Theoretical Ecology

, Volume 10, Issue 1, pp 1–20

Optimization methods to solve adaptive management problems

  • Iadine Chadès
  • Sam Nicol
  • Tracy M. Rout
  • Martin Péron
  • Yann Dujardin
  • Jean-Baptiste Pichancourt
  • Alan Hastings
  • Cindy E. Hauser
REVIEW PAPER

DOI: 10.1007/s12080-016-0313-0

Cite this article as:
Chadès, I., Nicol, S., Rout, T.M. et al. Theor Ecol (2017) 10: 1. doi:10.1007/s12080-016-0313-0

Abstract

Determining the best management actions is challenging when critical information is missing. However, urgency and limited resources require that decisions must be made despite this uncertainty. The best practice method for managing uncertain systems is adaptive management, or learning by doing. Adaptive management problems can be solved optimally using decision-theoretic methods; the challenge for these methods is to represent current and future knowledge using easy-to-optimize representations. Significant methodological advances have been made since the seminal adaptive management work was published in the 1980s, but despite recent advances, guidance for implementing these approaches has been piecemeal and study-specific. There is a need to collate and summarize new work. Here, we classify methods and update the literature with the latest optimal or near-optimal approaches for solving adaptive management problems. We review three mathematical concepts required to solve adaptive management problems: Markov decision processes, sufficient statistics, and Bayes’ theorem. We provide a decision tree to determine whether adaptive management is appropriate and then group adaptive management approaches based on whether they learn only from the past (passive) or anticipate future learning (active). We discuss the assumptions made when using existing models and provide solution algorithms for each approach. Finally, we propose new areas of development that could inspire future research. For a long time, limited by the efficiency of the solution methods, recent techniques to efficiently solve partially observable decision problems now allow us to solve more realistic adaptive management problems such as imperfect detection and non-stationarity in systems.

Keywords

Adaptive management Markov decision process MDP Partially observable Markov decision process POMDP Stochastic dynamic programming Value of information Hidden Markov models Natural resource management Conservation 

Supplementary material

12080_2016_313_MOESM1_ESM.docx (27 kb)
Fig. S1(DOCX 27 kb)
12080_2016_313_MOESM2_ESM.docx (24 kb)
Table S1(DOCX 23 kb)
12080_2016_313_MOESM3_ESM.docx (35 kb)
Table S2(DOCX 34 kb)

Funding information

Funder NameGrant NumberFunding Note
Commonwealth Scientific and Industrial Research Organisation
  • Julius Career Award
Australian Research Council
  • DP110101499
National Environmental Research Program Environmental Decisions Hub

    Copyright information

    © Springer Science+Business Media Dordrecht 2016

    Authors and Affiliations

    • Iadine Chadès
      • 1
    • Sam Nicol
      • 1
    • Tracy M. Rout
      • 2
    • Martin Péron
      • 1
      • 3
    • Yann Dujardin
      • 1
    • Jean-Baptiste Pichancourt
      • 1
    • Alan Hastings
      • 4
    • Cindy E. Hauser
      • 2
    1. 1.CSIROBrisbaneAustralia
    2. 2.School of BioSciencesUniversity of MelbourneParkville VicAustralia
    3. 3.School of Mathematical SciencesQueensland University of TechnologyBrisbaneAustralia
    4. 4.Department of Environmental Science and PolicyUniversity of CaliforniaDavisUSA

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