Probability, possibility and evidence: approaches to consider risk and uncertainty in forestry decision analysis

Abstract Uncertainty is an important issue in the support of any forestry decision. Forestry decision making today typically involves objectives and information concerning ecological, economic and social issues. The consequences of alternative forest management programmes might be hard to assess, and predictions and assessments always include uncertainty. Forestry decisions also often concern large areas, long time horizons and multiple stakeholders, which further complicates forest management planning and increases uncertainty involved in it. This paper deals with different definitions and classifications of uncertainty, sources of uncertainty, and theories and methodologies presented to deal with uncertainty. The aim is to provide readers with an overview of alternative approach for coping with uncertainty, especially from the viewpoint of forestry and natural resource management applications. Generally taken, there are two main conventional approaches, namely classical (frequentist) and Bayesian probability theory. These lead to either classical or Bayesian decision theory, respectively. In addition, uncertainty can be dealt with, for instance, using the fuzzy set theory. This theory mostly deals with uncertainty due to the ambiguity of concepts. So far, in decision support tools, probability and fuzzy set theory are the most common approaches. However, the possibility theory and the evidence theory, for instance, can also be relied upon when managing uncertainty. These theories deal with subjective beliefs and expert judgements. They are able to deal with partial information and pure ignorance. The counterparts to the classical decision rules based on these theories are presented, as well as some decision support methods designed using the approaches presented. Because of the manifold sources of uncertainty, all these approaches can have application in the support of forestry decisions.

[1]  Ilan Vertinsky,et al.  Carbon sequestration and land management under uncertainty , 2001, Eur. J. Oper. Res..

[2]  Didier Dubois,et al.  Possibility Theory - An Approach to Computerized Processing of Uncertainty , 1988 .

[3]  David Draper,et al.  Assessment and Propagation of Model Uncertainty , 2011 .

[4]  Harry T. Valentine,et al.  Assessing Uncertainty in a Stand Growth Model by Bayesian Synthesis , 1999, Forest Science.

[5]  M. Beynon,et al.  The Dempster-Shafer theory of evidence: an alternative approach to multicriteria decision modelling , 2000 .

[6]  Erwin H. Bulte,et al.  Uncertainty and forest land use allocation in British Columbia: vague priorities and imprecise coefficients. , 1997 .

[7]  G. A. Mendoza,et al.  A fuzzy analytic hierarchy process for assessing biodiversity conservation , 2001 .

[8]  Ilan Vertinsky,et al.  Framework for the analysis of risks in forest management and silvicultural investments. , 1990 .

[9]  B. Marcot,et al.  Using Bayesian belief networks to evaluate fish and wildlife population viability under land management alternatives from an environmental impact statement , 2001 .

[10]  H. Zimmermann Fuzzy programming and linear programming with several objective functions , 1978 .

[11]  Pekka Korhonen,et al.  Interactive, multiobjective programming for forest resources management , 1994 .

[12]  Hans-Jürgen Zimmermann,et al.  An application-oriented view of modeling uncertainty , 2000, Eur. J. Oper. Res..

[13]  David Lindley Scoring rules and the inevitability of probability , 1982 .

[14]  Annika Kangas,et al.  Methods for assessing uncertainty of growth and yield predictions , 1999 .

[15]  A. Weintraub,et al.  Analysis of uncertainty of futue timber yields in forest management , 1995 .

[16]  George J. Klir,et al.  Types and Measures of Uncertainty , 1997 .

[17]  Kaisa Miettinen,et al.  Ordinal criteria in stochastic multicriteria acceptability analysis (SMAA) , 2003, Eur. J. Oper. Res..

[18]  David E. Bell,et al.  Regret in Decision Making under Uncertainty , 1982, Oper. Res..

[19]  Kweku-Muata Osei-Bryson,et al.  Generating Belief Functions from Qualitative Preferences: An Approach to Eliciting Expert Judgments and Deriving Probability Functions , 1998, Data Knowl. Eng..

[20]  Raimo P. Hämäläinen,et al.  Preference Assessment by Imprecise Ratio Statements , 1992, Oper. Res..

[21]  Philippe Vincke,et al.  An outranking method under uncertainty , 1988 .

[22]  Klaus von Gadow,et al.  Risk Analysis in Forest Management , 2001, Managing Forest Ecosystems.

[23]  H. Todd Mowrer Estimating components of propagated variance in growth simulation model projections , 1991 .

[24]  M. Bevers,et al.  Pragmatic approaches to optimization with random yield coefficients , 1995 .

[25]  P. L. Marshall A procedure for constructing timber management strategies under uncertainty , 1988 .

[26]  Dietmar W. Rose,et al.  Heuristic simulation: an alternative to linear programming in developing forest management schedules. , 1990 .

[27]  Risto Lahdelma,et al.  SMAA-2: Stochastic Multicriteria Acceptability Analysis for Group Decision Making , 2001, Oper. Res..

[28]  James B. Pickens,et al.  A Strategy for Multiproduct Stand Management with Uncertain Future Prices , 1996, Forest Science.

[29]  John Sessions,et al.  Eight heuristic planning techniques applied to three increasingly difficult wildlife planning problems , 2002 .

[30]  Dietmar W. Rose,et al.  A model for recognizing forestwide risk in timber management scheduling. , 1987 .

[31]  Luis G. Vargas,et al.  Comparison of eigenvalue, logarithmic least squares and least squares methods in estimating ratios , 1984 .

[32]  S. G. Rabinovich Measurement Errors: Theory and Practice , 1994 .

[33]  Lawrence D. Teeter,et al.  A Multiattribute Utility Model for Incorporating Risk in Fire Management Planning , 1986, Forest Science.

[34]  James B. Pickens,et al.  Chance constraints and chance maximization with random yield coefficients in renewable resource optimization , 1992 .

[35]  Rakesh Kumar Sarin,et al.  Strength of Preference and Risky Choice , 1982, Oper. Res..

[36]  W. Edwards,et al.  Decision Analysis and Behavioral Research , 1986 .

[37]  B. Bare,et al.  Timber harvest scheduling in a fuzzy decision environment , 1992 .

[38]  George Z. Gertner,et al.  A quality assessment of a Weibull based growth projection system , 1995 .

[39]  Kaisa Miettinen,et al.  Decision-aid for discrete multiple criteria decision making problems with imprecise data , 1999, Eur. J. Oper. Res..

[40]  J. Neumann,et al.  Theory of Games and Economic Behavior. , 1945 .

[41]  Jean-Marc Martel,et al.  "Value" of additional information in multicriterion analysis under uncertainty , 1999, Eur. J. Oper. Res..

[42]  Ami Arbel,et al.  Approximate articulation of preference and priority derivation , 1989 .

[43]  J. Gove,et al.  Optimizing the management of uneven-aged forest stands : a stochastic approach , 1992 .

[44]  Juha Lappi Estimating the distribution of a variable measured with error: stand densities in a forest inventory , 1991 .

[45]  Annika Kangas,et al.  Uncertainty in growth and yield projections due to annual variation of diameter growth , 1998 .

[46]  Annika Kangas,et al.  Outranking methods as tools in strategic natural resources planning , 2001 .

[47]  Sylvia Richardson,et al.  Markov Chain Monte Carlo in Practice , 1997 .

[48]  Olli Varis,et al.  Bayesian decision analysis for environmental and resource management , 1997 .

[49]  Jayanath Ananda,et al.  The use of Analytic Hierarchy Process to incorporate stakeholder preferences into regional forest planning , 2003 .

[50]  H. Todd Mowrer,et al.  Uncertainty in natural resource decision support systems: sources, interpretation, and importance. , 2000 .

[51]  B. Roy THE OUTRANKING APPROACH AND THE FOUNDATIONS OF ELECTRE METHODS , 1991 .

[52]  Hiroyuki Itami Expected Objective Value of a Stochastic Linear Program and the Degree of Uncertainty of Parameters , 1974 .

[53]  Jyrki Kangas,et al.  Uncertainty in Expert Predictions of the Ecological Consequences of Forest Plans , 1996 .

[54]  Jyrki Kangas,et al.  A heuristic optimization method for forest planning and decision making , 1993 .

[55]  Hans-Jürgen Zimmermann,et al.  Decision Making in Fuzzy Environment , 1985 .

[56]  David E. Bell,et al.  Disappointment in Decision Making Under Uncertainty , 1985, Oper. Res..

[57]  R. L. Keeney,et al.  Decisions with Multiple Objectives: Preferences and Value Trade-Offs , 1977, IEEE Transactions on Systems, Man, and Cybernetics.

[58]  Jyrki Kangas,et al.  A Method for Integrating Risk and Attitude Toward Risk into Forest Planning , 1996, Forest Science.

[59]  S Senn Covariance analysis in generalized linear measurement error models. , 1990, Statistics in medicine.

[60]  M. Bevers,et al.  Chance-constrained optimization with spatially autocorrelated forest yields , 1996 .

[61]  Benjamin F. Hobbs,et al.  Is optimization optimistically biased , 1989 .

[62]  Jyrki Kangas,et al.  Analyzing uncertainties in experts' opinions of forest plan performance , 1997 .

[63]  James C. Fortson,et al.  Optimum plantation planting density and rotation age based on financial risk and return , 1991 .

[64]  Annika Kangas,et al.  Optimization bias in forest management planning solutions due to errors in forest variables , 1999 .

[65]  Graham Dunn,et al.  Design and Analysis of Reliability Studies: The Statistical Evaluation of Measurement Errors , 1989 .

[66]  George Z. Gertner,et al.  Approximating Precision in Simulation Projections: An Efficient Alternative to Monte Carlo Methods , 1987, Forest Science.

[67]  Annika Kangas,et al.  On the prediction bias and variance in long-term growth projections , 1997 .

[68]  Rudolf Kruse,et al.  Uncertainty and Vagueness in Knowledge Based Systems , 1991, Artificial Intelligence.

[69]  T. Postelnicu,et al.  Foundations of inference in survey sampling , 1977 .

[70]  C. L. Sheng Some quantitative concepts of value and utility from a utilitarian point of view , 1989 .

[71]  P. D. Jong A statistical approach to Saaty's scaling method for priorities , 1984 .

[72]  Guillermo A. Mendoza,et al.  A fuzzy multiple objective linear programming approach to forest planning under uncertainty , 1993 .

[73]  Mark J. Ducey,et al.  Representing uncertainty in silvicultural decisions : an application of the Dempster-Shafer theory of evidence , 2001 .

[74]  D. Boychuk,et al.  A Multistage Stochastic Programming Model for Sustainable Forest-Level Timber Supply Under Risk of Fire , 1996, Forest Science.

[75]  L. Valsta,et al.  A Scenario Approach to Stochastic Anticipatory Optimization in Stand Management , 1992, Forest Science.

[76]  Jyrki Kangas,et al.  Incorporating risk attitude into comparison of reforestation alternatives , 1994 .

[77]  Augustine Kong,et al.  [Probabilistic Expert Systems in Medicine: Practical Issues in Handling Uncertainty]: Comment , 1987 .

[78]  Krassimir T. Atanassov,et al.  Intuitionistic fuzzy sets , 1986 .

[79]  Biing T. Guan,et al.  Projection variance partitioning of a conceptual forest growth model with orthogonal polynomials , 1996 .

[80]  James B. Pickens,et al.  Use of chance-constrained programming to account for stochastic variation in theA-matrix of large-scale linear programs: A forestry application , 1991, Ann. Oper. Res..

[81]  Jyrki Kangas,et al.  Analysing uncertainties of interval judgment data in multiple-criteria evaluation of forest plans , 1998 .

[82]  Timo Pukkala,et al.  Multiple risks in multi-objective forest planning: integration and importance , 1998 .

[83]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[84]  Annika Kangas,et al.  Effect of errors-in-variables on coefficients of a growth model and on prediction of growth , 1998 .

[85]  Z. Pawlak Rough sets and fuzzy sets , 1985 .

[86]  Graham Dunn,et al.  Review papers : Design and analysis of reliability studies , 1992 .

[87]  M. Goumas,et al.  An extension of the PROMETHEE method for decision making in fuzzy environment: Ranking of alternative energy exploitation projects , 2000, Eur. J. Oper. Res..

[88]  William F. Caselton,et al.  Decision making with imprecise probabilities: Dempster‐Shafer Theory and application , 1992 .

[89]  Anne Lohrli Chapman and Hall , 1985 .

[90]  B. Matérn Spatial variation : Stochastic models and their application to some problems in forest surveys and other sampling investigations , 1960 .

[91]  Andrew P. Sage,et al.  Uncertainty in Artificial Intelligence , 1987, IEEE Transactions on Systems, Man, and Cybernetics.

[92]  A. Dempster Upper and lower probability inferences based on a sample from a finite univariate population. , 1967, Biometrika.

[93]  Claire A. Montgomery Risk and forest policy: Issues and recent trends in the U.S , 1996 .

[94]  J. B. Pickens,et al.  Use of stochastic production coefficients in linear programming models: objective function distribution, feasibility, and dual activities , 1988 .

[95]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[96]  Guillermo A. Mendoza,et al.  Forest planning and decision making under fuzzy environments: an overview and illustration , 1989 .

[97]  Hans-Jürgen Zimmermann,et al.  Fuzzy Set Theory - and Its Applications , 1985 .

[98]  Risto Lahdelma,et al.  SMAA - Stochastic multiobjective acceptability analysis , 1998, Eur. J. Oper. Res..

[99]  H. T. Mowrer,et al.  Variance propagation in growth and yield projections , 1986 .

[100]  S. Ferson,et al.  Different methods are needed to propagate ignorance and variability , 1996 .

[101]  F. O. Hoffman,et al.  Propagation of uncertainty in risk assessments: the need to distinguish between uncertainty due to lack of knowledge and uncertainty due to variability. , 1994, Risk analysis : an official publication of the Society for Risk Analysis.

[102]  Jyrki Kangas,et al.  Operationalization of biological diversity as a decision objective in tactical forest planning , 1996 .

[103]  T. Pukkala Multi-objective Forest Planning , 2002, Managing Forest Ecosystems.

[104]  George Z. Gertner,et al.  Effects of measurement errors on an individual tree-based growth projection system , 1984 .

[105]  William A. Duerr,et al.  Forest Resource Management , 1977 .

[106]  Didier Dubois,et al.  Decision-theoretic foundations of qualitative possibility theory , 2001, Eur. J. Oper. Res..

[107]  Dennis M. Buede,et al.  A target identification comparison of Bayesian and Dempster-Shafer multisensor fusion , 1997, IEEE Trans. Syst. Man Cybern. Part A.

[108]  Bruce C. Larson,et al.  A fuzzy set approach to the problem of sustainability , 1999 .

[109]  Richard Bellman,et al.  Decision-making in fuzzy environment , 2012 .

[110]  David Williams,et al.  Probability with Martingales , 1991, Cambridge mathematical textbooks.

[111]  Earl Cox,et al.  The fuzzy systems handbook , 1994 .

[112]  Bjørnar Tessem,et al.  Interval probability propagation , 1992, Int. J. Approx. Reason..

[113]  Jian-Bo Yang,et al.  Rule and utility based evidential reasoning approach for multiattribute decision analysis under uncertainties , 2001, Eur. J. Oper. Res..

[114]  Annika Kangas,et al.  Multiple Criteria Decision Support Methods in Forest Management , 2002 .

[115]  Ronald E. McRoberts,et al.  Variation in forest inventory field measurements , 1994 .

[116]  Indrani Basak,et al.  Probabilistic judgments specified partially in the analytic hierarchy process , 1998, Eur. J. Oper. Res..

[117]  G. Crawford,et al.  A note on the analysis of subjective judgment matrices , 1985 .

[118]  G. E. Campbell,et al.  Multiobjective programming for generating alternatives: a multiple-use planning example , 1987 .