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Comparing adaptive management and real options approaches: slides and pre-print

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Adaptive management and real options approaches for sequential decisions making have undergone significant evolution over the last two decades. Both approaches are based on stochastic optimal control and Markov decision processes. They evolved independently from each other and their developments were motivated by different needs.

Adaptive management was specifically developed to handle decision problems with imperfect knowledge of the dynamics of the system, and is known as ‘learning by doing’. On the other hand, real options analysis was introduced specifically to value the flexibility to change actions over time in response to the evolution of uncertainty, and represents both optimal sequential decisions under uncertainty and a capital budgeting methodology. Because of these different purposes, different analytic and numerical methods were developed to solve these problems.

In our recent MODSIM paper (Chades et al, 2015), we review and compare the concepts, applications and recent advances in the numerical and analytic techniques in adaptive management and real options methodologies. A large body of knowledge accumulated in both fields makes a comprehensive review impractical in the context of this paper. Therefore, our review focuses on the most recent developments, with the purpose to identify potential areas of new developments that would address new challenges in the environmental decision area.

I. Chadès, T. Tarnopolskaya, S. Dunstall, J.Rhodes, and A.Tulloch (2015). A comparison of adaptive management and real options approaches for environmental decisions under uncertainty. In Weber, T., McPhee, M.J. and Anderssen, R.S. (eds) MODSIM2015, 21st International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, December 2015. ISBN: 978-0-9872143-5-5. (pre-print)

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Comparing adaptive management and real options approaches: slides and pre-print

  1. 1. 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
  2. 2. 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? Presentation title | Presenter name | Page 2 decisiont statet statet+1 statet+2 decisiont+1 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?
  3. 3. 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. Presentation title | Presenter name | Page 3 decisiont Statet Statet+1 Statet+2 decisiont+1 Good? Bad? Good? Bad?
  4. 4. 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 decisiont Lifet Lifet+1 Lifet+2 decisiont+1 Don’t knowt+1Don’t knowt
  5. 5. Adaptive management is “learning by doing” Presentation title | Presenter name | Page 5 Decisions are selected to achieve a management objective while simultaneously gaining information to improve future management outcomes. manage monitor learn objective
  6. 6. 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 Presentation title | Presenter name | Page 6 decisiont Lifet Lifet+1 Lifet+2 decisiont+1 Don’t knowt+1Don’t knowt
  7. 7. Presentation title | Presenter name | Page 7 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
  8. 8. Presentation title | Presenter name | Page 8 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.
  9. 9. Two types of adaptive management principle: active and passive Presentation title | Presenter name | Page 9 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)
  10. 10. 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
  11. 11. 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 |
  12. 12. 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 |
  13. 13. 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
  14. 14. 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 |
  15. 15. 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 |
  16. 16. Presentation title | Presenter name | Page 16 Adaptive Management Real Options Attitude to uncertainty Reduce structural uncertainty to maximise management outcomes. Harness uncertainty to add value though optimal management.
  17. 17. 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).
  18. 18. 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
  19. 19. 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
  20. 20. 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
  21. 21. 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
  22. 22. 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.)

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