real Options
Attitude to
uncertainty
Reduce structural uncertainty to
maximise management outcomes.
Harness uncertainty to add value
though optimal management.
Mathematical
model
Markov property, MDP or POMDP
with state space augmented with
sufficient statistics. Probability
transitions pre-calculated (not
simulated).
Markov property, optimal stopping and
optimal stochastic switching (MDP).
Solved using regression Monte Carlo
approach (simulation based).

Presentation title | Presenter name | Page 18
Adaptive Management Real Options
Attitude to
uncertainty
Reduce structural uncertainty to
maximise management outcomes.
Harness uncertainty to add value
though optimal management.
Mathematical
model
Markov property, MDP or POMDP
with state space augmented with
sufficient statistics. Probability
transitions pre-calculated (not
simulated).
Markov property, optimal stopping and
optimal stochastic switching (MDP).
Solved using regression Monte Carlo
approach (simulation based).
Role of
learning
Learning is part of the decision
process
Real options analysis share similarities
with passive adaptive management

Presentation title | Presenter name | Page 19
Adaptive Management Real Options
Attitude to
uncertainty
Reduce structural uncertainty to
maximise management outcomes.
Harness uncertainty to add value
though optimal management.
Mathematical
model
Markov property, MDP or POMDP
with state space augmented with
sufficient statistics. Probability
transitions pre-calculated (not
simulated).
Markov property, optimal stopping and
optimal stochastic switching (MDP).
Solved using regression Monte Carlo
approach (simulation based).
Role of
learning
Learning is part of the decision
process
Real options analysis share similarities
with passive adaptive management
Purpose Optimal management through
learning
Capital budgeting and optimal flexible
management

Presentation title | Presenter name | Page 20
Adaptive Management Real Options
Attitude to
uncertainty
Reduce structural uncertainty to
maximise management outcomes.
Harness uncertainty to add value
though optimal management.
Mathematical
model
Markov property, MDP or POMDP
with state space augmented with
sufficient statistics. Probability
transitions pre-calculated (not
simulated).
Markov property, optimal stopping and
optimal stochastic switching (MDP).
Solved using regression Monte Carlo
approach (simulation based).
Role of
learning
Learning is part of the decision
process
Real options analysis share similarities
with passive adaptive management
Purpose Optimal management through
learning
Capital budgeting and optimal flexible
management
Features of
advanced
methods
Small number of risk factors; non
stationary dynamics; large states
space; small action space; imperfect
detection.
Multiple stochastic risk factors; non
stationary dynamics; large state and
action space

Presentation title | Presenter name | Page 21
Adaptive Management Real Options
Attitude to
uncertainty
Reduce structural uncertainty to
maximise management outcomes.
Harness uncertainty to add value
though optimal management.
Mathematical
model
Markov property, MDP or POMDP
with state space augmented with
sufficient statistics. Probability
transitions pre-calculated (not
simulated).
Markov property, optimal stopping and
optimal stochastic switching (MDP).
Solved using regression Monte Carlo
approach (simulation based).
Role of
learning
Learning is part of the decision
process
Real options analysis share similarities
with passive adaptive management
Purpose Optimal management through
learning
Capital budgeting and optimal flexible
management
Features of
advanced
methods
Small number of risk factors; non
stationary dynamics; large states
space; small action space; imperfect
detection.
Multiple stochastic risk factors; non
stationary dynamics; large state and
action space
Problem size 800,000 discrete states, less than 10
actions, 1 hidden stochastic risk
factor and infinite time horizon
Continuous state space sampled via
Monte Carlo simu (up to 1,000,000
realizations), up to 50 actions; up to 10
stochastic risk factors; long time
horizons

Thank you
Dr Iadine Chadès
e iadine.chades@csiro.au
http://iadine-chades.org/
Twitter: @iadinec
Thanks to all my collaborators
This work has been supported by a Julius Career
award (I.C.)